Recently, the operational domain of unmanned aerial vehicles (UAVs) has expanded from outdoor environments to indoor spaces such as factories, power plants, and tunnels. In these environments, where the global navigation satellite system (GNSS) is unavailable and the risk of collision with surrounding structures is high, a positioning system with high accuracy and fast update rates is essential. In this paper, we propose a novel three-dimensional positioning algorithm for UAVs operating in a GNSS-denied environment. The proposed algorithm is based on sensor fusion of the inertial measurement unit (IMU) and ultra wideband-based wireless sensor network (UWB-WSN). The proposed algorithm, referred to as the recursive extended finite-memory positioning (REFMP), features a unique finite-memory (FM) structure that estimates the current position using only a limited set of recent information. This FM structure endows the algorithm with robustness against measurement model errors, linearization errors, and uncertainties in initial position information. We evaluated the performance of the proposed algorithm through both simulations and real-world experiments under various challenging conditions, including UAV collisions with walls, aggressive maneuvers inducing sensor measurement errors, and scenarios with uncertain initial position information. By comparing the proposed algorithm with state-of-the-art UAV positioning algorithms based on IMU and sensor fusion, we demonstrate the superior positioning accuracy and robustness of REFMP under harsh conditions.

This work introduces a fixed-time adaptive fault-tolerant control approach to tackle the trajectory tracking issues in wheeled mobile robots by accounting for unknown dead zones and actuator faults. Firstly, by introducing smoothing functions, bounded estimates, and adaptive parameters, combined with a fixed-time control strategy, actuator faults and the impacts of unknown dead zones are effectively eliminated. Second, an adaptive fault-tolerant control strategy for nonlinear wheeled mobile robot systems is developed. Subsequently, a fixed-time trajectory tracking controller is designed to guarantee system stability and achieve high-precision control for wheeled mobile robots. Furthermore, leveraging the principles of Lyapunov stability theory, this work rigorously establishes the convergence properties of the controller, offering a robust theoretical foundation for ensuring the precision and reliability of trajectory tracking. Finally, the proposed control strategy’s effectiveness and feasibility are validated through simulation outcomes.

This paper introduces a Physics-Informed Koopman Neural Operator (PI-KNO) for augmented dynamics visual servoing of multirotors that integrates Koopman operator theory with neural networks. The proposed method establishes a structured learning framework that effectively captures complex system dynamics while embedding physics-based priors. Unlike fully data-driven approaches, PI-KNO improves generalization and minimizes reliance on extensive real-world training data by employing a hybrid loss function that combines physics-informed constraints with real-time observations. The learned model is incorporated into a monotonically weighted nonlinear model predictive control (NMPC) framework, ensuring precise trajectory tracking while adhering to state and input constraints. Experimental results demonstrate that PI-KNO reduces training time by 16.81% and enhances tracking accuracy by 19.56% compared to conventional Data-Driven Koopman Neural Operators (DD-KNO) and Physics-Informed Neural Networks (PINN). Additionally, under 70.83% uncertainty in camera parameters, PI-KNO achieves 10.6% and 22.2% lower tracking errors than PINN and DD-KNO, respectively. These findings underscore the robustness and efficiency of the proposed approach for real-time multirotor visual servoing applications. Note to Practitioners - This work presents an implementation oriented view of a learning based predictive controller for aerial robots designed to operate within the limits of onboard computation and sensing. The proposed PI-KNO approach combines physical modeling and data driven learning to create an accurate and stable dynamic predictor suitable for real time deployment. The architecture is distributed between an NVIDIA Jetson and a Pixhawk. The Jetson runs the vision front end, state estimator, PI-KNO predictor, and NMPC optimizer. The estimator fuses visual and inertial data to provide position, velocity, and attitude states, while the NMPC computes high level motion commands at about 30Hz using the PI-KNO rollout. The Pixhawk executes inner rate loops at 250Hz through the native autopilot for attitude stabilization and motor mixing. Communication between the Jetson and Pixhawk uses standard MAVLink setpoints, requiring no firmware modification. The training workflow begins with a nominal visual servoing multirotor model and limited flight data, from which the operator is trained offline using a physics-informed regularizer. At runtime, the estimator updates the state, PI-KNO predicts short horizon dynamics, the NMPC optimizes control sequences, and the Pixhawk executes the commands. This setup achieves reliable tracking across trajectories, maintains physical consistency under uncertainty, and fits within the computational limits of embedded hardware.

The task performance of mobile manipulators can be significantly enhanced by whole-body control and optimization in complex scenarios. Due to the nonlinear properties of whole-body dynamics and parameter uncertainty, modeling accurate system dynamics is essential in addition to designing an effective control strategy. However, traditional control methods have high computational costs and fail to deal with the parameter errors caused by model linearization. To address these issues, we propose a model predictive control (MPC)-Net, a learning-based approach that facilitates rapid online optimization by combining deep learning with multiple MPCs. Firstly, we develop a parameter identification algorithm based on a deep neural model to estimate the unknown dynamics parameters. Although the control performance of the MPC approach positively correlated with the prediction horizon, a long horizon would result in additional computational costs. Thus, MPC-Net is constructed by combining multiple sub-MPC issues, and the nonlinear coefficients are obtained by using a deep neural network-based optimization framework. Furthermore, MPC-Net generates the solution by combining the outputs of multiple sub-MPC problems using the nonlinear transformation of learned coefficients. Experiments are conducted on a mobile manipulator, which demonstrates the proposed MPC-Net-based optimization control offers fast efficient computation and low tracking error performance.

In practical applications, input saturation is an unavoidable phenomenon that can degrade system performance and even lead to instability. This work, therefore, addresses the practical finite-time formation control problem of two-wheeled mobile robots under input saturation, a consequence of the physical limitations of their drive motors. To mitigate the adverse effects of input saturation while achieving finite-time convergence, a saturated controller is developed to realize leader-follower formation control. Furthermore, event-triggered strategies—both with and without continuous monitoring—are incorporated into the proposed controller to accommodate the limited computational resources of two-wheeled mobile robots. The Zeno phenomenon is explicitly excluded by ensuring that all inter-event intervals are bounded below by a strictly positive constant. The experimental results demonstrate the effectiveness of the developed controller in achieving finite-time formation for two-wheeled mobile robots in real-world applications.

This paper investigates the robust control problem of Markov jump linear systems (MJLSs) with unknown transition probabilities (TPs). While existing temporal difference learning (TDL) methods eliminate the requirement for the precise value of TPs, they often overlook the rapidity of the method convergence. Therefore, we propose an online transfer-enabled temporal difference learning (TTDL) method that explores prior knowledge from similar yet different systems to accelerate convergence and improve the estimation accuracy of decision matrices. Specifically, a transfer estimator is constructed by combining control parameters from the source domain with mode trajectories from the target domain to approximate the target decision matrices. At the beginning of each learning episode, this estimator is incorporated into the value function through an adaptive transfer mechanism. The mechanism uses source knowledge only when reliable and suppresses it near convergence, effectively avoiding negative transfer and yielding rapid policy updates. The theoretical analysis provides a rigorous proof of the convergence for the value function in the online TTDL method. Comparative experiments validate its effectiveness and highlight its reliability under data-scarce scenarios. An application on an aero-engine system further validates the practical applicability and efficiency of the proposed method.

Developing non-visual sensing and intelligent recognition technologies is crucial for enhancing the manipulation performance of robots in dim or obstructed environments. Although precise object recognition has been extensively researched for rigid manipulators, the adoption of these techniques in soft robotic systems has been limited by the high modulus or low sensitivity of existing sensors (gauge factor/Young’s modulus$<$10 kPa$^{-1}$). Meanwhile, these systems necessitate advanced artificial intelligence(AI) algorithms to effectively process multiple sensing data. In this study, we utilize newly developed soft sensors and AI algorithm to establish a biomimetic perceptual soft gripper system capable of sensing and generating category object information during the grasping task. A strain/pressure bimodal sensor, mimicking the exceptional softness (Young’s modulus$<$10 kPa) and high sensitivity (gauge factor$>$2000) of human skin, has been developed and seamlessly integrated into a three-finger soft robotic gripper. A Swin Transformer network was developed to learn rules from bimodal data acquired from sensors and generate category information of the grasping objects. The perceptual gripper system exhibited superior recognition accuracy compared to previously reported systems, achieving an impressive 94.9% accuracy in categorizing 18 objects of varying shapes and sizes. We believe this advancement unlocks soft machines’ potential for automated applications.Note to Practitioners—Non-visual sensing and recognition techniques for soft robotic grippers are valuable for operation in dimly lit or obstructed environments. Traditional rigid sensor have limitations when applied to soft robotic systems due to their high modulus. This research developed novel ultra-soft and high sensitive strain/pressure bimodal sensors integrated into a three-fingered soft robotic gripper. Meanwhile, an AI algorithm applicable to the soft gripper system was developed to process the sensing data and generate the corresponding object category information when grasping the object. With the aid of the developed soft sensor and AI algorithm, the perceptual soft gripper can achieve high-precision object recognition, giving it great potential for automating tasks such as item picking, assembly, and handling in energy-efficient warehouses and logistics centers. The developed soft sensor and AI algorithm co-pilot object recognition strategy can contribute to the advancement of soft machine automation capability technology.

This article investigates the leader-following bipartite time-varying formation (BTVF) control problem for switched multiagent systems (MASs) under the changeable directed signed topologies. The communication topology switches to obey an average dwell-time condition, capturing realistic network dynamics. Two critical challenges are addressed in the controller design, one of which is that the real states are inaccessible and the other is that we know nothing about the active leader input. These constraints significantly increase the design complexity. Against this backdrop, we develop a novel control protocol that is capable of achieving the BTVF tracking even given the uncertain leader input without using the real states. The designed control protocol can perform well to deliver reliable commands to realize the BTVF under switched topologies. Through Lyapunov stability analysis and recursive algorithms, we rigorously prove convergence to the desired BTVF. Notably, the protocol is also shown to guarantee bipartite consensus as a special case. In the end, the effectiveness of the proposed control scheme is validated through simulations involving the clusters of the wheeled mobile robot and autonomous aerial vehicle models in different working scenarios.

Formation control of differential mobile robots (DMRs) has attracted considerable interest owing to its broad potential applications in industrial transportation, service robotics, and autonomous cooperative tasks. Accordingly, this work investigates the leader–follower finite-time formation control for DMRs under a directed communication graph. To address the kinematic underactuation problem, the heading angle is excluded from the control design, and a coordinate transformation is introduced to derive a generalized system representation for DMRs. A chattering-free fixed-time observer is developed to estimate the input to the leader, and a nonlinear distributed protocol is proposed to achieve practical finite-time formation control. Finally, two experiments are conducted under three scenarios to validate the effectiveness of the proposed protocol for real-world applications.

This article proposes a sampled-data event-triggered adaptive neural network (NN) control strategy to cope with the digital communication and attack compensation problems of networked systems with actuator attacks and exogenous disturbance. By combining event triggering state, parameter estimation signals, and disturbance observer, a novel digital state feedback controller is designed to reduce its updating frequency and compensate for the deliberate impact of unknown actuator attacks. Moreover, considering that the state is partially measurable, a novel observer-based digital controller is designed via a double-ended event-triggering mechanism (ETM). Then, two new Lyapunov functionals are created to analyze the system stability, and two design methods are given to solve the control gain. Finally, the feasibility and validity of the derived results are verified by a visual servo control system and an offshore structure system.

This paper presents a universal and adaptive control framework for reconfigurable vehicles subject to composite motion disturbances, incorporating a collaborative fault-tolerant mechanism. A model-based cascaded control architecture is developed based on the vehicle’s kinematic and dynamic models. To ensure safety, an improved adaptive geofencing strategy is proposed which integrates capability constraints with barrier functions in the kinematic loop. For dynamic feedback, an adaptive gain-filtered extended state observer enables accurate disturbance estimation from noisy outputs and improves robustness via feedforward compensation. Meanwhile, a collaborative fault-tolerant mechanism further allocates control inputs to mitigate actuator faults. Finally, experimental results validate the proposed method’s effectiveness under complex interference scenarios.

In this paper, a finite-time robust safe cooperative control strategy is proposed for heterogeneous multi-agent systems (HMAS) in cluttered obstacle environments under input saturation, external disturbances, and Denial-of-Service (DoS) attacks. An adaptive event-triggered observer is designed at the cyber layer to achieve distributed resilient tracking under DoS-induced communication networks. At the physical layer, a distributed control scheme based on finite-time robust control barrier function (FT-RCBF) is first developed to ensure fast obstacle avoidance for HMAS. The proposed method is applied to a cooperative forest traversal and monitoring task for ground-air autonomous systems, and its effectiveness and robustness are verified through simulations.

This paper investigates the distributed bipartite tracking problem for heterogeneous multi-agent systems (MASs) on signed directed networks. Leaders with both cooperative and competitive interactions and two different types of followers form the heterogeneous MASs. The dynamics of each agent are described in strict feedback form by parameters of different structures. The so-called reference signal tracking approach is employed in the hierarchical design to generate reference signal generators and bipartite tracking controllers. The control gains of leaders and followers are integrated into the design of reference signal generators, which utilize locally estimated states and remain independent of the communication topology. The convergence of the proposed heterogeneous MASs is proved. The simulation result demonstrates the effectiveness of the proposed hierarchical design.

A new resilient consensus control scheme based on the attack detection and isolation idea is proposed for nonlinear multiagent systems (MASs) experiencing false data injection attack (FDI) on communication links between the follower agents. At first, an improved dynamic surface control (DSC)-based consensus scheme is proposed for nonlinear MAS operating in safe conditions and its stability is analyzed. A local distributed diagnostic module based on the high-gain observer idea is proposed for each agent to monitor status of each agent and generate residual signal. Then, by evaluating residual signal, destination agent that receives false data is detected. After that, a bank of observers is activated for isolating under attack link from others ended to the detected agent by the attack detection unit. After identifying under attack link, validation index corresponding to the status of the identified under attack link is updated. Finally, by incorporating updated validation matrix into the control algorithm, the proposed resilient consensus algorithm is introduced. Simulation results on a group of single-link robot manipulators verify effective performance of the proposed scheme.

Cyber-physical system (CPS) security has become a paramount concern as the integration of digital and physical systems continues to grow. This survey article provides an in-depth analysis of cyber-attacks targeting CPSs, along with existing methodologies for attack modeling, defense, and design. It begins by categorizing the major types of cyber-attacks affecting CPSs, followed by a detailed discussion of the mathematical models representing typical cyber-attacks within cyber-physical space. This article then examines defense mechanisms, covering both detection and response strategies, and emphasizes techniques for designing stealthy attacks. Finally, it identifies promising directions for future research, aiming to provide researchers with a broad perspective that supports the development of robust, real-time security solutions for the increasingly complex CPS landscape.

Accurate detection of leaks in underground water pipelines poses significant challenges due to the complexity of acoustic signals and environmental noise. The deep burial and widespread distribution of water supply pipelines in urban areas make current manual detection methods inefficient and time-consuming. This paper introduces the deep semi-supervised anomaly detection multiscale (DSMS) algorithm, which combines a multi-scale convolutional network with the deep semi-supervised anomaly detection (deep SAD) framework to address these challenges. The multi-scale convolutional network is specifically designed to extract both fine-grained and global acoustic features from Mel frequency cepstral coefficients (MFCCs), enabling effective differentiation between leakage sounds and background noise. Additionally, a pre-training module based on an autoencoder initializes the network weights, improving convergence and increasing the evaluation metric by 27%. The DSMS algorithm achieves state-of-the-art performance, with a test accuracy of 99.14% and a false positive rate of 0.89%. This approach offers a promising solution for efficient and precise leak detection in urban water distribution systems.

This paper investigates the distributed secure fusion estimation problem under stochastic nonlinear hybrid attacks. Specifically, this work analyzes a hybrid attack scenario where the attacker employs a randomized approach to launch false data injection (FDI) attacks and Denial-of-Service (DoS) attacks on the measurement information communication channel. Then, an innovative distributed secure fusion estimation model is proposed, addressing three situations: DoS attacks, FDI attacks, and the absence of attacks. Following this, an existence condition is derived for the secure fusion estimator, utilizing probability-guaranteed set-membership filtering technology, to ensure that the fusion estimation error will consistently be bounded within an expected ellipsoid with the specified probability. Subsequently, a convex optimization problem involving constrained recursive matrix inequalities is formulated to compute the secure fusion estimation weight matrices. Finally, the effectiveness of the proposed probability-guaranteed set-membership secure fusion estimation (SSFE) algorithm is demonstrated through a simulation example. Note to Practitioners—The research in this paper is dedicated to addressing the problem of fusion state estimation in practical engineering tasks such as intelligent transportation, industrial manufacturing and military defense. With the increase in application requirements and process accuracy, the majority of projects demand that the true state must be bounded within a certain range, e.g., unmanned vehicle obstacle avoidance and missile precision strikes. To overcome this challenge, probability-guaranteed set-membership filtering is introduced to ensure that the fusion estimation error is bounded with a certain probability. However, due to the expanding scope of engineering applications, the system may be deployed to perform tasks in a non-secure environment, which increases the risk of malicious attacks that may lead to functional failures as well as performance degradation. Therefore, this paper simultaneously considers the scenario where the communication channels are subjected to the stochastic hybrid attacks, which can be effectively handled by utilizing the proposed probability-guaranteed SSFE algorithm. Preliminary simulations demonstrate the feasibility of the algorithm. Since the actual system may also encounter problems such as sensor energy constraints, bandwidth resource limitations, and data processing asynchrony. Therefore, in our future work, we will focus on addressing the various limitations present in the fusion estimation system and improving their resolution.

Quadrotor systems offer significant potential, yet precise trajectory tracking remains challenging due to their complex dynamics and nonlinear characteristics. In particular, the unmodeled dynamics of the translational subsystem and the nonlinearity of the rotational subsystem present substantial obstacles. This study proposes a dual-loop structure integrating neural networks (NN) and fuzzy control techniques. By combining the sliding-mode control (SMC) method with adaptive laws, the approach achieves a fast tracking response, improved transient performance, and reduced chattering. An adaptive neural control based on a switching surface addresses the unmodeled dynamics of the outer loop and performs position control. Meanwhile, a dissipative fuzzy sliding-mode controller for the inner loop manages nonlinearity and input uncertainty, ensuring robust attitude control. The designed controllers’ stability was verified using Lyapunov theory, and the simulation of various trajectories demonstrated the robustness and potential of the proposed dual-loop neuro-fuzzy tracking controller.

This paper proposes a hierarchical task-oriented whole-body locomotion framework for the exoskeleton walking-cart pushing (EWCP) task, which includes a straight gait and a bypassing gait, allowing the exoskeleton robot to avoid obstacles during walking. In this framework, the core components are gait planning and phase estimation for locomotion in unstructured environments. Notably, our mobile redundancy exoskeleton system can provide more flexibility and versatility in manipulation when performing complex tasks. For the hierarchical task-oriented whole-body locomotion, the detour gait consists of straight lines and turning shapes so that the EWCP system can avoid obstacles on the ground. For gait planning, we use the dynamic motion primitives to learn the joint motion trajectory of whole-body locomotion, which has good generalization ability and adaptability with respect to the gait. For phase estimation, the current gait phase can be estimated from the joint angles. Additionally, we design an task switching mechanism, where the exoskeleton system can switch different configurations flexibly for different scenarios, such as track switching only with both feet supported. And the phase estimation and gait switching strategy ensure the stability of task switching. The experimental results show that the exoskeleton can effectively accomplish EWCP tasks in an environment with obstacles. Our work has also shown that even some challenging motion tasks can be implemented with relatively simple controllers, which greatly simplifies the design of control systems.Note to Practitioners—This paper is motivated by issues of hierarchical tasks of the lower limb exoskeleton. Traditional exoskeletons cannot achieve obstacle avoidance in complex scenes because they do not have enough degrees of freedom or they do not use hierarchical locomotion. In this paper, a hierarchical task-oriented whole-body locomotion is proposed, which includes a straight gait and a bypassing gait, allowing the exoskeleton robot to avoid obstacles during walking. For gait planning, we use DMP to generate the gait trajectory, which can ensure the smoothness of trajectory. For phase estimation, the current gait phase can be estimated from the joint angles. Additionally, we design an task switching mechanism, where the exoskeleton system can switch different configurations flexibly for different scenarios, such as track switching only with both feet supported. And the phase estimation and gait switching strategy ensure the stability of task switching. The proposed hierarchical task-oriented whole-body locomotion framework is expected to be applied to exoskeletons to assist patients in rehabilitation training and users in mobility in daily life. Additionally, the proposed framework uses a series of motion primitives to learn and reproduce the trajectory and update it online, which requires a heavy computation load calculation to ensure the update speed of the trajectory. Therefore, we expect to reduce computation complexity for a walking exoskeleton in the future.

In this paper, an upper limb prosthesis has been furnished with a novel vision-based manipulation and grasping strategy. The proposed whole-body safety-critical control design includes vision servoing, multiple tasks planning with strict priorities, which can be formulated as an hierarchical multi-task optimization (HMO) problem with safety conditions—expressed as control barrier functions (CBF). Firstly, a modified YOLOv7 algorithm with key points detection is developed to determine the grasping pattern of the object and extract its edge contour information using a depth camera. An HMO-based strategy with a notion of CBF, providing inequality constraints in the control input, is proposed to handle multiple prioritized tasks with various constraints to offer guarantees of safety with the whole-body motion in consideration. Then the HMO problem is solved by a neuro-dynamics optimization solution online. Finally, experiments are implemented by using a self-developed upper limb prosthesis. Experimental results validate the performance of the proposed whole-body control strategy. Note to Practitioners—The intuitive, convenient and autonomous control of prosthetics has always been the object of researchers’ efforts. This paper proposes a whole-body safety-critical control design, including artificial perception system, autonomous control system under visual guidance, and user volition control system. The HMO strategy with safety conditions-expressed as control barrier functions in the context of real-time optimization-based method can fully consider the task requirements of different priorities and realize the orderly execution of prosthetic tasks. The vision feedback can detect surrounding environments in real time to obtain fine position information and optimal grasping pattern, which are conducive to reduce the amputees’ cognitive burden and provide new ideas for the whole body control of prostheses.

Lately, voltage regulations for distributed generation (DG) in direct current (DC) microgrid (MG) have been extensively investigated based on various communication methods. However, the communication is not always reliable or even nonexistent in actual remote area. In this paper, a distributed coordinated voltage control strategy based on power/information hybrid modulation without additional communication lines is proposed. First, the whole state-space model in DC MG under double carriers differential phase shift keying (DC-DPSK) modulation method is constructed. Then, distributed dynamic output feedback control strategy is designed according to established model. The designed distributed control can address leader-following consensus issue in linear heterogeneous time delay multi-agent system which is equal to solve output voltage consensus issue in DC MG. Finally, the effectiveness and potential of the proposed design strategy is verified by simulation results.Note to Practitioners—Output voltage is the vital index for evaluating the quality of power system. Although various accomplishments have been achieved in the area of voltage regulation, the study of voltage regulation control without extra communication lines is rarely considered in DC MG. To fill in the gap, the paper concentrates on designing output feedback consensus control method under DC-DPSK modulation communication to ensure the stability and output voltage consensus in power system. It is worth noting that proposed control strategy based on multi-agent algorithm can be implemented distributed performance without additional communication lines, which is practical in real application. The results aim to provide a helpful reference for secondary control design in the remote area power system, such that the corresponding application research can be promoted.

Visually impaired individuals face limited mobility and restricted independent navigation in complex environments. Improving mobility and independent navigation for people with visual impairments is essential. In this paper, we introduce wearable electronic glasses (E-Glasses) that utilize a target detection network to fuse visual and auditory information for searching desired targets. Integrated with the electronic glasses, we present a neural path planning that combines spiking neural and convolutional neural networks. Several participants took part in experiments that showcased the remarkable capabilities of the developed system in target detection and navigation. The experimental results revealed an impressive success rate of 95.46% for the target detection network, providing participants with more accurate target information. Additionally, the neural path planning network achieved a success rate of 92.60%, demonstrating a significant speed advantage compared to the enhanced$A^{\ast}$algorithm.Note to Practitioners—This article aims to address the mobility and navigation challenges faced by visually impaired individuals in complex environments. Many solutions have been proposed to assist with wearable navigation by utilizing sensors to perceive the environment and provide path information to users. In this article, we introduce wearable electronic glasses (E-Glasses) with advanced target detection and path planning capabilities to help visually impaired individuals navigate more effectively in complex environments. We have developed a target detection network that integrates visual and auditory information to effectively search for desired targets. Additionally, we have developed a neural path planning algorithm that combines spiking neural networks and convolutional neural networks. Furthermore, experiments conducted in indoor navigation challenges demonstrate the feasibility of this approach. In future research, we will focus on further exploring the target detection network and refining the neural path planning algorithms to improve the overall performance of the system.

This paper focuses on the formation-containment (FC) control for multi-agent systems (MASs) with long input delay under jointly connected switching topologies. The considered MASs contain one virtual leader, multiple leaders and followers. The leaders complete the formation first, then the followers move into the formation created by the leaders. Compared with existing FC control, each agent in the considered MASs can have different dynamics. To handle long input delay, based on the partial differential equation (PDE) backstepping technique, a new distributed predictor feedback control method is proposed. Utilizing this method and the certainty equivalence principle, the MASs with long input delay can be mapped into new coupled ODE-PDE target systems. Then, Lyapunov-Krasovskii functions are used to prove the stability of the system. Simulations verify the validity of the proposed results.Note to Practitioners—In actual applications, multiple agents need to perform complex tasks together. These agents can be divided into leaders and followers. The leaders need to establish a time-varying formation (TVF) around a virtual leader to adapt to the changing circumstances, then the followers can move into the multiple leaders’ spanning convex hull so they can be protected from outside obstacles. In practical, input delays frequently occur in MASs due to network transmission, which brings difficulties to the FC control. Although there are plenty of works on FC control, research on the FC control of MASs with long input delay is still lacking.

In this paper, a novel approach for detecting anomalies in the non-linear fully-submerged hydrofoil attitude control system (HACS) is proposed, even in the presence of time-varying disturbances. To address the trade-off between robustness against disturbances and optimality in terms of estimation error, the extended sliding innovation filter (SIF) is employed as a state estimator for the target non-linear HACS. By utilizing a switching gain with a sliding boundary layer, the SIF inherently possesses a degree of robustness to estimation issues that may involve fault conditions or factors of disturbances. A residual framework is subsequently established to achieve state tracking and comparison. The residual evaluation for the fault detection (FD) scheme is then easily conducted using statistical methods such as the modified Z-Score and the peak signal-to-noise ratio (PSNR). Finally, the effectiveness of the developed FD strategy is substantiated through experiments conducted on a hardware-in-loop (HIL) platform. Comparative analysis with state-of-the-art robust UKF algorithms reveals the impressive fault detection proficiency of the proposed strategy. Note to Practitioners—This paper was motivated by the challenges of state estimation and FD for hydrofoil crafts under stochastic ocean wave disturbances. The extended SIF-based approach enhances robustness in the estimation and FD against disturbances by introducing a dynamic layer. Moreover, the adaptive layer indicates system anomalies as significant changes, which can assist engineers in promptly identifying anomalies. Furthermore, the modified statistics used in the FD scheme effectively reduce interference in the results. The developed FD method is easy to implement without linearizing the non-linear target system. The experimental validation conducted on the dSPACE platform utilizing the PCH-1 model illustrates the practicality of the proposed strategy for pertinent practitioners.

This paper presents a set-membership control approach for formation tracking in heterogeneous leader-following multi-agent systems. In order to improve the adaptability of the control strategy to changes in topology, a virtual neighbor is constructed for each follower, which represents the combined influence of all its neighbors. With the help of virtual neighbors, the interaction relationships between agents can be hidden, thereby mitigating the impact of topology changes on controller design. Then, a novel time-varying topology with adaptive adjacent weights is proposed, such that the coupling strength between agents can be actively adjusted to improve the formation performance. Subsequently, the set-membership technique is utilized to establish the optimization problems to ensure that the one-step forward tracking errors can be constrained within the ellipsoid sets. Further, an online algorithm is developed to continuously obtain the optimal controllers by solving the optimization problems recursively. Finally, the effectiveness and practicality of the proposed approach are demonstrated via a numerical simulation and its application on a robotic platform with UAVs and UGVs.

This paper is interested in the event-triggered leader-following consensus problem for discrete-time multi-agent systems subject to nonlinear dynamic topology. Considering that the on-board resources of each agent are generally limited, a novel adaptive event-triggered strategy is presented in this paper through establishing a nonlinear transformation law of consensus errors, which provides an effective way to weaken the impact of small fluctuations on triggering behaviors after the error systems converge. Then, an interval type-2 fuzzy model is introduced to describe the nonlinear dynamic topology where the dynamic characteristics contain both nonlinear time-varying law and uncertain parameter. In view of the discrepancies in adjacency relationships between different agents, the heterogeneous fuzzy-dependent controllers are employed to further decrease the consensus error. Meanwhile, some sufficient conditions are deduced to solve the designed controllers while ensuring that the consensus of multi-agent systems can be achieved with desired$H_{\infty }$performance. Ultimately, advantages of the presented leader-following control strategy are illustrated by two examples.Note to Practitioners—Many practical control scenarios, such as formation of unmanned air vehicles, can be described by a leader-following consensus problem of multi-agent systems with limited onboard resources. To save the resource consumption of agent, a novel event-triggered scheme is provided, which has more potential to reduce the computational tasks to a greater extent. In addition, aiming to promote the convergence of tracking error, the agent will tend to strengthen connections with the one that have not yet reached consensus, and weaken connections with the others. Therefore, a nonlinear dynamic topology with time-varying weight is introduced during the design of heterogeneous controller, such that the adjacency relationship between agents can be adjusted online. The effectiveness of the obtained results is demonstrated by an actual experiment. It is expected that the presented strategy can be further extended to multi-agent systems where network structures are more complex, such as the one with multiple groups of agents.

In this paper, the reliable tracking control and collision avoidance problems for cyber-physical systems (CPSs) with false data injection (FDI) attacks are investigated. FDI attacks can significantly compromise the safety and performance of CPSs by corrupting control and navigation data. Safety is an important aspect of CPSs. Unmanned ground vehicles and aerial vehicles are important applications of CPSs. The dual challenge of maintaining system safety and stability under deliberate cyberattacks, while ensuring reliable obstacle avoidance in dynamic environments, remains unresolved in many current methodologies. These challenges are amplified in CPSs owing to their reliance on real-time data and their susceptibility to adversarial manipulation. The main objective of this study is to develop a resilient tracking control strategy that can effectively mitigate the impact of FDI attacks and achieve obstacle avoidance. We propose a novel framework based on the exponential control barrier function (ECBF) and a novel observer-based auxiliary signal approach that can ensure the resilience of CPSs against FDI attacks and obstacle avoidance. The effectiveness of our methods is shown through extensive simulations and physical experiments, which depict improved tracking accuracy and system stability in the presence of FDI attacks as compared with those of traditional control methods.Note to Practitioners—This research tackles the problem of maintaining dependable tracking control and avoiding collisions in CPSs vulnerable to FDI attacks, with applications in fields such as smart transportation and smart city. To mitigate the impact of FDI attacks, we propose a resilient control strategy that uses exponential ECBF and an observer-based auxiliary signal approach. This framework mitigates the impact of corrupted data, maintains system stability, and avoids collisions, even in the presence of cyberattacks. Practitioners in CPS design and deployment can benefit from this approach by integrating it into existing systems to improve safety and security. The proposed method is validated through both simulations and physical experiments, demonstrating its practicality for real-world applications.

This paper tackles the security cooperative tracking control problem for uncertain heterogeneous nonlinear multi-agent systems (MASs) subject to denial-of-service (DoS) attacks. Since the communication between agents can only transmit digital signals, a periodic sampling mechanism is used to reduce bandwidth pressure. Based on these digital signals, a novel distributed switching observer is designed to estimate the state of the reference system for each follower in the case of DoS attacks on aperiodic intermittently blocked communication channels. Then, an attack-parameter-dependent Lyapunov functional is established to analyze the exponential convergence of the error signals and a design scheme of observer gains is proposed. Further, with the help of the sampling observer outputs, follower’s states and adaptive signals, a sampled-data neural network controller is designed to guarantee the boundedness of the tracking errors by backstepping method and impulsive system analysis theory. Finally, a simulation example is performed to showcase the effectiveness of the developed strategy. Note to Practitioners—In industrial systems, since it is difficult for a single plant to complete complex control tasks, the cooperative control of MASs has gradually become a hot research issue. For example, in industrial automated production, a single robotic arm cannot achieve mass production of products, while multiple robotic arms can achieve this task and improve production efficiency. In this context, the study on cooperative control of MASs is of great scientific and economic value. The wireless communication network is the medium of information transfer between the agents and the controllers, while giving attacker an opportunity to attack. When network interaction information is affected by attacks, the performance and stability of the system are easily damaged. Moreover, under the condition of ensuring the performance of the system, the number of packet transmission is as small as possible, which will be conducive to the applications with limited bandwidth or congested network. Taking these problems into consideration, this paper studies sampled-data tracking control for uncertain heterogeneous nonlinear MASs under DoS attacks. This result provides a reference for the design of digital security controller, and can also be applied to intelligent engineering systems.

This study aims to explore the stabilization problem of state-based fuzzy switched dynamic systems with Lévy noise, non-differentiable delay and actuator saturation through a novel intermittent output feedback control mechanism. Firstly, the delay studied in this study is non-differentiable, eliminating the harsh condition that the delay in the existing conventional system must be differentiable or even require the derivative to be less than 1, and replacing the existing common Brownian motion with Lévy noise that is more realistic to simulate the inevitable stochastic noise in the system, which has more theoretical research significance and exploration value. Subsequently, by means of dynamic output feedback mechanism, a new intermittent output feedback control scheme is designed to replace the conventional intermittent state feedback control scheme, which has important practicability and flexibility in practical control. Especially, a novel stabilization criterion is developed by selecting a novel switching-Lyapunov function in combination with two novel lemma pairs for solving non-differentiable terms and actuator saturation. Furthermore, two additional algorithms are given to maximize the attraction domain of the closed-loop system and to minimize the control gain matrix in order to minimize the control cost. Finally, a numerical example is used to verify the validity of the developed results, and a meaningful additional practical electronic circuit model is provided to prove its practicability and superiority.

This study examines the output tracking control of discrete-time networked semi-Markov jump systems (SMJSs) under cyber-attacks in the framework of finite-time control methodology. Different from most networked systems that employ single-channel communication, this work considers the case of multi-channel communication in the controller-to-actuator networks. Aimed at better reflecting the practical situation, a type of hybrid attacks is taken into consideration, which is a mixture of denial-of-service attacks and false data injection attacks. Subsequently, the dynamic characteristics of hybrid attacks among multiple channels are modeled by two stochastic processes. The goal is to design a state feedback controller such that the resulting closed-loop system is not only finite-time boundedness with dissipative performance but also has robustness against hybrid attacks. Finally, the effectiveness of the proposed novel controller design method is verified by an illustrative example.

This study is devoted to the observer-based sliding mode control (SMC) for discrete nonlinear semi-Markov jump models with incomplete sojourn information and application to the quarter-car suspension model. The nonlinear plant with parameter uncertainty is represented by an interval type-2 fuzzy model, where the membership functions of the fuzzy rules are related to the system mode. Since sojourn information is challenging to obtain in practice, the sojourn time probability density function is considered to be incompletely available. The considered system is more general, not only relaxing the traditional assumption that all the sojourn time probability density functions are completely available, but also covering completely available sojourn time probability density functions as a special case. The main innovation is that an observer-based SMC scheme is designed, which makes the discrete nonlinear models have better dynamic performance, and realizes the reachability of discrete quasi-sliding mode. By the interval type-2 fuzzy and classical Lyapunov function, the mean-square stability criterion of the semi-Markov jump models is constructed employing additional matrix variables. Then, an observer-based SMC mechanism is constructed to achieve the reachability of the quasi-sliding mode. Ultimately, the proposed method is validated by a two-degree-freedom quarter-car suspension model.

Fault detection and estimation issues of China railway high-speed (CRH) train suspension systems in early stage are addressed in this article based on data-driven design of total measurable fault information residual (ToMFIR). First, a discrete trailer car model of the CRH train is established. Based on this model, input/output (I/O) data matrices and system data models are constructed step by step using ToMFIR theory through sensor measurements. By utilizing the projection on controller residual, the data-driven form of ToMFIR residual can be further obtained. For the purpose of efficient and accurate incipient fault detection, the Kullback–Leibler divergence (KLD), an indirect method, is employed to evaluate and monitor the slight changes in the ToMFIR residual in matrix form. Finally, a fault amplitude estimation method based on KLD for detecting incipient sensor effectiveness loss is introduced. Simulation results demonstrate that the data-driven detection and estimation scheme proposed offers higher sensitivity to spring faults, damper faults, actuator faults, and sensor faults of CRH train suspension systems in early stage.

This article presents a comprehensive analysis of the limitations associated with single ES in regulating CL voltage, specifically highlighting their restricted adjustment range and suboptimal performance in high-power applications. To address these challenges, a novel parallel configuration of ESs is proposed for the first time, with an in-depth examination of its design principles and critical technical issues. To overcome the energy supply limitations inherent in traditional ES systems, the study integrates PV systems and BESS, replacing the conventional assumption of an ideal dc power source. This integration establishes a PV/BESS model that not only enhances the utilization efficiency of renewable energy but also ensures effective stabilization of the dc bus voltage. Under diverse disturbances, including solar irradiance fluctuations and microgrid voltage variations, the proposed parallel PV/BESS energy storage system achieves seamless coordinated operation via MPC, substantially expanding the CL voltage regulation range. However, disparities in internal parameters and switching states among parallel ESs may induce significant circulating currents, posing a threat to system stability. To mitigate this issue, a multiagent-based MPC approach is introduced. This method incorporates a circulating current suppression term into the cost function alongside the CL voltage regulation objective, ensuring balanced currents distribution across all units while significantly enhancing system stability and coordination. Simulation and experimental results demonstrate the effectiveness of the proposed multiagent MPC strategy, confirming its capability to significantly improve the stability and performance of the parallel PV/BESS ESs system.

The emerging vehicle-to-vehicle communication technique enables vehicle platoon control, which greatly increases road throughput and travel efficiency. For cybernetically connected vehicles (CVs) with dynamics heterogeneities subject to actuator uncertainties, a dynamic event-triggered platoon control protocol is proposed in this paper to significantly reduce communication overheads. By considering the platoon problem of CVs as an output tracking consensus problem of heterogeneous multi-agent systems (MASs), a hierarchical control framework composed of an upper-level interactive event-triggered observer layer and a lower-level local tracking controller layer is established. Within the proposed framework, considering a virtual dynamic leader with non-zero inputs and external disturbances, a distributed observer is first designed for each following vehicle to observe the leader in an event-triggered manner. An internal dynamic variable is introduced to construct the dynamic triggering law, which not only relaxes the requirement on continuous state transmission in triggering detection but also benefits the exclusion of the Zeno behavior. Then, the solutions of a set of regulator equations associated with the proposed observer and the observer itself are integrated into the local tracking controller design to guarantee the tracking ability of each follower agent to its own observer. As thus, an event-triggered platoon control mechanism of heterogeneous CVs is proposed. Simulations and experiments on unmanned ground vehicles (UGVs) are conducted, which validate the effectiveness of the proposed platoon control method.

This study considers both a robot and its actuator dynamics to construct a performance recovery controller for rover vehicle applications. The resultant controller structures the simple single loop forms for linear velocity and yaw angle measurements, equipping the feed-forward compensators against disturbances caused by uncertainties of the motion equations. The salient aspects of this paper are (a) the order reduction observers estimate the velocities and accelerations independent from the motion equations; the simple observer-based order reduction output-feedback controller forms the proportional-integral-derivative and proportional-integral-double derivative with the nonlinearly structured gains; and (c) the closed-loop system guarantees to recover the closed-loop responses to the first-order desired system exponentially. A prototype rover vehicle with a MyRIO1900 processor validates the practical benefits of these three novelties above.

Autonomous ground vehicles (AGVs) play an important role in transportation, environmental exploration, and so on. However, they may suffer from inevitable sensor and actuator faults which degenerate the setpoint tracking performance. Within this context, this paper focuses on the issues of deploying event-triggered fault-tolerant controller based on reduced-order descriptor observer. First, allowing for the AGV kinematics and the setpoint tracking objective, we design a reduced-order descriptor sliding mode observer to synchronously reconstruct the tracking error states as well as physical faults. Then, we develop an event-triggered communication scheme in the control center side, leading to the tolerant controller by virtue of reconstructed tracking error dynamics with an improved transmission efficiency. In addition, we perform the strict theoretical guarantee of both estimation stability and tracking stability, based on which, the estimator and controller gains in conjunction with event trigger parameters are codesigned. Finally, the correctness and effectiveness of theoretical derivation are examined by the experimental study of setpoint tracking of AGV.

The adaptive neural network (NN) control for the fixed-wing unmanned aerial vehicle (FUAV) under the unmodeled dynamics and the time-varying switching disturbance (TVSD) is investigated in this article. To better describe the TVSD induced by the change in the flight area of the FUAV, a switching augmented model (SAM) based on the known information about the TVSD is proposed first. The parameter adaptation technique is used to estimate the related TVSD. Thereafter, the time-varying disturbance that cannot be described by the SAM is estimated by the disturbance observer (DO). The radial basis function NN (RBFNN) is adopted to approximate the unknown unmodeled dynamics. The coupling terms derived from the co-design of DO and the parameter adaptation (PA) are separated by some inequality techniques. Then, the separated unknown terms are eliminated by designing the parameters of the controller and that of the adaptive law. The separated known terms are tackled by adding robust control terms to the controller. In addition, to improve the estimation performance for the TVSD and RBFNN, the auxiliary system in the DO form is designed. Sufficient stable conditions about the closed-loop switched system (CLSS) are obtained with and without the inequality about the switching times. Finally, an illustrative example is given to show the feasibility and advantage of the proposed control strategy by the attitude model of the FUAV.

This article presents a novel learning-based collaborative control framework to ensure communication security and formation safety of nonlinear multiagent systems (MASs) subject to denial-of-service (DoS) attacks, model uncertainties, and barriers in environments. The framework has a distributed and decoupled design at the cyber-layer and the physical layer. A resilient control Lyapunov function-quadratic programming (RCLF-QP)-based observer is first proposed to achieve secure reference state estimation under DoS attacks at the cyber-layer. Based on deep reinforcement learning (RL) and control barrier function (CBF), a safety-critical formation controller is designed at the physical layer to ensure safe collaborations between uncertain agents in dynamic environments. The framework is applied to autonomous vehicles for area scanning formations with barriers in environments. The comparative experimental results demonstrate that the proposed framework can effectively improve the resilience and robustness of the system.

This article proposes two novel projection neural networks (PNNs) with fixed-time ( ) convergence to deal with variational inequality problems (VIPs). The remarkable features of the proposed PNNs are convergence and more accurate upper bounds for arbitrary initial conditions. The robustness of the proposed PNNs under bounded noises is further studied. In addition, the proposed PNNs are applied to deal with absolute value equations (AVEs), noncooperative games, and sparse signal reconstruction problems (SSRPs). The upper bounds of the settling time for the proposed PNNs are tighter than the bounds in the existing neural networks. The effectiveness and advantages of the proposed PNNs are confirmed by numerical examples.

Considering the memory property of the fractional calculus and the potential diverging state of the open-loop mode, existing analysis methods are difficult to solve the finite-time issue of intermittently controlled fractional-order systems (FOSs). This article studies the finite-time synchronization of coupled FOSs with nonlinearity via fuzzy intermittent quantized control and two novel fractional-order differential inequalities. An interval-type 2 Takagi–Sugeno fuzzy technique is introduced, which not only facilitates the handling of the nonlinear term in the error dynamic system, but also greatly simplifies the control design. Synchronization conditions in form of linear matrix inequalities are provided by designing a novel Lyapunov function on the basis of ellipsoidal norm. Moreover, two corollaries show the generality of the new analysis framework. Compared with existing results, it is amazing that the decreasing magnitude of Lyapunov function on the control intervals can be smaller than its increasing magnitude on the subsequent noncontrol interval. Finally, Chua’s system is used to clarify the effectiveness of theoretical outcomes.

This paper presents event-triggered integral re-inforcement learning methods to solve nonlinear nonzero-sum differential game problems. Firstly, for nonlinear systems, by constructing coupled Hamilton-Jacobi equations, the theoretical basis for solving multi-player nonzero-sum game problems is established. With the help of integral reinforcement learning, the approximate optimal control strategy corresponding to each player can be obtained without knowing the drift dynamics of the system. Then, the event-triggered mechanism with preliminary operation is constructed by designing appropriate triggering condition. The dynamic triggering mechanism is further integrated into the algorithm architecture of online learning method to realize aperiodic adaptive learning and sampling control, and effectively save system computing and communication resources. Finally, the effectiveness of the proposed reinforcement learning method is verified by theory analyses and simulation experiments.

This paper tackles the problem of event-triggered unified performance state estimation in neural networks with time-varying delays. A novel event-triggered methodology is introduced, aiming to balance the performance of the state estimator and the network's communication bandwidth. The proposed method leverages a triggered-parameter-dependent integral inequality with matrices that consider the event-triggered mechanism, capturing the interplay between the time-varying delay and system states. This innovative approach guarantees the asymptotic stability of the estimation error system, thereby meeting the$H$∞ performance criterion. The efficacy of the proposed condition is demonstrated by a numerical example.

In this paper, the issue of designing a dynamic event trigger mechanism for an estimator-based networked controller is discussed. Instead of separating the uncertainty estimation module and the control algorithm into two layers as most of the existing works do, the approach proposed in this paper can trigger the estimator and the controller simultaneously. Moreover, this paper is the first to consider nonlinear and state-correlated control gain matrix for both event-triggered observer design and event-triggered controller design. A new dynamically triggered extended state observer is first constructed for uncertain first-order systems with nonlinear and state-correlated control gain matrices. To avoid potential state oscillation, a new uncertainty estimator is then developed on the basis of the proposed observer. To ensure stability of the uncertainty estimator and the estimator-based controller simultaneously, a new dynamical event trigger mechanism that is free of Zeno behaviour is developed. Hardware-in-the-loop experiments based on an omnidirectional robot are also carried out to illustrate the potential of the new techniques in practice.Note to Practitioners—This article discusses the event-based control design of networked systems that can be implemented in areas such as intelligent transportation and robot-based rescue. We addressed two important practical issues in specific. The first one is how to perform adaptive event-based uncertainty estimation for nonlinear first-order systems. While the second question is how to develop a unified dynamic trigger scheme that determines if a new event has arrived based on both the uncertainty estimation error and the tracking error to maintain system stability. The proposed scheme can be attached to the sensor module to reduce the frequency of communication from the sensor to the controller. Based on the results of hardware-in-the-loop experiments, when the time-triggered step size is 8 ms and the desired speed of the rover does not exceed 0.144 m/s, the proposed scheme can reduce around$90\%$of wireless data transmission without compromising much tracking precision. Meanwhile, a thorough analysis regarding what affects the ratio of the overall saved resources in practice is provided based on the results of comparative experiments.

This article presents a model predictive control (MPC) design based on dynamic event-triggered mechanism (DETM). In the sensor-to-controller channel, the networks are unreliable in the sense that the transmitted signals may suffer from channel fading which is characterized by a stochastic process. In the controller-to-actuator network, the Denial-of-Service (DoS) attacks are taken into consideration whose dynamic behavior is described by a binary Markov process. Moreover, the external disturbance is considered. First, a DETM is employed to schedule the data transmission with the aim of reducing the communication burden. Then, an$H_{\infty}$-type cost function is applied in the MPC design to improve the system’s robustness against disturbance. Different from the conventional MPC, the novelty of developed MPC is that it can improve the communication efficiency and enhance the robustness against these network-induced issues simultaneously. At last, the validity and superiority of the proposed technique are demonstrated by simulation studies.Note to Practitioners—With the development of information technologies, the practical control systems are highly integrated with wireless networks. In such case, the network-induced issues are very important. For example, in the platooning control of automated vehicles, the conventional time-triggered control scheme leads to unnecessary waste of communication resource and thus heavy communication burden. Especially, this issue becomes particularly important when the number of vehicles increases. This article was motivated by this and it suggests a new control design based on DETM. Moreover, in control systems, it is necessary to utilize the state measurement to compute control signals. However, when the measured state information is affected by DoS attacks and channel fading, the system performance and even stability will be seriously damaged. Furthermore, the external disturbances widely exist in practical engineering. Considering the influence of these issues, this paper studies the design of MPC. The obtained results aim to provide a helpful reference for the controller design under various network-induced issues, involving bandwidth constraints, DoS attacks and channel fading. It is expected that the proposed MPC design can be applied to more practical engineering systems.

In this article, several fixed-time (FT) neurodynamic algorithms with time-varying coefficients are introduced for composite optimization problems. The remarkable features of neurodynamic algorithms are FT convergence from arbitrary initial conditions with faster convergence rate by choosing different time-varying coefficients. The FT convergence of neurodynamic algorithms can be proved by the Polyak-${\L}$ojasiewicz condition, which is beyond strong convexity condition. The upper bounds of the settling time for time-varying neurodynamic algorithms are explicitly given. The robustness of neurodynamic algorithms under bounded noises are further studied. In addition, the proposed neurodynamic algorithms are also utilized for dealing with absolute value equations and sparse signal reconstruction problems. The circuit framework for FT neurodynamic algorithms is subsequently introduced, and an example simulated in Multisim 14.3 is provided to verify the practicability of the proposed analog circuits. Numerical experiments on image recovery and sparse logistic regression are conducted to validate the superiority of the proposed algorithms.

This study proposes a resilient asynchronous controller for Markovian jump neural networks, which can be impervious to the dual cyber attacks that act on actuators and sensors. The complicated occasions of uncertain system modes, actuator and sensor attacks, and unknown attack information are all considered. It is known that sensor attacks can generate corrupted signals to destroy the controller, and actuator attacks can maliciously tamper with the control signals. Mindful of such circumstances, a resilient controller is developed to defend against actuator and sensor attacks as well as to guarantee good synchronization performances. To overcome the unknowns of the occurred attacks, some new adaptive laws for adjusting attack parameters are introduced into the controller to assist with offsetting attack-induced influences. Under the designed controller, the synchronization error dynamic system is proven to be ultimately bounded within a known region, and then the obtained results are extended to address some other cases. Furthermore, a practical example of an analog resistance-capacitance network circuit and some comparative studies are demonstrated to verify the feasibility and superiority of the proposed controller.

This paper proposes a new dual-design framework to investigate the consensus tracking problem of general switched heterogeneous nonlinear multi-agent systems (MASs) with an uncertain target. A distributed observer that only uses the neighbors’ information is constructed to estimate the target signal by designing a time-varying positive-define matrix function and mode-dependent observer gain matrices. After that, a distributed tracking algorithm is developed to solve the three consensus issues among the observer states, tracker states, and target states at the same time by utilizing the neighbors’ observer states and designing additional mode-dependent feedback gain matrices. The distinct merit of the research is that the designed Lyapunov function is strict monotone decreasing at switching instants, discovering the positive effect of the switching on the consensus. As an application, heterogeneous switched Chua’s circuits are provided to demonstrate the distributed observers and tracking algorithms.

The problem of fast accurate trajectory tracking for the underactuated wheeled mobile robots (WMRs) with unknown dynamics and external disturbances is investigated in this article. A novel adaptive fuzzy prescribed performance control strategy is proposed to deal with the problem. Instead of the approach angle or the azimuth angle, a pair of heading- and orientation-related auxiliary variables is adopted to deal with the underactuation of the WMR. In place of the transverse function, the barrier function is combined with the adaptive fuzzy logic system to cope with the unknown WMR dynamics, as well as environmental disturbances. Moreover, the barrier function is used to confine the position error rather than the tracking errors. By doing so, a continuous singularity-free high-performance control scheme is derived, which ensures that the WMR tracks the reference trajectory with the predefined speed and accuracy. Finally, a couple of comparative simulation studies are carried out to illustrate the above theoretical findings.

This article analyzes the stability and stabilization of interval type-2 (IT-2) Takagi–Sugeno (T–S) fuzzy systems by proposing novel time-dependent memory sampled-data control (TDMSDC) scheme based on the sampling-dependent functional approach. Unlike the existing studies, the novel TDMSDC, incorporates both conventional sampled-data control (SDC) and memory SDC schemes, along with the relation of sampling-time variable and time changes within the sampling period, is introduced. The novel TDMSDC overcomes the limitation of conventional SDC schemes, which only stays constant within the sampling period, thus enhancing control performance. Next, a sampling-dependent looped Lyapunov–Krasovskii functional, integrating information on sampling periods with various degrees, fuzzy membership function (MF), sampling-dependent matrices, and signal transmission delay, is constructed. Leveraging this novel functional and constraint condition of MFs, the asymptotic stability conditions are obtained in terms of linear matrix inequalities for IT-2 T–S fuzzy systems under the novel TDMSDC technique. Finally, the numerical examples demonstrate the superiority and efficiency of designed control method.

In this article, the improved command-filteringbased fixed-time fuzzy adaptive control of strict-feedback uncertain nonlinear systems with full state constraints is studied. Firstly, a new filtering approach is designed to improve the convergence speed and solve the “explosion of complexity” problem. And the filtering error can be effectively eliminated by a novel compensating mechanism. Then, barrier Lyapunov function with the filtering approach handles full state constraints in the system, so that the states will not violate the specified ranges. The proposed method ensures the tracking error converges to the neighborhood of the origin rapidly in a fixed time. Finally, the effectiveness and advantages of the proposed method is verified through simulation of a single link robot system.

This article investigates multiasynchronous control for asymptotic stabilization (AS) in mean-square and nonweighted$\mathcal {L}_{2}$-gain for a class of switched Takagi–Sugeno fuzzy systems with both time-varying delay and infinite-time distributed delay (mixed delays) as well as cyber-attacks. The proposed event-triggered controller not only is mode-dependent but also excludes Zeno behavior automatically with two tunable parameters to adjust the event-triggering (ET) number. A novel Lyapunov–Krasovskii functional (LKF) with a negative term is established to greatly reduce the conservatism and simplify the analysis of nonweighted$\mathcal {L}_{2}$-gain by ensuring its increment at switching instants be smaller than one. Three main results are provided to guarantee the AS in mean-square and design the control gains and weights of the ET mechanism, as well as the optimal nonweighted$\mathcal {L}_{2}$-gain. Compared with existing results, the new asynchronous control techniques eliminate the limitation of divergence in mismatched intervals for LKF. Numerical simulations demonstrate the merits of the theoretical analysis.

This article studies the stability control problem of uncertain nonlinear systems with unknown multiple classes of actuator faults. We investigate not only traditional actuator gain and bias faults but also a class of novel actuator faults, i.e.,input power faults, which are induced by the change of system input powers. By combining fuzzy logic systems, adaptive control, and other control technologies and methods, we develop an adaptive fault-tolerant fuzzy control scheme that guarantees that the system under scrutiny is asymptotically stable and the system states asymptotically converge to an adjustable small neighborhood of the origin. Compared with the existing results, input power faults, as a class of novel actuator faults, are first proposed and investigated in this article, and the corresponding fault-tolerant control scheme is studied. Furthermore, even if the system is subject to unknown input power faults, the actuator faults, especially bias faults, can be compensated properly. Based on a mechanical system, the simulation results indicate the effectiveness of our proposed fault-tolerant control method.

This article focuses on finite-time$\mathcal {L}_{2}$stabilization of T–S fuzzy systems with time delays and parameter uncertainties via intermittent control. To cope with the effects of parameters uncertainties, time delays, and intermittent divergence simultaneously, a new finite-time stability lemma for intermittently controlled systems is presented. Then, a weighted 2-norm Lyapunov–Krasovskii functional (LKF) is established, which has the advantage that it is convenient to derive less conservative linear matrix inequality sufficient conditions and to overcome the difficulty in analyzing$\mathcal {L}_{2}$performance under intermittent control frameworks. Another advantage of our result over existing results is that the growth increment of the LKF on the noncontrolled interval can be larger than the decreasing magnitude on the controlled interval. The merits of the theoretical results are examined by a numerical example and a coupled Chua's circuit.

The issue of spatiotemporal adaptive fuzzy state profile tracking (SAFSPT) control is addressed in this study for parabolic partial differential systems (PPDSs) on a hypercube exposed to unknown nonlinear dynamics. Specifically, a spatiotemporal fuzzy set on a hypercube is first constructed from the three-dimensional fuzzy set on a simple 1-D space domain to completely take into account the spatiotemporal coupling property of the PPDS state. Then a spatiotemporal fuzzy system is developed via the spatiotemporal fuzzy set to approximate any real continuous functions on an open subset of a separable Hilbert space. It is shown that any continuous functions can be approximated by the proposed spatiotemporal fuzzy system in arbitrary precision. Both indirect and direct SAFSPT control strategies are put forth with the use of the spatiotemporal fuzzy system. The closed-loop tracking error system's stability is thoroughly examined in terms of Lyapunov stability. Finally, the effectiveness and benefit of theoretical results are illustrated by extensive simulation experiments.

This article proposes a practical adaptive fuzzy backstepping control (FBC) scheme for the constrained locus tracking problem of a nonlinear cable-driven manipulator. Specifically, fuzzy logic systems aim to approximate unknown nonlinear terms, and the adaptive backstepping control achieves tracking of the desired motion state by recursively designing the virtual references and control laws. Compared with the existing FBC, the practicality is enhanced in the sense that: 1) simultaneous compliance with prescribed performance and state constraints; 2) no need for the tricky process of finding feasibility conditions for virtual references under strict constraints; and 3) insensitive to system initial conditions for the tracking error convergence of the constrained system. These advanced features lie in the improvement of backstepping protocols through the novel coordinate transformation mechanism, the designed asymmetric barrier Lyapunov functions, and adaptive fixed-time control laws. Application and comparison studies based on a laboratory-developed cable-driven manipulator not only confirm the effectiveness but also show the advantages of the proposed scheme.

Driven by the rise of collaborative robots, a lot of work has focused on the transparency and stability of physical human–robot interaction (pHRI), in which most of the efforts do not take the requirement of multiple tasks into account. However, the spectrum of applications for collaborative robots has been continuously broadened, and robots without the ability to perform multiple tasks simultaneously may not be capable of collaborating in certain scenarios. In this article, we provide a fuzzy-based multitask intelligent control framework of collaborative robots for pHRI. Our controller formulation consists of “outer-loop” and “inner-loop.” In the “outer-loop,” a fuzzy logic system predicts human desired motion trajectory for the robot to track. In the “inner-loop,” the robot is driven by a hierarchical multitask controller to track the trajectory generated by the “outer-loop” and perform other subtasks simultaneously. With null space projections, the whole task stack can be implemented in a strict task hierarchy in order of priority. The weight-tuning law of the FLSs and the hierarchical multitask control law are given based on Lyapunov stability analysis. The proposed control framework is applied to a mobile manipulator and the effectiveness is verified by exploratory experiments. Results confirm the effectiveness of the proposed control framework and compare its performance with other approaches.

This paper addresses the incipient fault detection and isolation (FDI) problem for high-speed rail vehicle suspension systems and explores further results of data-driven total measurable fault information residual (ToMFIR) with Takagi-Sugeno (T-S) fuzzy dynamical mode. Firstly, the T-S fuzzy model is constructed to represent the global nonlinear dynamics of a China Railway High-speed (CRH) trailer car. Secondly, the data-driven ToMFIR based on T-S fuzzy theory and system identification is designed with the help of system input/output(I/O) data models. Moreover, Jensen-Shannon (JS) divergence based evaluation function is proposed for monitoring the slight changes of data-driven ToMFIR. Furthermore, generalized likelihood ratio (LR) reconstruction method combined with data-driven ToMFIR is designed for incipient isolation of suspension actuator and sensor faults. Finally, simulation studies conducted on SIMPACK-MATLAB/Simulink Co-simulation environment are given to demonstrate the effectiveness of the developed FDI scheme for both slowly developing faults and incipient faults with intermittent characteristics.

The control design method for a class of non-strict feedback nonlinear systems is studied in this brief considering uncertain nonlinearities and unknown non-symmetrical input dead-zone. Combining with the finite-time command filtered backstepping (FCFB) technique, a novel finite-time adaptive control approach is proposed in which a neural network-based methodology is adopted to cope with the uncertain nonlinearities in the non-strict feedback form. The input dead-zone model is transformed into a simple linear system with unknown gain and bounded disturbance which is estimated by an adaptive factor. Using the finite-time Lyapunov theory, the system convergence is proved. And the effectiveness of the proposed control scheme is verified through comparative numerical simulations.

As a crucial energy storage for the spacecraft power system, lithium-ion batteries degradation mechanisms are complex and involved with external environmental perturbations. Hence, effective remaining useful life (RUL) prediction and model reliability assessment confronts considerable obstacles. This article develops a new RUL prediction method for spacecraft lithium-ion batteries, where a hybrid data preprocessing-based deep learning model is proposed. First, to improve the correlation between battery capacity and features, the empirically selected high-dimensional features are linearized by using the Box-Cox transformation and then denoised via the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method. Second, the principal component analysis (PCA) algorithm is employed to perform feature dimensionality reduction, and the output of PCA is further processed by the sliding window technique. Third, a multiscale hierarchical attention bi-directional long short-term memory (MHA-BiLSTM) model is constructed to estimate the capacity in future cycles. Specifically, the MHA-BiLSTM model can predict the RUL of lithium-ion batteries by considering the correlation and significance of each cycle's information during the degradation process on different scales. Finally, the proposed method is validated based on multiple types of experiments under two lithium-ion battery datasets, demonstrating its superior performance in terms of feature extraction and multidimensional time series prediction.

This article is concerned with the synchronization issue of discrete Markov jump neural networks (MJNNs). First, to save communication resources, a universal communication model, including event-triggered transmission, logarithmic quantization, and asynchronous phenomenon, is proposed, which is close to the actual situation. Here, to further reduce conservatism, a more general event-triggered protocol is constructed by developing the threshold parameter as a diagonal matrix. To cope with mode mismatch between the nodes and controllers due to potentially occurring time lag and packet dropouts, a hidden Markov model (HMM) method is adopted. Second, considering that state information of nodes may not be available, the asynchronous output feedback controllers are devised by a novel decoupling strategy. Then, sufficient conditions based on linear matrix inequalities (LMIs) for dissipative synchronization of MJNNs are proposed with the virtue of Lyapunov techniques. Third, by eliminating asynchronous terms, a corollary with less computational cost is devised. Finally, two numerical examples verify the effectiveness of the above results.

This article concentrates on proposing a scalable deep reinforcement learning (DRL) method for a multiple unmanned surface vehicle (multi-USV) system to operate cooperative target invasion. The multi-USV system, which is made up of multiple invaders, needs to invade target areas in a specified time. A novel scalable reinforcement learning (RL) method called Scalable-MADDPG is proposed for the first time. In this method, the scale of the multi-USV system can be changed at any time without interrupting the training process. Then, to mitigate the policy oscillation after applying Scalable-MADDPG, a bi-directional long-short-term memory (Bi-LSTM) network is constructed. Moreover, an improved -greedy strategy is proposed to help balance the exploration and exploitation in RL. Furthermore, to enhance the robustness of the optimal policy, Ornstein-Uhlenbeck (OU) noise is added in this improved -greedy strategy during the training process. Finally, the scalable RL method is used to help the multi-USV system perform cooperative target invasion under complex marine environments. The effectiveness of Scalable-MADDPG is demonstrated through three experiments.

The problem of resilient optimal bipartite tracking control for heterogeneous multi-agent systems with multiple targets under denial-of-service (DoS) attacks is investigated in this paper. A bipartite tracking mechanism is devised in which the agents track the targets under bipartite consensus control, which becomes non-autonomous. This is owing to DoS attacks, as the agents cannot obtain real-time information on the tracked targets and neighboring agents. Consequently, A target observer with a storage module has been developed for the efficient estimation of agent states and the storage of observed information as historical data. By recalling the historical data of the observed state, a new type of distributed resilient optimal controller is formulated, which can achieve the control objective in the case of communication blockage, while minimizing the performance index function of the system. Numerical simulations are performed to verify the proposed secure control design.

This article proposes an asymptotic consensus control algorithm for a vehicle platoon with time-varying communication delays and external disturbances. To avoid continuously monitoring the state of the vehicles and utilize network communication resources efficiently, an event-triggered control (ETC) with three free parameters based on periodic sampling is designed. Consider a switched topology, a novel method is constructed to make the discretized Lyapunov–Krasovskii functional (LKF) monotonically decreasing at switching instants, which allows for better analysis of nonweighted$\mathcal {L}_{2}$-gain. The main results give the sufficient conditions for global asymptotic consensus by linear matrix inequalities (LMIs). Finally, a numerical simulation is presented to show the validity of the new analysis technique.

This article deals with the tracking control problem for a class of multi-input and multioutput (MIMO) nonlinear systems with uncertain dynamics under the premise of feedback path transmitted by dynamic event-trigger mechanism. The neural network adaptive plant model is designed to generate predictive system states for controllers. Command filters are introduced to fix the jumping problem of virtual controllers while avoiding the issue of “explosion of complexity” caused by the recursive differentiate behavior in conventional event-triggered backstepping controllers design. Moreover, dynamic event-trigger conditions are constructed to decide the feedback path aperiodically transmit plant states instants. Simulation results indicate that this proposal can reduce the communication times considerably without degrading system performance.

This article presents a novel event and learning-based resilient formation control strategy for heterogeneous multiagent systems subjected to denial-of-service (DoS) attacks and uncertainties. It involves a decoupled cyber-layer and physical system layer design that enables a distributed and model-free approach. In the cyber-layer, the design is an event-triggered resilient observer for a reference exosystem estimation under DoS attacks using dual adaptive laws and an optimal algorithm. This approach eliminates the need for global information of the communication topology and enhances system resilience under attacks. In the physical system layer, the design is a model-free formation output controller for heterogeneous agents based on off-policy reinforcement learning. The incorporation of a new rank condition improves the convergence performance. Experiments using unmanned ground vehicles are conducted for scanning a physical area to verify the effectiveness and resilience of the proposed control strategy.

To design a finite impulse response (FIR) filter for Markov jump linear systems (MJLSs), a fundamental problem is to avoid constructing the extended state-space model without knowing the mode sequence. This article proposes a new FIR filtering algorithm for MJLSs to address this problem. Under each mode, the variational inference approximates the posterior distribution as a product of Gaussian distribution and inverse gamma distribution by minimizing the Kullback–Leibler divergence. A recursion is then derived over a predefined estimation horizon, where the influence of abandoning the measurements beyond the horizon is compensated. By setting the estimation horizon length as a fixed number, the recursion achieved becomes a new FIR filter for MJLSs, while a new suboptimal Bayesian estimator appears when the horizon length is determined as the full horizon. A Newtonian tracking example as well a three degree-of-freedom hover model is presented to demonstrate that the proposed FIR method has good immunity against unpredicted modeling uncertainties at the cost of extra computational resources and memories, and its full-horizon form does not show this feature and may lose to some exiting algorithms when the underlying model is accurate.

This article studies the cooperative output regulation problem for multiple flexible manipulators. First, based on a two-parameter-correlated adaptive law, a new fully distributed observer is proposed. Different from the existing works, the observer does not depend on global graph information and can handle the uncertainties in the leader system. Second, a barrier-function-based sliding mode disturbance observer is proposed for the flexible manipulator to estimate the state-dependent disturbance. This new disturbance observer does not depend on the exact form of the disturbance. We show that the fully unknown state-dependent disturbance can be estimated and rejected in finite time. Finally, based on the proposed sliding mode disturbance observer, a new adaptive controller is proposed. The controller does not require the tip-end state information of the flexible manipulator, which is beneficial for the implementation. Both simulations and experiments show the validity of the presented method.

In this paper, we propose a reinforcement learning-based safe control strategy for uncertain heterogeneous multi-agent systems. The objective is to achieve collision-free time-varying formations under switching topologies and with limited network resources. Without requiring global communication information, an event-triggered observer is designed to decouple the heterogeneous dynamics from switching networks and reduce data transmission frequency. A data-driven off-policy reinforcement learning algorithm is developed for addressing the robust safe formation control problem. The algorithm is capable of solving non-quadratic optimization problems without requiring model information. The proposed strategy is applied to a multi-unmanned ground vehicle (UGV) system for a patrolling mission, and the experimental results verified the effectiveness of the proposed strategy.

The composite velocity and acceleration constraints suffered by nonholonomic mobile robots during motion severely hamper the stability and smoothness of the cluster. This paper presents an applicable control framework for the robust and smooth implementation of distributed formations based on nonholonomic mobile robot clusters against velocity and acceleration saturation. The decoupled feedback linearization control of cascaded position, heading and wheel velocity is used to achieve distributed time-varying formations with modular and scalable stabilization of the triple-level errors. The adaptive auxiliary dynamics are incorporated into the distributed coincident errors to reduce velocity and acceleration saturation, which improves the overall transient results and stability range. The adaptive saturation extended observer in the substrate dynamics is conceived to simultaneously estimate the lumped disturbances and suppress undesired peaks, smoothly enhancing the robustness of wheel velocity control. Ultimately, comparative simulations and extensive experiments for mobile robots are performed to test validity and practicality.

This paper concerns distributed state estimation for linear systems over a sensor network. Two novel algorithms utilizing the framework of moving horizon estimation (MHE) are proposed, which are fully distributed, scalable and allow for taking into account constraints on the noises and the system states. The proposed methods estimate the state by minimizing a local quadratic objective function, which can be efficiently solved by quadratic programming (QP). Consensus technique is employed to fuse information to construct the local quadratic objective function for each node. The first algorithm, which minimizes a quadratic function involving consensus on measurement costs (CM), approaches the centralized MHE with a sufficiently large number of consensus steps. The second one involving consensus both on arrival costs (CA) and measurement costs , enjoys the benefits of both the CA and CM. To avoid directly running consensus on some functions, a novel consensus strategy for the CM is developed. The estimation errors of the proposed methods are proven to be stochastically ultimately bounded under certain conditions. Finally, numerical results are presented to verify the effectiveness of the developed algorithms.

This paper develops model-free optimal tracking policies for Markov jump systems by solving non-zero-sum games (NZSGs). First, coupled action and mode-dependent value functions (CAMDVFs) are built for solving a two-player NZSG and getting Nash equilibrium solutions. Second, we propose a value iteration (VI) algorithm to parallelly update policies under each mode by collecting data on different operation modes within each iterative window. Moreover, the iterative increasing convergence of the CAMDVFs is proved by introducing auxiliary functions between two adjacent iterations. It is worth pointing out that an influence function is introduced to remove abnormal data to improve the learning capability of the VI algorithm effectively. Finally, the tracking policies' validity, self-adaptability and application potential are verified by a numerical example and a generalized economic model.

Information and communication technology tremendously facilitates the operation efficiency and economy of modern power systems in recent years. However, risks such as bandwidth constraints and malicious attacks threaten the secure load frequency control (LFC) of power systems. To mitigate such risks, this paper proposes distributed observer-based event-triggered LFC schemes for multi-area power systems under cyber attacks. Considering the practical situation that only local system output information may be available, distributed observer-based LFC schemes are designed. Meanwhile, to reduce the communication burden, an event-triggered mechanism is adopted to design control laws, where both static and dynamic event-triggered approaches are taken and the dynamic one is proved to be more economical in terms of control cost. Verifiable sufficient conditions are established to guarantee the stability of the closed-loop system in the presence of cyber attacks and the controller gains are explicitly derived. Finally, validation studies on a three-area interconnected power system are carried out to demonstrate the proposed control schemes. Note to Practitioners—Load frequency is a crucial index for evaluating the quality of electric energy and thus LFC has brought considerable attention in the area of power systems control. Although many achievements have been made on the LFC of multi-area power systems, the risks such as bandwidth constraints and malicious attacks affect the normal operation of LFC due to the interconnection between different power systems. To deal with these risks, this paper focuses on designing event-triggered LFC to guarantee the stability of the frequency deviation while reducing the communication burden and mitigating cyber attacks. More importantly, the proposed event-triggered LFC is designed based on an observer and can be implemented in a distributed manner, which is relatively practical in real applications. The results presented in this paper aim to provide a helpful reference for stable and secure LFC design of multi-area power systems, such that the corresponding application research can be promoted.

This paper focuses on distributed estimation and smoothing for linear dynamic systems monitored by sensors that are organized as a network. To achieve low power consumption of sensors, the system is decomposed into several subsystems based on the observable canonical decomposition method. Each sensor in the network performs state estimation for one subsystem at most, while estimations for the majority of subsystems are obtained from the neighbors of each sensor. The optimal gains of the estimator and the smoother are pre-calculated offline, and a numerical solution is obtained by taking advantage of optimization algorithms. Furthermore, the boundedness of the prediction error covariance is guaranteed resulting from the bounded solution of a Riccati equation, and the performance of the smoother is analyzed. A numerical example is presented to validate the feasibility of the algorithm even if the sensor network suffers from network interruptions. The proposed algorithm is more robust against partial loss of predictions and recovers more rapidly, compared to some consensus-based distributed estimation algorithms.

This paper investigates the disturbance utilization-based attitude control for the fixed-wing unmanned aerial vehicle (UAV). The disturbance utilization condition (DUC) is formed based on the Lyapunov function analysis to retain the disturbance that benefits the stability of closed-loop systems. To reduce the sign-misjudgment of disturbance coupling terms induced by the DUC, the disturbance estimation error analysis auxiliary system (DEEAAS) is designed based on the disturbance observer (DO). Combined with the DEEAAS and the DO, the boundaries of the disturbance estimation error (DEE) are derived. Subsequently, the composite DUC is proposed based on the above boundaries and the given thresholds to replace the basic DUC. Then, the boundedness of the attitude tracking errors of the fixed-wing UAV can be ensured by the controller designed with the composite DUC. And combined with the derived boundaries of the DEE and the given thresholds, the adaptive DO and the adaptive DEEAAS are also designed to avoid the use of big parameters. In addition, sufficient conditions that stabilize the attitude closed-loop system of fixed-wing UAVs equipped with the disturbance utilization-based controller (DUBC) are given. Finally, the numerical simulation for the fixed-wing UAV illustrates the effectiveness of the proposed DUBC.

In this paper, we study$H_{\infty} $pinning synchronization control problem for Markovian intermittent coupled neural networks with time-varying coupling strengths, and the influence of multiplicative noises on actuators is also considered. A set of random variables of known mathematic expectations and variances is introduced to describe stochastically changing interactions among neural networks, and a two-states Markov chain is used to characterize the intermittently time-varying interactive relationship between neural network and its neighboring neural networks. By analyzing stability and$H_{\infty} $disturbance attenuation performance of the obtained synchronization error systems, two sufficient conditions are derived to ensure global synchronization and$H_{\infty} $global synchronization of the controlled networks respectively. Then, the pinning synchronization controllers are designed, and we can change control cost by varying the number of pinned nodes. Finally, an algorithm resulting the set of optimal pinned nodes which minimizes the$H_{\infty} $disturbance attenuation performance index is provided, and a numerical example illustrating the effectiveness of the obtained theoretical results is given.

This paper addresses the event-triggered input-output finite-time mean square synchronization for uncertain Markovian jump neural networks with partly unknown transition rates and quantization. Considering the limited network resources, an event-triggered technique and a logarithmic quantizer are both provided. The error system model with uncertainty is established in the unified framework. Then, based on Lyapunov functional approach, interesting results are presented to guarantee the properties of the input-output finite-time mean square synchronization for the error systems. Furthermore, some solvability conditions are induced for the desired input-output finite-time mean square synchronization controller under linear matrix inequality techniques. Eventually, the theoretical finding’s efficiency is shown by an example.

The current paper is devoted to studying global asymptotic$H_{\infty }$drive-response synchronization for a kind of switched nonlinear time-delay systems with output random injection attacks (IAs). An attack-decomposition method is proposed to derive an attack-free signal, by which an observer is designed to estimate the state of the driving system. Then, two mode-dependent event-triggering mechanisms (MDETMs) are respectively designed for observer-controller (O-C) and controller-actuator (C-A) channels to save the communication resources as much as possible. In order to analyze the effects of the switching on the$H_{\infty }$performance, a mode-dependent discretized Lyapunov-Krasovskii functional (LKF) is developed, which has the merit of monotone decreasing on any time-interval and switching instants. Sufficient criteria are given to ensure the$H_{\infty }$synchronization with non-weighted$\mathcal {L}_{2}$-gain, whether the attack is related to the output or not. Numerical simulations are provided to verify the non-weighted$\mathcal {L}_{2}$-gain performance with low conservatism.

This article addresses the problem of lateral control problem for networked-based autonomous vehicle systems. A novel solution is presented for nonlinear autonomous vehicles to smoothly follow the planned path under external disturbances and network-induced issues, such as cyber-attacks, time delays, and limited bandwidths. First, a fuzzy-model-based system is established to represent the nonlinear networked vehicle systems subject to hybrid cyber-attacks. To reduce the network burden and effects of cyber-attacks, an asynchronous resilient event-triggered scheme (ETS) is proposed. A dynamic output-feedback control method is developed to address the underlying problem. Conditions are derived to obtain the output-feedback controller and resilient asynchronous ETS such that the closed-loop switched fuzzy system is globally exponentially stable. Examples are provided to demonstrate the effectiveness and merits of the proposed new control design techniques.

In order to save network resources of discrete-time Markov jump systems, an event-triggered control framework is employed in this article. The threshold parameter in the event-triggered mechanism is designed as a diagonal matrix in which all elements can be adjusted according to system performance requirements. The hidden Markov model is introduced to characterize the asynchronization between the controller and controlled system. The effect of randomly occurring gain fluctuations is taken into account during the controller design. For the purpose of guaranteeing that the closed-loop system is stochastically stable and satisfies the strictly$(\mathcal {D}_{1},\mathcal {D}_{2},\mathcal {D}_{3})-\gamma -$dissipative performance, sufficient conditions are constructed by employing the Lyapunov function and stochastic analysis. After linearization, the proposed controller gains are obtained by solving the linear matrix inequalities. Ultimately, a practical example of the dc motor device is used to illustrate the effectiveness of the proposed new design technique.

This work is devoted to the nonasymptotic and robust fractional derivative estimation of the pseudo-state for a class of fractional-order nonlinear systems with partial unknown terms in noisy environments. In particular, the estimation for the pseudo-state can be obtained by setting the fractional derivative's order to zero. For this purpose, the fractional derivative estimation of the pseudo-state is achieved by estimating both the initial values and the fractional derivatives of the output, thanks to the additive index law of fractional derivatives. The corresponding algorithms are established in terms of integrals by employing the classical and generalized modulating functions methods. Meanwhile, the unknown part is fitted via an innovative sliding window strategy. Moreover, error analysis in discrete noisy cases is discussed. Finally, two numerical examples are presented to verify the correctness of the theoretical results and the noise reduction efficiency.

This article examines the distributed filtering problem for a general class of filtering systems consisting of distributed time-delayed plant and filtering networks with semi-Markov-type topology switching (SMTTS). The SMTTS implies the topology sojourn time can be a hybrid function of different types of probabilistic distributions, typically, binomial distribution used to model unreliable communication links between the filtering nodes and Weibull distribution employed to depict the cumulative abrasion failure. First, by properly constructing a sojourn-time-dependent Lyapunov-Krasovski function (STDLKF), both time-varying topology-dependent filter and topology-dependent filter are designed. Second, a novel nonmonotonic approach with less design conservatism is developed by relaxing the monotonic requirement of STDLKF within each topology sojourn time. Moreover, an algorithm with less computational effort is proposed to generate a semi-Markov chain from a given Markov renewal chain. Simulation examples, including a microgrid islanded system, are presented to testify the generality and elucidate the practical potential of the nonmonotonic approach.

This article investigates the finite-time control of the strict-feedback nonlinear system using composite learning based on the historical stack. The controller design adopts the backstepping scheme while the nonlinear function is introduced to avoid the singularity problem. The first-order Levant differentiator is introduced to obtain the filtered command signal and the compensation signal is further constructed. To indicate the learning performance, the historical data over the moving time window are analyzed to construct the predictor error using the maximum-minimum singular value algorithm. Furthermore, the finite-time neural update law is proposed. The stability of the closed-loop system is analyzed via the Lyapunov approach. The performance of the proposed method is verified using simulations.

In this article, an adaptive neural-network (NN) command-filtered output-feedback control strategy is proposed for a class of stochastic nonlinear systems (SNSs) with the actuator constraint. The problem of "explosion of complexity" existing in the conventional backstepping design procedure for SNSs is successfully resolved based on the command filter technique, and the error compensation mechanism is introduced to remove effectively the influence of filtered error. By using the NNs to identify the unknown nonlinear functions, a neural-network-based state observer is designed to estimate the unmeasurable states of the SNSs. Based on the quartic Lyapunov function, the stability of stochastic closed-loop systems is analyzed. It is proved that all signals of the closed-loop systems are bounded in probability, and the tracking error approaches a small neighborhood of the origin in probability. Finally, the effectiveness of the developed control algorithm in this article is verified by a comparison example.

This article aims to develop a noncascade speed control approach for permanent magnet synchronous motor (PMSM) systems based on the Takagi–Sugeno (T-S) fuzzy model. In noncascade control structures, the lack of firsthand current references imposes a tricky challenge in dealing with current constraints from the circuit and unmatched disturbances caused by load torques. To this end, a virtual reference-based fuzzy noncascade speed control scheme is proposed for PMSM systems. First, unmatched load torques are estimated by a fuzzy disturbance observer. And then, a novel virtual reference planner is constructed to inject the estimated unmatched disturbance into the control channel as feedback compensation. This improves antidisturbance performance, while keeping the current response within the feasible domain and reducing speed overshoot. Furthermore, the noncascade speed regulation problem is converted into a system stabilization problem and addressed by the composite control strategy based on the nonparallel distributed compensation. Finally, closed-loop stability with reduced conservatism is ensured through the fuzzy Lyapunov functions and improved slack variables. Simulations and experiments show that the proposed scheme achieves excellent speed response without overcurrent even under mismatched load torque variations.

This article studies the admissibility stability issue of Takagi–Sugeno fuzzy singular (TFS) systems with time-varying delay. First, negative-definiteness constraints for$n$th-order matrix-valued polynomials are established based on the Finsler's Lemma. A new reciprocally convex inequality (RCI) is developed according to the proposed negative-definiteness method. Then, an improved state decomposition Lyapunov–Krasovskii (L–K) functional is constructed based on the decomposed state method. The improved L–K functional is augmented by considering more information on TFS system states and the delay-dependent matrix. Based on the novel RCI and the L–K functional, a less conservative admissibility criterion is derived. Finally, a well-studied numerical example is performed to reveal the superiority and validity of the proposed admissibility criteria.

The existing hand gesture classification research based on surface electromyogram (sEMG) faces the challenges of low classification accuracy, weak real-time ability, weak robustness, few categories, and lack of explainability. In this article, we investigate how to classify sEMG signals for grasp recognition and human–robot interaction to consider these issues. A novel interval type-2 (IT2) fuzzy classifier based on explainable neural network is proposed for sEMG gesture recognition. Based on fully connected neural network, the adaptive moment estimation is applied to tune the antecedent parameters. The Ninapro data is adopted to test the performance of the proposed model, which realizes recognition of 52 gestures and achieves 95.04% categorization accuracy. Moreover, grasping experiments are conducted on computer, communication, and consumer electronics (3C) experiment platform to test the ability of the classifier in real scenarios. The experiment recognizes six gestures. The results of the 3C grasping experiment show that the proposed method achieves 99.4% offline training accuracy as well as 96.07% online test accuracy. Meanwhile, 89.4% of the classification results can be obtained within 0.5 s. The overall results demonstrate great potential for real-world applications, such as human intent detection and manipulator control.

This article investigates the resilient control problem for nonlinear cyber-physical systems with hybrid cyberattacks and physical constraints, including aperiodic denial-of-service attacks, deception attacks, input saturation, and external disturbances. The underlying system is described by the fuzzy model by decomposing the nonlinear plant into some local linear subsystems. An input saturating integrator and an antiwindup compensator are introduced inside a dynamic output feedback fuzzy controller to handle the input saturation in both amplitude and rate. Within the resulting fuzzy-model-based control framework, a novel adaptive event-triggered-based resilient control method is proposed to ensure that the insecure and imperfect system is stochastically stable with an$H_\infty$performance level. Simulation results of an autonomous vehicle operating in a wireless network environment are provided to verify the effectiveness of the proposed control method.

This article focuses on the problem of resilient leader-following consensus for multiagent systems against denial-of-service (DoS) attacks. Two update strategies based on event/self-triggering are proposed for the control protocols to handle the leader-following consensus in unreliable shared networks. We first propose a dynamic event-triggered communication scheme to mitigate unnecessary information transfer through energy-limited and vulnerable networks. Continuous communication between agents can be avoided. DoS attacks are supposed to be aperiodic and asynchronous at different edges. The concept of valid DoS attack interval is introduced, and the frequency and duration of attacks are analyzed. The leader-following consensus can be achieved when there exist DoS attacks using the dynamic event-based control strategy. Then, to avoid continuous event detection and save computation resources, a self-triggered communication function is developed. The next triggering moment is predetermined with the latest received state information, and the Zeno behavior is eliminated for the feasibility of the event/self-triggering scheme. Finally, a simulation is provided to verify the effectiveness of the proposed update strategies and control protocols.

This article investigates the adaptive learning control for a class of switched strict-feedback nonlinear systems with external disturbances and input dead zone. To handle unknown nonlinearity and compound disturbances, a collaborative estimation learning strategy based on neural approximation and disturbance observation is proposed, and the adaptive neural switched control scheme is studied in a dynamic surface control framework. In the adaptive learning control design, to obtain the evaluation information of uncertain learning, the prediction error is constructed based on the composite learning scheme. Then, the prediction error and the compensated tracking error are applied to construct the adaptive laws of switched neural weights and switched disturbance observers. The system stability analysis is carried out through the Lyapunov approach, where the switching signal with average dwell time is considered. Through the simulation test, the effectiveness of the proposed adaptive learning controller is verified.

This article investigates the collision-free cooperative formation control problem for second-order multiagent systems with unknown velocity, dynamics uncertainties, and limited reference information. An observer-based sliding mode control law is proposed to ensure both the convergence of the system's tracking error and the boundedness of the relative distance between each pair of agents. First, two new finite-time neural-based observer designs are introduced to estimate both the agent velocity and the system uncertainty. The sliding mode differentiator is then employed for every agent to approximate the unknown derivatives of the formation reference to further construct the limited-information-based sliding mode controller. To ensure that the system is collision-free, artificial potential fields are introduced along with a time-varying topology. An example of a multiple omnidirectional robot system is used to conduct numerical simulations, and necessary comparisons are made to justify the effectiveness of the proposed limited-information-based control scheme.

Decomposing data matrix into low-rank plus additive matrices is a commonly used strategy in pattern recognition and machine learning. This article mainly studies the alternating direction method of multiplier (ADMM) with two dual variables, which is used to optimize the generalized nonconvex nonsmooth low-rank matrix recovery problems. Furthermore, the minimization framework with a feasible optimization procedure is designed along with the theoretical analysis, where the variable sequences generated by the proposed ADMM can be proved to be bounded. Most importantly, it can be concluded from the Bolzano-Weierstrass theorem that there must exist a subsequence converging to a critical point, which satisfies the Karush-Kuhn-Tucher (KKT) conditions. Meanwhile, we further ensure the local and global convergence properties of the generated sequence relying on constructing the potential objective function. Particularly, the detailed convergence analysis would be regarded as one of the core contributions besides the algorithm designs and the model generality. Finally, the numerical simulations and the real-world applications are both provided to verify the consistence of the theoretical results, and we also validate the superiority in performance over several mostly related solvers to the tasks of image inpainting and subspace clustering.

In this article, a new asynchronous event-triggered-based output-feedback control method is proposed for a special type of networked Markovian jump systems susceptible to network delays and attacks. A novel controller is designed to make allowances for four cases that are common in practical situations: 1) a network delay; 2) false data injection (FDI) attacks; 3) asynchrony between system modes and controller modes; and 4) an event-triggered scheme. First, to avoid traditional assumptions about system delays, a novel Lyapunov function is proposed, based on which a new stability theory is formulated to guarantee exponential stability. Second, an FDI attack is represented as a stochastic process, which can be dealt with properly by the designed controller. Third, a new delay-based event-triggered scheme is brought into the controller design to reduce the network bandwidth consumption and save the network transmission resources. Then, an asynchronous controller is engineered with the aid of a hidden-Markov model, which can extend the application scope of the proposed method. Note that we put the above four factors into one framework, which implies a co-design of an anti-attack controller and an event-triggered scheme is developed. Finally, an operational amplifier circuit is simulated to verify the feasibility and superiority of the designed controller.

This article focuses on the secure consensus problem of linear multiagent systems (MASs) with quantized event-triggered control (ETC) against denial-of-service (DoS) attacks. DoS attacks with constrains on frequency and duration are discussed, which intend to block the communication links between agents to destroy the consensus. Due to the limited transmission capacity and communication resources, a uniform quantizer and an event-triggered mechanism (ETM) are considered to economize energy consumption. An ETC protocol based on the quantized relative state is designed to resist malicious DoS attacks. Then, sufficient conditions to guarantee the practical consensus of MASs are derived with finite data transmission rates, and the tolerance of DoS attack frequency and duration are given. The Zeno behavior does not exhibit by proving that lower positive bounds exist for all agents, indicating the feasibility of the proposed ETM. Finally, two simulation examples are given to verify the validity and superiority of the theoretical results.

The combination of Internet of Things (IoT) and artificial intelligence (AI) technology plays an important role in many fields, especially in the field of psychology and medical treatment. This work is mainly to study an affective robotics that can serve humans emotionally based on the IoT and AI technology. The design of affective robotics is important to understand the underlying mechanisms of human behaviors in real life. These mechanisms mainly include human nonverbal behaviors and affective states, which are important but difficult to be precisely modeled. To address this challenge, we introduce a human–robot interaction (HRI) architecture, including emotion recognition, affective computing, emotion diagnosis, and emotion control. First, we propose a system model based on HRI between affective robotics and human, in order to enhance the emotional service. Then, we develop a dynamical model with affective computing and control, where we provide a mathematical formulation method based on stochastic differential equations to quantify the emotional state. Furthermore, we perform the dynamic behavior analysis of the existence, boundedness, and stability of the model solution comprehensively. Numerical results are provided to demonstrate the validity and feasibility of the proposed design techniques.

In this article, a distributed adaptive event-triggered formation control strategy is proposed for unified nonlinear heterogeneous multiagent systems under uncertainties and disturbances to achieve time-varying formations. To reduce the frequency of data transmission, a distributed dual adaptive observer with an event-triggered strategy is developed to estimate the states of a reference exosystem. Without incurring prior global information about a communication graph, a novel robust formation controller, with dynamic distributed compensators for uncertainties and disturbances, is designed based on an observer result and a nonlinear internal control principle. Finally, both simulation and experiment are conducted for tracking and patrolling formation to verify the effectiveness of the proposed formation control strategy and its robustness. Note to Practitioners—This article addresses the collaborative formation problem of multiagent systems that has potential applications in transportation and disaster relief. The design of robust and energy-saving coordination strategies is challenging in heterogeneous multivehicle systems. The proposed distributed method is suitable for large-scale uncertain heterogeneous systems, and the use of the dual adaptive event-triggered strategy reduces the data transmission rate.

By considering the under-actuated characteristics of the quadcopter suspension transportation system and the external disturbance, the effective tracking control of the quadrotor transportation system faces great difficulties and challenges. An adaptive hierarchical sliding mode control (HSMC) scheme based on fixed-time sliding mode disturbance observer (FTSMDO) is developed for the anti-disturbance tracking control of under-actuated quadcopter suspension transportation system (UQSTS). The HSMC is used to deal with the under-actuated characteristics of the UQSTS, and the external disturbance is estimated by designing the FTSMDO. Synchronously, the disturbance estimation error is processed by designing the adaptive law. The stability analysis based on Lyapunov is applied to verify the uniform ultimate boundedness of the closed-loop system. Finally, the comparison of physical experiment upshots shows the effectiveness and potential of the proposed new control techniques.

This paper focuses on fixed-time event-triggered consensus tracking of high-order MAS over digraph and under DOS attack. The considered DOS attack is divided into connectivity-maintained attack and connectivity-broken attack. To detect connectivity-broken attack under event-triggered environment, a new cyberattack detection algorithm is presented. Next, using cyberattack detection results, a fixed-time event-triggered distributed observer is developed to estimate the leader’s states in the presence of DOS attack, which conducts event-triggered update in the normal condition and under connectivity-maintained attack and stops updating in the presence of connectivity-broken attack. Then, an adaptive event-triggered prescribed-time dynamic surface consensus tracking control protocol is proposed to eliminate mismatched disturbances caused by estimation error and achieve prescribed-time convergence of the tracking error. Stability analysis proves fixed-time convergence of the developed consensus tracking strategy and non-zeno analysis shows the developed distributed observer and consensus tracking control strategy exclude zeno behavior. This paper contributes to proposing a fixed-time leader-follower consensus control strategy for MAS under DOS attack via event-triggered approach, which eliminates the effect of DOS attack, achieves consensus tracking within a fixed time and reduces resources consumption. Finally, simulation results show the effectiveness and superiority of the developed consensus tracking approach.

Self-triggered pseudo terminal ingredients are proposed in this study for the classic dual-mode robust model predictive control (RMPC) of discrete-time linear systems. By resorting to the off-line design of pseudo terminal ingredients, viz. pseudo terminal set and pseudo terminal cost, the optimization problem to be solved online is transformed into several subproblems with short prediction horizons. Furthermore, the controller design is based on the nominal system state, which enables a self-triggered mechanism during the implementation. The proposed approach is able to steer the system state into a predetermined terminal constraint set via intermittent samplings. The resultant computational and communicational burden for the online part is significantly reduced, which brings great convenience for band-limited practical systems. Simulations demonstrate the effectiveness of the proposed approach.

Intending to address the issue of sensor fault-tolerant control, a model-free adaptive fault-tolerant constrained control strategy is proposed with virtual sensor technology. The novel introduction of observer-based model-free adaptive control to fault-tolerant control brings benefits to the nonlinear systems with unknown dynamics in the presence of sensor faults. In the design procedures, the dynamic linearization technique is utilized first to establish the equivalent linear model of nonlinear systems. Then, the virtual sensor technology is introduced by designing the novel state-observer-based estimation algorithm to fulfill the data-driven system dynamic and sensor fault modeling. Besides, the broad learning technique is employed with offline training to provide output information for observer-based data-driven modeling under the fault case. Based on the data-driven model, the fault-tolerant constrained control scheme is explored with the anti-windup compensator against input constraints caused by actuator saturation, and the stability analysis is supplied. Finally, the simulations are carried out for both affine and non-affine nonlinear systems to manifest the effectiveness and superiority of the proposed control strategy.

In this paper, the observer based neural adaptive finite-time containment control strategy for non-strict feedback nonlinear multi-agent systems is studied. The finite-time command filter is used to overcome the explosion of complexity problem and the established fractional power based error compensation signal is applied to compensate the filtering error caused by the filter. The distributed finite-time command filtered backstepping control method combines with the neural adaptive control technology and state observer is given, which ensures the containment control errors reach to the desired neighborhood of the origin in finite-time in the presence of uncertain dynamics and unmeasurable states in the system. The given numerical simulations show the effectiveness of the proposed control strategy.

In this paper, the resilient controller design and synthesis issues of networked control systems under denial-of-service (DoS) attacks are investigated via an adaptive event-triggered strategy. In a networked control system, DoS attacks have serious impacts on the security of communication, possibly causing degraded stability performance of the system. To remove threats from DoS attacks, an adaptive event-triggered communication mechanism is proposed, which also provides benefits in reducing the consumption of communication resources and relieving the pressure on network bandwidth. Since the system state information is usually not fully known, an observer-based controller is developed to stabilize the system and maintain a desired performance index despite the occurrence of stochastic attacks. Furthermore, a joint design method is proposed to obtain the controller gain, observer gain and event-triggered weight matrix. Finally, practical examples based on a four-tank system, an Internet-based three-tank system and an Internet-based test rig system are presented to illustrate the effectiveness of the proposed techniques to respond and eliminate the impact of DoS attacks.

This article investigates the asynchronous distributed finite-time$H_{\infty }$filtering problem for nonlinear Markov jump systems over sensor networks under stochastic attacks. The stochastic attacks, called two-channel deception attacks, exist not only between the Markov jump plant and the sensors but also among the sensors. It is assumed that the mode of the filter relies on, but is asynchronous with, that of the Markov jump plant. First, we establish a filtering error system that combines the Markov jump plant with the asynchronous filtering system. Then, we present an asynchronous distributed filter, which ensures the filtering error system mean-square finite-time bounded and satisfies a prescribed$H_{\infty }$performance level under the two-channel attacks. Finally, an example is given to illustrate the effectiveness of the presented filter.

For the target-tracking problem, full state of the target may not be available since it may be expensive or impossible to obtain. Thus, the state needs to be reconstructed or estimated only according to measured inputs and outputs. The impossible case that all followers can measure the target directly yields the study of distributed methods, thus reducing the communication and computation resource while resulting in more robustness. This article confronts these problems by addressing a distributed iterative finite impulse response (DIFIR) consensus filter for leader-following systems. A solution to the underlying problem is obtained by involving a distributed measurement model wherein not only the neighbors’ estimates are applied but also the directed measurement data are used, and expressed by a computationally efficient iterative algorithm. Applying this DIFIR strategy, it is shown that the leader’s estimates by all followers reach$H_{\infty }$consensus, whose value is the local unbiased estimates of the leader. Then, the result is extended to multiagent systems whose leader has unknown inputs. Incorporating the input estimates, a new DIFIR is proposed. Finally, examples are given to illustrate the consistency and robustness of the developed new design techniques.

This article investigates a class of finite-time cooperative tracking problems of heterogeneous mixed-order multiagent systems (MASs) with higher-order dynamics. Different from the previous works of heterogeneous MASs, the agents in this study are considered to have different first-, second-, or even higher-order nonlinear dynamics. It means that, according to different tasks and situations, the following agents can have nonidentical orders or different numbers of states to be synchronized, which is more general for the practical cooperative applications. The leader is a higher-order nonautonomous system and contains full state information to be synchronized for all agents with mixed-order dynamics. Accordingly, the spanning tree is defined based on the specific state rather than on the agent to guarantee that each following agent can receive adequate state information. Distributed control protocols are designed for all agents to achieve the ultimate state synchronization to the leader in finite time. The Lyapunov approach is used for the stability analysis and a practical example of mixed-order mechanical MASs verifies the effectiveness and performance of the proposed distributed control protocols.

Diagnosing the fault as early as possible is significant to guarantee the safety and reliability of the high-speed train. Incipient fault always makes the monitored signals deviate from their normal values, which may lead to serious consequences gradually. Due to the obscure early stage symptoms, incipient faults are difficult to detect. This article develops a stacked generalization (stacking)-based incipient fault diagnosis scheme for the traction system of high-speed trains. To extract the fault feature from the faulty data signals, which are similar to the normal ones, the extreme gradient boosting (XGBoost), random forest (RF), extra trees (ET), and light gradient boosting machine (LightGBM) are chosen as the base estimators in the first layer of the stacking. Then, the logistic regression (LR) is taken as the meta estimator in the second layer to integrate the results from the base estimators for fault classification. Thanks to the generalization ability of stacking, the incipient fault diagnosis performance of the proposed stacking-based method is better than that of the single model (XGBoost, RF, ET, and LightGBM), although they can be used to detect the incipient faults, separately. Moreover, to find out the optimal hyperparameters of the base estimators, a swarm intelligent optimization algorithm, pigeon-inspired optimization (PIO), is employed. The proposed method is tested on a semiphysical platform of the CRH2 traction system in CRRC Zhuzhou Locomotive Company Ltd. The results show that the fault diagnosis rate of the proposed scheme is over 96%.

This article investigates the problem of fixed-time event-triggered output consensus tracking for high-order multiagent systems (MASs) under directed interaction graphs. First, a fixed-time event-triggered distributed observer and triggering functions are proposed. Next, fixed-time convergence of the presented distributed observer is proved by the Lyapunov function approach, and an analysis is conducted to show the proposed distributed observer excludes zeno behavior. Then, an event-triggered adaptive dynamic surface fixed-time controller is designed to stabilize the tracking error system. Finally, simulation results are given to show the effectiveness and superiority of the consensus scheme developed. The contribution of this article is to present a novel event-triggered fixed-time distributed observer and a novel fixed-time controller, which can reduce frequency of communication and control update, avoid continuous monitor, exclude zeno behavior, eliminate the effect of mismatched disturbance caused by observation error, and achieve practical fixed-time output consensus tracking of high-order MAS under directed interaction graphs.

This article investigates the model predictive control (MPC) for discrete-time Markov jump systems (MJSs). First, the asynchronization between the modes of the controller and those of the plant is studied. An asynchronous MPC controller is designed to tackle this issue. Next, to reduce the computational cost and communication burden, a version of the dynamic event-triggered mechanism (ETM) is presented. Finally, the exogenous disturbances are considered and the notion of mean-square input-to-state stability (ISS) is taken into account in the controller design. The highlight of this article is the introduction of both dynamic ETM and asynchronous control into the MPC design. The control algorithm is developed and formulated as a convex optimization problem. Moreover, the recursive feasibility and the closed-loop mean-square ISS are both studied. Finally, some simulations are given to show the effectiveness of the derived MPC method.

This article studies the problem of event-triggered fault detection (FD) for 2-D systems subjected to amplitude-bounded exogenous disturbance and measurement noise via a zonotopic residual evaluation mechanism. An event-triggered mechanism is introduced into the FD framework to save limited communication resources. A finite-frequency (FF) mixed$\ell _{\infty }/h_{\infty }$index is derived to ensure the residual signal is sensitive to a fault signal while robust to disturbance and noise, based on which an optimal mixed$\ell _{\infty }/h_{\infty }$FD filter design criterion is provided. Instead of constant thresholds, novel zonotope-based dynamic thresholds are utilized for residual evaluation. Finally, simulation results are presented to illustrate the effectiveness of the developed mechanism.

The fault detection (FD) problem for systems with both model uncertainty and external disturbance is investigated in this article. First, the mathematical models of systems with model uncertainty and disturbance, systems with additive faults, and systems with multiplicative faults are established with both left and right coprime factorization. Then, an observer-based FD scheme is proposed and the FD thresholds are derived for both open-loop and closed-loop manners. The necessary conditions on multiplicative FD are obtained and the fault detectability analyses are carried out with the aid of the gap metric technique. Finally, the effectiveness of the proposed method is illustrated by a case study on a cart dynamic system.

This article considers the problem of finite-time (FT) tracking control for a class of uncertain multi-input-multioutput (MIMO) nonlinear systems with input backlash. A modified FT command filter is designed in each step of backstepping, which ensures the output of the filter can faster approximate the derivatives of virtual signals, suppress chattering, and relax the input signal limit of the Levant differentiator. Then, the corresponding improved FT error compensation mechanism is adopted to reduce the negative impact of filtering errors. Furthermore, a neural-network-adaptive technology is proposed for MIMO systems with input backlash via FT convergence. It is shown that desired tracking performance can be implemented in finite time. The simulation example is presented to illustrate the effectiveness and advantages of the new design method.

In this article, we investigate the problems of robust exponential stabilization in mean square and$\mathcal {H}_{\infty }$-based integral sliding mode controller design for uncertain stochastic Takagi–Sugeno (T-S) fuzzy switched time-delay systems with both matched and unmatched uncertainties under synchronous switching and asynchronous switching, respectively. In these systems, the control input is dependent on a switching signal. Under synchronous switching and asynchronous switching, sufficient conditions are developed to guarantee that the resulting closed-loop systems are robustly exponentially stable in mean square with a prescribed$\mathcal {H}_{\infty }$-performance, respectively. The integral sliding mode surfaces and the sliding mode controllers are designed for the underlying systems under synchronous switching and asynchronous switching, respectively. By two examples stemming from the mass–spring–damper model and the single-link robot arm model under stochastic perturbation, the effectiveness of the results obtained is verified.

This article considers the problem of finite-time adaptive fuzzy prescribed performance control (PPC) via output-feedback for nonstrict-feedback nonlinear systems. The fuzzy state observer is designed to estimate the unmeasured system states. To rapidly approximate the derivative of virtual signal, a novel finite-time command filter is proposed. The fractional power error compensation mechanism is established to remove filtered error. By integrating the PPC and command filter technique into backstepping recursive design, a finite-time adaptive output-feedback controller is constructed, and the stability of closed-loop system is strictly proved. The designed control strategy shows that the closed-loop system is practical finite-time stable, and the output tracking error converges to a residual set within prescribed performance bound in finite time. Finally, a numerical comparison and practical examples are provided to demonstrate the validity of the developed finite-time control algorithm.

In this article, a new robust optimization method is proposed to simultaneously optimize the expectation and variability of system performance with parametric uncertainties and fuzzy variables. The expectation-entropy model is presented to characterize the fuzzy robust optimization problem as an equivalent biobjective optimization problem. An approximate mapping method is developed to calculate the response of fuzzy variables, which improves the computational efficiency of objective functions. Then, according to the decision makers’ preference for objectives, the optimization framework based on Stackelberg game is established. A leader–follower state transition algorithm is designed to search for the equilibrium solutions. Two practical case studies are provided to show the effectiveness of the new optimization approach in both subjective judgment and objective assessment.

In order to ease the online computational burden of fuzzy model-predictive control for the Takagi–Sugeno model with bounded disturbance, a lookup table containing the possible mappings from the state to the input is usually constructed offline so that the online computational burden is reduced to searching in this lookup table. However, with the increase in the problem size, the computational burden of the online search in the lookup table can be large enough to influence the real-time implementation. In this article, we propose a novel offline approach to solve this problem, where the control law is online searched in a receding horizon cache, which is only a small portion of the lookup table. The cache is refreshed in a one-step-ahead fashion to guarantee that the proper one-step-ahead state-to-input mapping can be found in the cache. The recursive feasibility and stability hold. The effectiveness and the efficiency of the proposed approach are verified through two examples.

This article addresses the problem of dynamic hybrid-triggered control for nonlinear networked control systems. A novel resilient control method is presented to improve network resource utilization and system performance against cyberattacks for the underlying systems. First, a fuzzy-model-based system is established to describe the nonlinear networked systems subject to multiple cyberattacks, external disturbances, parameter uncertainties, and network-induced delays. Then, a resilient dynamic hybrid-triggered scheme is proposed to conserve the network bandwidth and reduce the effect of cyberattacks. A new codesign method of the observer-based fuzzy controller and the resilient dynamic hybrid-triggered scheme is developed. Sufficient conditions in terms of linear matrix inequalities are given such that the closed-loop system is global exponential stable with an$H_\infty$disturbances attenuation level. Finally, an example of lateral control in autonomous driving is given to verify the effectiveness of the developed control method.

In this article, the problem of observer-based sliding-mode control for dynamic fuzzy systems with bounded external disturbances is investigated. First, the sufficient conditions for codesign of an observer and a sliding-mode control law are presented, such that the closed-loop system is asymptotically stable and affords satisfactory dissipative performance. The event-triggering mechanisms are also applied in the observer-based controller design. Then, an integral sliding-mode surface and an observer-based sliding-mode controller are designed such that the system trajectories are steadily maintained on the sliding surfaces. Finally, a numerical example is given to demonstrate the effectiveness of the developed new design method.

In this article, we study the problems of bipartite and cooperative consensus with a strictly dissipative performance for fuzzy multiagent systems (MASs) in a unified framework. First, we prove that bipartite consensus over a structurally balanced signed graph is equivalent to cooperative consensus over the corresponding unsigned graph by leveraging the gauge transformation for a class of nonlinear MASs. Then, a polynomial fuzzy model is constructed to describe the nonlinear MAS formed by one leader and followers. For mitigating communication and computational load, a mode-dependent event-triggered transmission strategy is proposed. By establishing the switching topologies through Markovian process, a new sampled-data event-triggered consensus protocol is designed. With a mode-dependent Lyapunov–Krasovskii function, a novel relaxed dissipative criterion is obtained. The criterion guarantees that all agents can achieve both event-triggered cooperative consensus and event-triggered bipartite consensus with the same magnitude but opposite signs for MASs over structurally balanced signed directed graphs and Markovian switching topologies. Moreover, the event-triggered parameters and consensus control gains can be numerically solved via the sum-of-squares method. Simulation results are given to show the effectiveness of the proposed design method.

In this article, the issue of multicenter and single-area humanitarian relief allocation with uncertain travel time and imprecise transportation information is investigated. Expert human knowledge using fuzzy control is employed to select rescue paths, and the considered problem is formulated as a fuzzy chance-constrained model to ensure that the allocated goods can be delivered to the disaster area on time within a desired probability. A new method is presented to transform chance-constrained programming into a mixed-integer model utilizing triangle fuzzy numbers and the robust optimization problem. A practical example is used to test the validity of the theoretic results obtained.

This article addresses the event-triggered tracking control and filtering problem for Takagi–Sugeno fuzzy-approximation-based discrete-time nonlinear networked systems subject to the effect of denial-of-service attacks. First, the unreliability of the communication channel between the sensor and the actuator/filter is considered, and the packet loss caused by denial-of-service attacks is characterized by the binary Markov chain. Second, two novel resilient adaptive event-triggered mechanisms are proposed to resist the impact of denial-of-service attacks and discard unnecessary data packets, in which the dynamic threshold variable is designed to adjust the event-triggered condition adaptively. A problem caused by the event-triggered mechanism is that the controller or filter cannot receive the system mode signal during the trigger interval. To surmount this problem, this article designs an estimator to compensate for the unavailable system mode. Then, an adaptive event-triggered controller or filter related to the estimated mode is designed to track the desired signal. Under this framework, by constructing a membership-function-dependent Lyapunov function, the conservativeness of the stability criterion is relaxed. Finally, two examples are used to validate the applicability of the proposed approaches.

This article focuses on the sampled-data synchronization issue for a class of complex dynamical networks (CDNs) subject to noisy sampling intervals and successive packet losses. The sampling intervals are subject to noisy perturbations, and categorical distribution is used to characterize the sampling errors of noisy sampling intervals. By means of the input delay approach, the CDN under consideration is first converted into a delay system with delayed input subject to dual randomness and probability distribution characteristic. To verify the probability distribution characteristic of the delayed input, a novel characterization method is proposed, which is not the same as that of some existing literature. Based on this, a unified framework is then established. By recurring to the techniques of stochastic analysis, a probability-distribution-dependent controller is designed to guarantee the mean-square exponential synchronization of the error dynamical network. Subsequently, a special model is considered where only the lower and upper bounds of delayed input are utilized. Finally, to verify the analysis results and testify the effectiveness and superiority of the designed synchronization algorithm, a numerical example and an example using Chua's circuit are given.

This article focuses on the investigation of finite-time dissipative state estimation for Markov jump neural networks. First, in view of the subsistent phenomenon that the state estimator cannot capture the system modes synchronously, the hidden Markov model with partly unknown probabilities is introduced in this article to describe such asynchronization constraint. For the upper limit of network bandwidth and computing resources, a novel dynamic event-triggered transmission mechanism, whose threshold parameter is constructed as an adjustable diagonal matrix, is set between the estimator and the original system to avoid data collision and save energy. Then, with the assistance of Lyapunov techniques, an event-based asynchronous state estimator is designed to ensure that the resulting system is finite-time bounded with a prescribed dissipation performance index. Ultimately, the effectiveness of the proposed estimator design approach combining with a dynamic event-triggered transmission mechanism is demonstrated by a numerical example.

In this brief, the problem of synchronization control is investigated for a class of fractional-order chaotic systems with unknown dynamics and disturbance. The controller is constructed using neural approximation and disturbance estimation where the system uncertainty is modeled by neural network (NN) and the time-varying disturbance is handled using disturbance observer (DOB). To evaluate the estimation performance quantitatively, the serial-parallel estimation model is constructed based on the compound uncertainty estimation derived from NN and DOB. Then, the prediction error is constructed and employed to design the composite fractional-order updating law. The boundedness of the system signals is analyzed. The simulation results show that the proposed new design scheme can achieve higher synchronization accuracy and better estimation performance.

The tracking control is investigated for a class of uncertain strict-feedback systems with robust design and learning systems. Using the switching mechanism, the states will be driven back by the robust design when they run out of the region of adaptive control. The adaptive design is working to achieve precise adaptation and higher tracking precision in the neural working domain, while the finite-time robust design is developed to make the system stable outside. To achieve good tracking performance, the novel prediction error-based adaptive law is constructed by considering the estimation performance. Furthermore, the output constraint is achieved by imbedding the barrier Lyapunov function-based design. The finite-time convergence and the uniformly ultimate boundedness of the system signal can be guaranteed. Simulation studies show that the proposed approach presents robustness and adaptation to system uncertainty.

This article addresses the output feedback control of micromechanical (MEMS) gyroscopes using neural networks (NNs) and disturbance observer (DOB). For the unmeasured system states, the state observer and the high gain observer are constructed. The adaptive NNs are investigated to approximate the nonlinear dynamics, including the known nominal terms and the system uncertainties caused by environmental fluctuations. For the time-varying disturbances, the DOB is utilized. The sliding mode control is employed to enhance the robustness. Through simulation verification, the output feedback control using NNs and DOB can adapt to the dynamics of MEMS gyroscope with unmeasured system speed, while an expected effective tracking performance is obtained in the presence of unknown system nonlinearities and external disturbances.

Cluster synchronization means that all multiagents are divided into different clusters according to the equations or roles of nodes in a complex network, and by designing an appropriate algorithm, each cluster can achieve synchronization to a certain value or an isolated node. However, the synchronization values between different clusters are different. With a feedback controller based on the calculation of the control input value and a trigger condition leading to the updating instants, this article introduces the trigger mechanism and designs a new data sampling strategy to achieve cluster synchronization of the coupled neural networks (CNNs), which reduces the number of updates of the controller, thereby reducing unnecessary waste of limited resources. In addition, an example proposes a synchronization algorithm and gives iterative procedures to calculate the trigger instants and prove the validity of the theoretical results.

In this article, the problem of stability is investigated for a class of hybrid time-delay systems with double degrees. The systems consist of both discrete and continuous subsystems that have homogeneity and nonzero degrees. Conditions are derived to guarantee the preasymptotic stability of the systems. The relationships of solutions are established between the systems with different sizes using homogeneous properties. Then, equivalent conditions are presented for the analysis of preasymptotic stability. Furthermore, necessary and sufficient delay-independent conditions are developed to analyze the stability of the systems. Finally, two examples are provided to illustrate the effectiveness of the proposed new stability analysis methods.

In this article, an event-triggered control scheme is proposed for a group of elliptical agents to achieve a predefined formation. The agents are assumed to have the same dynamics, and the control law for each agent is only updated at its event sequence based on its own minimum collision time and deviation time. The probability-driven controller is designed to prevent the stuck problem among agents. Mapping-adaptive strategy and angle-adaptive scheme based on the minimum collision distance are also developed. Two examples are presented to analyze and demonstrate the effectiveness and potential of the new event-triggered adaptive control algorithm.

This article is devoted to the investigation of reduced-order dissipative filtering for Takagi–Sugeno (T–S) fuzzy Markov jump systems with the event-triggered mechanism. For the proposed event-triggered mechanism, its threshold parameter is constructed as a special diagonal matrix which can improve system performance by flexibly adjusting the matrix elements. Due to the impact of the sampling behaviors and the environmental disturbance, the asynchronization between the filter and the estimated system is considered in this article, which can be characterized by the hidden Markov model. Through handling the linear matrix inequalities (LMIs) with some slack matrices, event-triggered fuzzy filters are designed to guarantee the resulting system is stochastically stable and strictly dissipative. The proposed filter parameters are obtained by solving LMIs. Ultimately, both the effectiveness and advantages of the proposed reduced-order filter with the event-triggered mechanism are verified by a practical example.

Care robots have been developed to address the shortage of caregivers in hospitals and homes. However, providing care services considering abstract physiological desires (e.g., hunger, thirst, hot, and cold) rather than specific commands (e.g., serve a cup of water) has not been fully investigated. The key to such services that satisfy abstract requests, e.g., thirst, is to evaluate the available options (e.g., milk, tea, biscuit, and Coke) with the consideration of the care recipient’s preferences. In this study, we argue that both general taste (long-term care preferences) and sequential influence [short-term care preferences (STCPs)] should be considered for a robot to provide satisfying services. In this article, a long STCP model (LSTCPM) is introduced. The model can be initialized and updated incorporating the feedback from the care recipients. Then, a virtual agent-based simulation system is presented, which provides a way to evaluate and fine-tune the LSTCPM prior to its deployment to actual care robots. Finally, experiments were conducted in a real household domain using our care robot KUT-PCR to demonstrate the feasibility of the proposed approach.

This paper provides a systematic method to easily design robust, smooth and effective control laws of PD, PID and (PD2)-type for a marine surface vehicle (MSV). The proposed control laws allow one to force, with ideal or real actuators, a MSV with uncertain parameters and subject to disturbances to follow any navigation trajectory, having a bounded second or third derivative, with a tracking error norm as small as desired, a good transient phase, and feasible control signals. Some practical guidelines to easily design the proposed controllers are given. Finally, the provided control approach is illustrated and validated in cases of berthing a MSV in a busy port, accurately and in a minimum time, and maintaining a MSV in a predetermined position without using anchoring systems, also in presence of measurement noises.

The study focuses on the sliding mode output feedback control issues for Markovian jump systems via event-triggered scheme. First, combined the designed output feedback controller with sliding mode, the augmented closed-loop Markovian jump system dynamics are presented. Meanwhile, an event-triggered method is introduced to save the network resources. Second, the exponential stability of the closed-loop system is analyzed. Then, a singular value decomposition technique is utilized to synthesize the ${\mathcal{L}_2} - {\mathcal{L}_\infty }$ output feedback controller. Consequently, the controller gains expressions are derived. Moreover, the proposed sliding mode is proved to ensure the system to jump onto the sliding surface and maintain therein. Finally, simulation results are provided to show the proposed method is effective.

In interconnected industrial control networks like smart grids, topology attacks on physical grids can lead to severe cascading failures and large-scale blackouts. Effective defense on vulnerable devices can significantly reduce the risk of cascading failures and improve overall system robustness. In this paper, we investigate the vulnerability analysis problem from a graph theoretical classification perspective. By calculating a node vulnerability vector composed of features based on complex network theory, node embedding, extended betweenness and power flow distribution, we propose a node vulnerability analysis method based on XGBoost classifier. A cascading failure simulation model based on DC power flow is used to simulate the smart grid behaviours under topology attacks and create the dataset for the XGBoost classifier. The effectiveness of the proposed XGBoost-based method with newly-introduced features is demonstrated by case studies.

This paper investigates the decentralized event-triggered output feedback control for nonlinear networked interconnected systems under multiple cyber attacks, where denial of service and deception attacks are considered. A resilient period event-triggered strategy is adopted to reduce the occupation of network resources. Considering the impact of cyber attacks, the output-based decentralized security controller is constructed within a unified framework. Sufficient conditions of stochastically finite-time stable of the switched system are given by employing the Lyapunov-Krasovskii functional. Then, the controller gains and event-triggered weight matrix are designed simultaneously. Finally, a chemical reactor system is used to substantiate the effectiveness of the proposed method.

In this paper, the problem of formation control is studied for second-order multi-agent systems with practical issues like mismatched uncertainties and obstacle avoidance. A reference correction algorithm based sliding mode control scheme is proposed to ensure the boundedness of each agent's position tracking error. Both disturbance observer and artificial potential field are implemented to first tackle the problem of obstacle avoidance with the existence of mismatched uncertainties. The unreachable reference scenarios are then defined to describe the passive correcting behaviours of the multi-agent system when agents are trying to avoid obstacles. A distributed reference correction algorithm is developed for each agent to attenuate the harmful effects of passive correcting behaviours. The effectiveness of obstacle avoidance and boundedness of position tracking error are both verified by Lyapunov stability theory. Finally, multi-robot system based numerical simulations and comparisons are conducted to demonstrate the effectiveness of the proposed algorithm and controller.

This paper addresses the problem of simultaneous fault detection and control (SFDC) for Markov jump systems (MJSs) over sensor networks. In order to save the bandwidth of the network, round-Robin protocol is adopted for the communication between the sensors and fault detection filter. First, to fully utilize the data of sensor networks, a new distributed system is developed especially for SFDC, and a residual system, which integrates the MJSs with the distributed SFDC system, is also established. Then, by constructing a mode-dependent Lyapunov functional, the conditions for distributed SFDC are derived, which can make the residual system asymptotically mean-square stable and meanwhile satisfy the prescribed H∞performance requirements. Finally, comparison analyses are made by examples to illustrate the effectiveness and superiority of the proposed new design approach.

This paper studies the event-triggered robust model predictive control (MPC) problem for constrained linear systems subject to bounded disturbances. For control computations, explicit MPC reduces online optimizations to simple function evaluations, thus is suitable for resource constrained systems. We propose an event-triggering mechanism (ETM) for an explicit robust model predictive controller such that function evaluations are not needed at every time instant and the ETM itself requires comparably less online computations. In the proposed ETM, we design a contracting polytopic trigger set with respect to system constraints and the explicit robust control law. The resulting event-triggered control scheme ensures robust constraint satisfaction and robust stability for the closed-loop system while avoiding the Zeno-like behavior. Simulation results illustrate the validity of the event-triggered robust control scheme.

In this paper, a two-stage reconfiguration strategy is proposed for a group of agents to find their specially designated formation, which can be seen as a transition from the current states to the predefined formations. The specially designated formation occurs during the inter-task idle time in order to minimize the reconfiguration time. The basic specially designated formation is designed for dotted agents based on the optimal mapping relationships and current probabilities of the predefined formations. Then, the reconfiguration schemes are developed with the agents modeled as circles or ellipses to account for their physical shapes, and the problem of agent overlapping during the formation is addressed. Examples and simulation results are presented to analyze and demonstrate the effectiveness and potential of the new design techniques.

This paper presents a sliding mode dynamic output feedback controller design for Markovian jump systems under a communication network. For the inaccessible states and uncertain parts of Markovian jump systems, a novel integral-type sliding mode output feedback controller combined with system states and dynamic controller states is proposed. An event-triggered mechanism is introduced to reduce network bandwidth and resources requirements. The augmented dynamic system and complete control framework are presented together. The reachability of the novel integral-type sliding mode is proved. Then, based on delay-dependent Lyapunov functions, free-weighting matrices, and a singular value decomposition approach, a less conservative sufficient condition is proposed to ensure exponential stability with a weighted$\mathcal {L}_{2}$-$\mathcal{ L}_{\infty}$disturbance performance. Finally, a numerical and a DC-DC switched boost converter circuit simulations that verify the feasibility and effectiveness of the proposed controller are presented.

In this paper, the problem of control is investigated for cancer-tumor-immune systems, based on a two-dimension uncertain nonlinear model describing the interaction between immune and cancer cells in a body. First, the control problem is transformed into a state tracking problem. Second, an adaptive control method is proposed to track and stop the growth of cancer and maintain cancer and immune cells at an acceptable level. Different from the existing results in literature, the singularity problem in controller and the inaccuracy in control design have been overcome. From theoretical analysis, it is shown that the resulting closed-loop system is asymptotically stable and the tracking errors converge to the origin. Finally, simulation results illustrate not only the competitive relationship between immune system and tumor, but also the immune system has strong immunity to low level tumor volumes.

In this article, we investigate the problem of the dissipativity-based resilient sliding-mode control design of cyber-physical systems with the occurrence of denial-of-service (DoS) attacks. First, we analyze the physical layer operating without DoS attacks to ensure the input-to-state practical stability (ISpS). The upper bound of the sample-data rate in this situation can be identified synchronously. Next, for systems under DoS attacks, we present the following results: 1) combined with reasonable hypotheses of DoS attacks, the ISpS as well as dissipativity of the underlying system can be guaranteed; 2) the upper bound of the sample-data rate in the presence of DoS attacks can be derived; and 3) the sliding-mode controller is synthesized to achieve the desired goals in a finite time. Finally, two examples are given to illustrate the applicability of our theoretical derivation.

This article is concerned with fixed-time leader-follower consensus problem for multiagent systems (MASs) with output constraints, unknown control direction, unknown system dynamics, unknown external disturbance, and dead-zone control input. First, a fixed-time distributed observer is presented for each follower to estimate the leader's states. Next, using a modified nonlinear mapping, an output-constrained system is transformed into an unconstrained system. Then, by adopting adding a power integrator technique, radial basis function neural network (RBFNN) approximation, and adaptive method, the ideal fixed-time stable virtual control protocol is derived and the issues of unknown control direction, unknown system dynamics, and unknown external disturbance are addressed. Finally, the actual control protocol is developed using the bound of dead-zone parameters. It is shown that the proposed control scheme achieves fixed-time leader-follower consensus of the studied MAS. The presented control protocol is applied to the leader-follower consensus of inverted pendulums and simulation results verify its effectiveness.

In this article, a robust adaptive learning control strategy for uncertain single-input-single-output systems in strict-feedback form and controllability canonical form (CCF) is studied. For the strict-feedback system, the dynamic surface control is introduced while for the controllability canonical system, sliding-mode control is further constructed. The finite-time design is introduced for fast convergence. Under the switching mechanism, the intelligent design and the robust technique work together to obtain robust tracking performance. Once the states run out of the domain of intelligent control, the robust item will pull the states back while inside the neural working domain, the composite learning is developed to achieve higher approximation precision by building the prediction error for the weight update. The closed-loop system stability is analyzed via the Lyapunov approach. Especially for the CCF, the finite-time convergence is achieved while the system signals are globally uniformly ultimately bounded. Simulation studies on the general nonlinear systems and the flight dynamics show that the new design scheme obtains better tracking performance with higher precision and stronger robustness.

This article investigates the problem of resilient control for the Takagi–Sugeno (T–S) fuzzy systems against bounded cyberattack. A novel memory-based event triggering mechanism (ETM) is developed, by which the past information of the physical process through the window function is utilized. Using such an ETM cannot only lead to a lower data-releasing rate but also reduce the occurrence of wrong triggering event. Furthermore, the frequency of event generation is relatively smoother than existing ETMs. From the current releasing instant to the next, two periods are designed. The ETM works only when the first period ends, thereby avoiding the Zeno behavior that commonly exists in continuous ETM designs. The control system is then formulated as a switched fuzzy control system with two modes in each releasing period. Based on an assumption of secure control, and the proposed ETM, sufficient conditions are obtained to guarantee the exponential stability of networked T–S fuzzy systems in the presence of deception attacks in secure sense. Finally, a single-link rigid robot is taken as an example to illustrate the advantages of theoretical results.

In this article, the problem of robust optimization is considered for dynamical systems with both constraints and uncertainties. Conditions are established to ensure the existence of solutions to the problem with both robust optimality and feasibility. The objective performance with respect to fuzzy uncertainties is evaluated based on the expectation-entropy model. A feasibility robustness analysis method is proposed to handle the uncertainties in the constraints. Using the hierarchy structure in robust design, the optimization framework based on Stackelberg–Nash game is developed. A leader–followers state transition algorithm is designed to search for the equilibrium solution. Two application examples are given to demonstrate that the proposed robust optimization method can accurately evaluate the robustness performance and successfully search for a compromise solution.

This article investigates the problem of event-triggered-based sliding mode control (SMC) for a class of Takagi-Sugeno (T-S) fuzzy systems with actuator failures and signal quantization. The classical dynamic uniform quantization strategy is employed to quantize data in both sensor-controller side and controller-actuator side. A new event-triggered adaptive SMC scheme is proposed to stabilize the fault closed-loop systems, where the event-triggered condition is based on quantized state vectors. Reachability of the proposed sliding surface can be ensured by the designed control scheme. Furthermore, the existence of minimal inter-event time and sufficient conditions under which zeno behavior can be avoided is analyzed and presented. Finally, two examples are provided to demonstrate the effectiveness of the proposed new design techniques.

In this article, the distributed synchronization control problem is investigated for a class of uncertain nonlinear leader-following systems on directed graph, where unknown backlash-like hysteresis exists in the actuator of each follower and the topology of the graph is fixed. Based on basic graph theory, the principle of variable structure control and Lyapunov theory, by using the unknown smooth function approximation capability of type-2 fuzzy logic systems, a new distributed adaptive supervisory control design method is proposed such that all followers asymptotically synchronize to the leader and each synchronizing error is bounded. In the control method, with the help of supervisor control strategy, it is guaranteed that the resulting closed-loop signals including system states, respectively, belong to the corresponding compact sets, which is necessary for fuzzy approximation theory because it is on a compact set that an unknown smooth function can be approximated by fuzzy logic system. Furthermore, the adaptive compensation terms of the optimal approximation errors are adopted to reduce the effects of modeling errors. Finally, simulation results demonstrate the effectiveness of the proposed new design method.

In this article, the problem of dissipativity-based consensus is considered for polynomial fuzzy multiagent systems (MASs) with switching directed topologies. First, a novel polynomial fuzzy modeling approach is proposed to describe the error dynamic system which is formulated by a virtual leader and follower agents. Then, consensus control protocol is designed to guarantee that the MASs under switching topologies can reach an agreement. By the polynomial Lyapunov function technique, relaxed sufficient conditions are presented to ensure exponential consensus with a strictly dissipative performance for the underlying systems subject to external disturbances. The derived conditions are converted into sum of squares and can be numerically solved. Finally, an illustrative example is provided to demonstrate the effectiveness of the new design scheme developed.

This article addresses the problem of continuous-time dynamic sliding mode control for Takagi-Sugeno fuzzy nonlinear systems based on an event-triggered mechanism. First, a novel sliding mode dynamic system is established by introducing a corresponding sliding mode function and an efficient event-triggered mechanism. Then, based on a new Lyapunov function and the reciprocally convex method, sufficient conditions are presented to guarantee that the constructed network sliding mode dynamics is asymptotically stable. It is shown that the trajectories of the sliding mode dynamic system can converge into a bounded domain of a sliding mode surface in finite time. Furthermore, the explicit expression of the desired sliding mode controller is given in the form of linear matrix inequalities. Finally, the applicability and effectiveness of the proposed design techniques are demonstrated by a numerical example.

This article concerns the event-triggered fault detection filtering problem for nonlinear networked control systems. First, an event-triggered scheme with two trigger matrices is proposed by considering limited network resources; a fuzzy fault detection filter with a general structure is chosen as the residual generator. Then, we introduce a new event-based residual system by taking into consideration the interval time-varying delays. Sufficient conditions are obtained to guarantee that the event-based residual system is asymptotically stable and satisfies a prescribedH∞performance with the help of a reciprocally convex technique. Furthermore, some new relaxation matrices are introduced such that the corresponding solvability conditions can be established by means of a linearization procedure. Finally, examples are given to illustrate the effectiveness and advantages of the proposed new fault detection filtering techniques.

This article considers the decentralized stabilization problem of stochastic interconnected systems. The considered stochastic systems simultaneously contain unknown interconnections, power integrator dynamics, and time-varying delays. First, both state and output feedback controllers are presented for the stochastic interconnected nonlinear systems. Novel barrier Lyapunov–Krasivskii functions are used for the stability analysis of the proposed method. Second, fuzzy systems are adopted to approximate the complex and unknown dynamics in the systems with guaranteed compact set conditions. Based on barrier functions and switching mechanism, it is strictly proved that all the states will always remain in a compact set. Third, an adaptive compensator is presented to handle the resulting errors from input quantization, which finally renders asymptotic stability of the whole interconnected systems. Simulations are conducted to verify the main results.

Efficient task assignments can significantly improve agent management and reduce communication load and energy consumption. This article investigates the cooperative control problem for multiagent systems with an active task assignment strategy, in which whether an agent exchanges the information with neighbors depends on a perceived mission. By defining a set of missions, a task assignment mechanism for cooperative control problem is first proposed, in which the tasks will be scheduled by intermittent communication signals associated with the actual optimization requirements. By allowing appropriate task assignment conditions, a class of tracking cooperative control protocol is designed and accordingly, the stability of the closed-loop systems under the intermittent communication will be guaranteed. We also consider a case that the communication links between the followers and the leader can be optimized. To maximize the quality of information interaction, a leadership competition mechanism is introduced to design the tracking cooperative control protocol. As an application, cooperative surveillance using a group of rotary-wing air vehicles is considered. Numerical simulation demonstrates the effectiveness of the proposed approaches.

Filter design for nonlinear systems, especially time delayed nonlinear systems, has always been an important and challenging problem. This brief investigates the filter design problem of nonlinear systems with multiple constraints: time delay, actuator, and sensor faults, and a new adaptive neural network-based filter design method is proposed. Comparing with the existing works where there is a shortcoming that the designed filters contain unknown time delay(s), the design method proposed in this brief overcomes the shortcoming and only the estimation of the unknown time delay exists in the filter. Furthermore, not only the system states can be estimated, but also the unknown time delay with actuator and sensor faults can be estimated in this brief. Finally, simulation results are given to show the effectiveness of the proposed new design method.

This article studies the adaptive neural controller design for a class of uncertain multiagent systems described by ordinary differential equations (ODEs) and beams. Three kinds of agent models are considered in this study, i.e., beams, nonlinear ODEs, and coupled ODE and beams. Both beams and ODEs contain completely unknown nonlinearities. Moreover, the control signals are assumed to suffer from a class of generalized backlash nonlinearities. First, neural networks (NNs) are adopted to approximate the completely unknown nonlinearities. New barrier Lyapunov functions are constructed to guarantee the compact set conditions of the NNs. Second, new adaptive neural proportional integral (PI)-type controllers are proposed for the networked ODEs and beams. The parameters of the PI controllers are adaptively tuned by NNs, which can make the system output remain in a prescribed time-varying constraint. Two illustrative examples are presented to demonstrate the advantages of the obtained results.

In this paper, the problem of sliding mode control (SMC) is considered for a class of nonlinear continuous-time Markov jump systems (MJSs) with uncertainties and time delay. A novel integral-type switching sliding surface function is designed, where the controller gain may jump asynchronously with original MJSs. Then, an SMC law is constructed to force system trajectories onto the specified switching sliding surface in a finite time. The stochastic stability and dissipative performance of sliding mode dynamics are analyzed and the delay-dependent sufficient condition for the existence of the desired switching surface is developed. Moreover, we also extend SMC to investigate the finite-time stability problem during both reaching phase and sliding motion phase in the stochastic setting. Finally, simulation results are given to illustrate the effectiveness of the proposed design techniques.

In practice, many dynamical systems can be modeled by multiagent systems (MASs) with different operating modes. The consensus problem of leader-follower MASs with semi-Markovian jump parameters is investigated in this article. First, to save the bandwidth resources of the MASs, adaptive event-triggered communication and double quantization are introduced to design the consensus protocol, in which the event-triggered threshold is dynamically adjusted instead of being a constant, and not only the data from the agents to the consensus controller but also that from the controller to the agents, are both quantized. Next, a sufficient condition is established to ensure the stability of the consensus error system under the adaptive event-triggered condition, and then the consensus controller is developed by matrix inequality approach. Finally, a numeral example is given to demonstrate the effectiveness of the developed consensus controller.

This paper is concerned with the problem of finite-timeH∞synchronization for a class of discrete-time switched neural networks with quantized feedback controllers. First, we take advantage of mode-dependent average dwell time approach and semi-time-dependent (STD) Lyapunov function method to establish new criteria for the finite-time synchronization. Based on the newly developed criteria, a finite-time bounded real lemma is presented and further, the quantized state feedback controller is designed to ensure the finite-timeH∞synchronization of the underlying neural networks. The conservatism is further reduced by the proposed mode-dependent and STD controller. Finally, we use two illustrative examples to show the effectiveness and superiority of the results obtained in this paper.

In this paper, we discuss the modeling and stabilization problems for a class of discrete-time stochastic networked systems (DSNSs) subject to successive packet losses. The system under consideration is transformed into a stochastic one with bounded stochastic delay. Considering that the stochastic delay is characterized by nonuniform distribution, a new equivalent model is then constructed that enables the DSNS's controller design to benefit from knowing the probability characteristic of packet losses. Specially, to verify this feature, the probabilities of the delay can be explicitly obtained by utilizing the formula of total probability, which is critical to model transformation and analyze practical problems that exist in DSNSs. Based on the proposed model, sufficient conditions are established under which the globally mean-square asymptotic stability of resulting stochastic delay closed-loop system is guaranteed, and a delay-distribution-dependent design procedure is then proposed. A numerical example with simulation is provided to validate the analytical results and demonstrate the effectiveness of the design procedure.

Event-triggered schemes are characterized in less communication traffic while maintaining the resulting controlled plant's desired stability and performance criteria. In the presence of packet dropouts, this paper is concerned with the modeling and control problems for a class of discrete-time stochastic systems with event-triggered schemes. Two different mathematical analysis methods are proposed to model the packet loss when an event-triggered scheme is subject to packet loss. First, a stochastic distributed sequence satisfying the Bernoulli process is utilized to model the triggered packets transmitted in the communication networks. Considering the difference between time-triggered and event-triggered schemes, an equivalent model with a random sequence not satisfying a Bernoulli distribution process is also analyzed, which is not the same as some existing results in the literature. Then, the mean-square exponential stability of resulting augmented system is guaranteed and the prescribed H∞performance level is achieved by solving resulting discrete P-problem. Two examples with simulations are provided to validate the analytical results and demonstrate the effectiveness of the proposed co-design techniques.

This article is concerned with the problem of distributed model predictive control (DMPC) for second-order multiagent systems under event-triggered technique and logarithm quantized communication for a directed topological graph. Considering the limitation of communication bandwidth, a new bounded logarithm quantized communication strategy is proposed to preprocess the information before its transmission, thus reducing the influence of quantization error on the final convergence state. In order to decrease the frequency of control law update and reduce the power consumption, a distributed event-triggered rule is designed to decide when to transmit the information and when to optimize the model predictive control, in which trigger function synthesizes three factors, namely, predictive step, saturation of quantizer, and event-triggered error related with quantized error. The optimal control sequence of DMPC guides the update of controller between two triggering instants. The relationship among the quantization level, event-triggered parameters, and Laplacian matrix is established. Conditions are presented to ensure that all leaders asymptotically converge to a designed formation configuration, while all followers reach to the convex hull of them. Finally, an example is given to illustrate the effectiveness of the proposed methods.

In this article, the problem of filtering is studied for a class of nonlinear systems subject to time delays. The dynamics of nonlinear systems are characterized by Takagi–Sugeno (T–S) affine-fuzzy models. First, an extended bounded real lemma is established. In the process of analysis, a novel membership-dependent Lyapunov–Krasovskii functional is constructed, contributing to reducing the conservatism of the obtained results. Then, a fuzzy filter is designed through a linearization procedure such that the filtering error system is stable and satisfies a specified$H_{\infty }$performance level. The research results further deepen and enrich the theory of fuzzy filtering, providing the theoretical basis and the technical support for real applications. Finally, examples are provided to verify the effectiveness of the developed new design methods.

This paper addresses adaptive neural output feedback control for uncertain nonlinear switched systems. The main difficulty for control design comes from the loss of the precise information on those virtual coefficients of each subsystem. To overcome this difficulty, we give a robust observer design scheme by using convex combination approach. Furthermore, develop an observer-based output feedback control strategy. During the procedure of control design, adaptive neural control approach is used to deal with the unknown nonlinear functions and backstepping technique is employed to construct the ideal control laws. It is shown that the presented control law achieves the control issue of getting small tracking error, meanwhile, ensuring boundedness of all the closed-loop signals. Finally, a simulation example is used to test our results.

This article proposes a global adaptive neural network tracking control method for uncertain strict-feedback nonlinear system with output constraint and dead zone to achieve predefined-time convergence of the tracking error to predefined accuracy. First, an integral-type Barrier Lyapunov function (BLF) is constructed to handle output constraint. Next, radial basis function neural network (RBFNN) control and robust control are used to tackle unknown nonlinear function. The continuous switching function is designed to switch RBFNN control to robust control when the arguments of the unknown function exceed the active region of neural network. Utilizing the property of radial basis function, we derive the upper bound of the term containing the unknown nonlinear function and design adaptive laws to determine the derived upper bound and robust control gain. Then, the predefined time virtual control inputs are obtained and their derivatives are estimated by finite time differentiator. Finally, we use the dead zone inverse technique to obtain the actual control input. Stability analysis shows the presented control scheme achieves global convergence of the errors to predefined accuracy within predefined time. The simulation results verify the effectiveness of the presented control scheme.

This paper considers fixed-time consensus tracking of second-order multiagent systems (MASs) under directed interaction topology. A novel distributed observer is presented to estimate the leader's states within a fixed time, which overcomes the difficulties caused by the asymmetry of the Laplacian matrix. A sliding surface is designed and a nonsingular terminal sliding mode consensus protocol is developed to achieve fixed-time convergence of the tracking error to the origin. It is shown that each follower can track the leader's trajectory within a fixed time. Particularly, the gain of the presented consensus protocol is directly related to the prescribed time, which makes it convenient to determine and tune the gain according to the requirement of convergence time. Moreover, the presented control protocol reduces the conservativeness of the convergence time estimation for sliding motion. The simulation results validate the effectiveness of the proposed consensus scheme.

In practice, the dual constraints of limited interaction capabilities and system uncertainties make it difficult for large-scale multiagent systems (MASs) to achieve intelligent collaboration with incomplete local relative information. In this article, a review is conducted on the recent development of MASs intended for intelligent control, including consensus problem, formation control, and flocking control. Based on the limitations of the interaction level and the constraints of the individual system level, the published results on intelligent control are categorized into limited sensing-based control, event-based control, pinning-based control, resilient control, and collaborative control under system constraints. Also, the applications of intelligent control for MASs are presented, especially for robotics, complex networks, and transportation. Finally, a discussion is given about the challenges and future directions of research in this field.

In this paper, the problem of resilient observer-based robust control is considered for discrete-time Markov jump systems subject to asynchronous models and extended dissipativity. The model uncertainty is in the interval type, which has an ability to describe parameter fluctuating phenomenon more accurately than using the norm-bounded uncertainty. A hidden Markov chain is employed to depict the mismatch between the original system and the observer-based controller. Conditions are provided to ensure the stability of the resulting closed-loop system with a desired dissipation performance regardless of the uncertainties. An example is presented to illustrate the effectiveness and potential of the proposed new design techniques.

This paper studies the event-triggered containment control (CC) problem of multiagent systems with a directed graph, where the followers' communication graph is an undirected graph. A novel event-triggered CC protocol is presented to schedule communications between agents, which depends on both local states and control inputs. Different from traditional approaches using broadcasts, a unique property of the proposed protocol is that it enables agent-to-agent data transmission. To do so, each communication link is associated with a specific local event. The estimated relative interagent states are transmitted over a specific link only when the related input-based event is detected. We develop sufficient conditions to solve CC problem under this protocol and extend it to the adaptive event-triggered protocol that does not require the global knowledge on the smallest positive eigenvalue of the Laplacian. For both protocols, we derive positive low bounds on the interevent time intervals generated by individual events, which eliminate the “Zeno phenomenon.” Feasibility of the proposed algorithm is verified by an example.

In this paper, the exponential stability in mean square for Markovian jumping systems (MJSs) is discussed. A new dynamic model, which involves parameters uncertainties, nonlinearities, and Lévy noises, is proposed. Moreover, an adaptive sliding mode controller is built to study the stability of such a complex model. First, an integral-type sliding mode surface (SMS) is established to obtain the sliding mode motion dynamics of MJSs. By the generalized Itô formula and the Lyapunov stability theory, some sufficient conditions are obtained to make sure the exponential stability in mean square for the sliding mode motion dynamics. Second, an adaptive sliding mode control law is provided to assure the reachability of the specified SMS. Furthermore, corresponding parameters of the sliding mode controller and the SMS can be got by solving the convex optimization problem. Finally, the validity of the stability results obtained is illustrated by a numerical simulation and a practical simulation.

This paper deals with the dissipative control and filtering problems for the discrete-time two-dimensional (2-D) switched Fornasini-Marchesini (FM) systems. First, the definition of 2-D TSR- ϑ-dissipativity for the FM systems is introduced. By employing the switched quadratic Lyapunov function technique and establishing multiple recursive formulas, sufficient conditions are developed to guarantee that the considered system is asymptotically stable under the arbitrary switching signal (ASS) case and exponentially stable under the restricted switching signal (RSS) case with the 2-D TSR- ϑ-dissipativity, respectively. The special cases, such as the 2-D passivity performance and the 2-D H∞performance, are also discussed. Based on the obtained dissipative stability conditions, the 2-D TSR- ϑ-dissipative state feedback controller and filter design problems are further studied for the addressed system. Particularly, the extended average dwell time (define this later on in text) technique is utilized for the system under the RSS case. Finally, the effectiveness of the proposed design method is illustrated by numerical examples.

In this paper, the problem of adaptive finite-time attitude containment control for multiple spacecrafts with unknown external disturbances in spacecraft formation flying is investigated. A distributed control strategy combined with finite-time command filtered backstepping (FTCFB) and an adaptive technique is proposed, which can guarantee the containment errors of attitudes between leader spacecrafts and follower spacecrafts reaching the desired neighborhood in finite time. Moreover, the applied second-order sliding mode differentiator in FTCFB can make the output of the command filter fast approximate the derivative of the virtual signal at the second step of backstepping, which can further improve the control quality. A simulation example is given to show the effectiveness of the new designed technique presented.

Path planning and obstacle avoidance are essential for autonomous driving cars. On the base of a self‐constructed smart obstacle avoidance car, which used a LeTMC‐520 depth camera and Jetson controller, this paper established a map of an unknown indoor environment based on depth information via SLAM technology. The Dijkstra algorithm is used as the global path planning algorithm and the dynamic window approach (DWA) as its local path planning algorithm, which are applied to the smart car, enabling it to successfully avoid obstacles from the planned initial position and reach the designated position. The tests on the smart car prove that the system can complete the functions of environment map establishment, path planning and navigation, and obstacle avoidance.

In this paper, the problem of asynchronous output feedback control is investigated for a class of Takagi-Sugeno fuzzy switched systems subject to intermittent measurements. The Bernoulli process is employed to model the phenomenon of stochastic intermittent measurements. Based on the hidden Markov model and output measurements, an asynchronous controller is designed. Then, sufficient conditions for the existence of an asynchronous controller are proposed, which ensure the stochastic stability of the closed-loop system with desired extended dissipative performance. Finally, an example is presented to illustrate the effectiveness and advantages of the proposed new design techniques.

This paper proposes a robust adaptive algorithm that effectively copes with time-varying delay and uncertainties in Internet-based teleoperation systems. Time-delay induced by the communication network, as a major problem in teleoperation systems, along with uncertainties in modeling of robotic manipulators and remote environment warn the stability and performance of the system. A robust adaptive control algorithm is developed to deal with the system uncertainties and to provide a smooth estimation of delayed reference signals. The proposed control algorithm generates chattering-free torques which is one of the practical considerations for robotic applications. In addition, the achieved input-to-state stability gains do not necessarily require high gain control torques to retain the system's stability. Experimental simulation studies validate the effectiveness of the proposed control strategy on a teleoperation system consisting of a Phantom Omni Haptic device and SimMechanics model of the industrial manipulator UR10. The validation of the proposed control methodology was executed through a real-time Internet-based communication established over 4G mobile networks between Australia and Scotland.

This paper concentrates on the linear least mean square (LLMS) filtered and smoothed estimators for networked linear stochastic systems. Multiple packet losses, Markovian communication constraints, and superposed process noise are considered simultaneously. In order to reduce the channel load during communication, at every step, just one transmission node is permitted to send data packets. Hence, a Markovian communication protocol is utilized to arrange the packets of these transmission nodes. Moreover, multiple data packet dropouts occur during transmission due to an imperfect communication channel. Therefore, the global observation information cannot be obtained by the state estimator. The real state of Markov chain is assumed to be unknown to the estimator except the transition probability matrix. By means of the innovation analysis approach and orthogonal projection principle, we design Kalman-like estimators in a recursive form. Finally, through simulation experiments, we verify the effectiveness and superiority of the designed algorithm.

This paper is concerned with the distributed estimation problem in sensor networks subjected to unknown attacks. Network attacks are considered to exist in two classes of channels: 1) communication channels from the plant to sensors and 2) communication channels among sensors. The status of an attack is viewed as a stochastic phenomenon, and the transmitted information will be affected when the attacker successfully carries out an attack on the related data packet. Based on the sensors' own measurements and their neighbors' local information, a novel distributed estimation model against two-channel stochastic attacks is presented. A sufficient condition on the existence of the desired distributed H∞estimators is derived and the distributed estimator gains are designed by solving a linear matrix inequality. Two illustrative examples are provided to demonstrate the effectiveness of the new design techniques.

This paper is concerned with the problem of asynchronous and reliable filter design with performance constraint for nonlinear Markovian jump systems which are modeled as a kind of Takagi-Sugeno fuzzy switched systems. The nonstationary Markov chain is adopted to represent the asynchronous situation between the designed filter and the considered system. By using the mode-dependent Lyapunov function approach and the relaxation matrix technique, a sufficient condition is proposed to ensure the filtering error system, which is a dual randomly switched system, is stochastically stable and satisfies a given l2-l∞performance index simultaneously. Two different approaches are developed to construct the asynchronous and reliable filter. Owing to the Finsler's lemma, the second approach has fewer decision variables and less conservatism than the first one. Finally, two examples are provided to show the correctness and effectiveness of the proposed methods.

This paper studies the problem of event-triggered secure consensus for multiagent systems subject to periodic energy-limited denial-of-service (DoS) attacks, where DoS attacks usually prevent agent-to-agent data transmission. The DoS attacks are assumed to occur periodically based on the time-sequence way and the period of DoS attacks and the uniform lower bound of the communication areas are predetected by some devices. Based on the above assumptions, an event-based protocol consisting of two different measurements corresponding to leader-followers and follower-follower is presented to schedule communications between agents, which can reduce the update frequency of the controller. Then, the stability of the resultant error system is analyzed to derive sufficient conditions of achieving secure consensus by employing the Lyapunov function and the inductive approach. Besides, positive low bounds on any two consecutive intervals of events generated by individual events are calculated to eliminate "Zeno behavior" under the developed triggering condition and event-triggered protocol. Simulation result is provided to verify the theoretical analysis.

In order to deal with the speed cooperative control problem in the multiple linear induction traction systems consists of multiple linear induction motors, a model-free cooperative adaptive sliding-mode-constrained-control strategy is proposed considering the input magnitude and rate constraints which may cause the problem of actuator and integral saturation. First, the equivalent circuit topology of the single motor in the system is investigated. Besides, the system is considered as the multiagent system with fixed communication topology due to the interaction between adjacent motors. Then, the output observer is presented to estimate the output and the estimation algorithm of pseudo-partial derivative parameter and uncertainties is proposed. Based on the above, the proposed control scheme is presented by designing an integral sliding-mode surface containing the systematic error and an anti-windup compensator is added to eliminate the saturation. Finally, the simulations of the proposed control strategy for multiagent systems are carried out to demonstrate the effectiveness and superiority of the proposed control strategy.

In this paper, the tracking control problem is considered for a class of multiple-input multiple-output (MIMO) nonlinear systems with input saturation and unknown direction control gains. A command filtered adaptive neural networks (NNs) control method is presented with regard to the MIMO systems by designing the virtual controllers and error compensation signals. First, the command filtering is used to solve the "explosion of complexity" problem in the conventional backstepping design and the nonlinearities are approximated by NNs. Then, the error compensation signals are developed to conquer the shortcoming of the dynamic surface method. In addition, the Nussbaum-type functions are utilized to cope with the unknown direction control gains. The effectiveness of the proposed new design scheme is illustrated by simulation examples.

In this paper, asynchronous H∞filtering is studied for discrete-time switched Takagi-Sugeno (T-S) fuzzy systems. New Lyapunov functions are constructed for switched systems with asynchronous switching between subsystems and candidate filters. These improved Lyapunov functions are dependent on the mode of filters instead of subsystems, which are more consistent with the switching mechanism of switched systems with asynchronous switching. Meanwhile, they are quasi-time-dependent, which are effective in achieving more general results than traditional time-independent Lyapunov functions. Based on the new Lyapunov functions, a stability and ℓ2-gain criterion is deduced for switched T-S fuzzy systems. Then, switched fuzzy filters are designed to guarantee that the filtering error system is globally uniformly asymptotically stable and has a prescribed H∞performance. At last, an illustrative example is provided to demonstrate the potentials and advantages of the proposed filtering scheme.

This paper is focused on the static output feedback (SOF) control problem for a class of switched nonlinear systems with actuator faults. By means of the Takagi-Sugeno fuzzy model, the switched nonlinear plant is described by a family of switched fuzzy systems. Considering transmission failures may occur between controller and actuator, a reliable SOF controller against actuator faults is designed. Sufficient conditions are developed to guarantee the existence of the reliable SOF controller. Furthermore, an iterative algorithm is designed to determine the controller gains, which avoids the conservatism brought by the traditional singular value decomposition method. To validate the effectiveness of the proposed approach, a numerical example is exploited and simulation results are also presented.

With the aim to solve the problems related to the power distribution and current chattering in a distributed energy storage system (DESS), which can be considered as a multiagent system in dc microgrid, a model-free cooperative sliding mode control scheme with a directed-graph-based observer is proposed in this article. First, a state-of-charge (SoC)-based droop control strategy is combined with a directed-graph-based voltage regulation method to balance the SoCs of each energy storage unit (ESU), allocate the power, and maintain the bus voltage stability. Besides, a directed-graph-based observer is designed to estimate the battery current considering the communication between the ESUs, and based on this observer, an estimation algorithm for adaptive coefficients is developed. Then, the multiagent sliding mode control strategy with an antiwindup compensator of input constraint is developed to track the reference current with little chattering in the DESS, and the stability proof is given. Finally, simulations are conducted to validate the effectiveness and superiority of the newly designed control strategy for the DESS.

In this article, a new distributed learning control approach is proposed to address the cooperative fault-tolerant output regulation problem for linear multiagent systems with actuator faults. First, a distributed estimation algorithm with an online learning mechanism is presented to identify the system matrix of the exosystem and to estimate the state of the exosystem. In particular, an auxiliary variable is introduced in the distributed estimation algorithm to construct a data matrix, which is used to learn the system matrix of the exosystem for each subsystem. In addition, by resetting the state of the estimator and by using the identified matrix to update the estimator, all subsystems can reconstruct the state of the exosystem at an initial period of time, which is used for the neighbor subsystem to learn the system matrix of the exosystem. Based on the designed estimator, a novel distributed fault-tolerant controller is developed. Compared with the existing cooperative output regulation results, the system matrix of the exosystem considered in this article is unknown for all subsystems. Finally, a simulation example is provided to show the effectiveness of the obtained new design techniques.

The cooperative control problem of nonlinear multiagent systems is studied in this paper. The followers in the communication network are subject to unmodeled dynamics. A fully distributed neural-networks-based adaptive control strategy is designed to guarantee that all the followers are asymptotically synchronized to the leader, and the synchronization errors are within a prescribed level, where some global information, such as minimum and maximum singular value of graph adjacency matrix, is not necessarily to be known. Based on the Lyapunov stability theory and algebraic graph theory, the stability analysis of the resulting closed-loop system is provided. Finally, an numerical example illustrates the effectiveness and potential of the proposed new design techniques.

This paper is concerned with the set-membership estimation problem for complex networks subject to unknown but bounded attacks. Adversaries are assumed to exist in the nonsecure communication channels from the nodes to the estimators. The transmitted measurements may be modified by an attack function with added noise that is determined by the adversary but unknown to the estimators. A novel set-membership estimation model against unknown but bounded attacks is presented. Two sufficient conditions are derived to guarantee the existence of the set-membership estimators for the cases that the attack functions are linear and nonlinear, respectively. Two strategies for the design of the set-membership estimator gains are presented. The effectiveness of the proposed estimator design method is verified by two simulation examples.

This paper investigates the event-triggered dissipative filtering issue for discrete-time singular neural networks with time-varying delays and Markovian jump parameters. Via event-triggered communication technique, a singular jump neural network (SJNN) model of network-induced delays is first given, and sufficient criteria are then provided to guarantee that the resulting augmented SJNN is stochastically admissible and strictly stochastically dissipative (SASSD) with respect to (Xι, Yι, Zι, δ) by using slack matrix scheme. Furthermore, employing filter equivalent technique, codesigned filter gains, and event-triggered matrices are derived to make sure that the augmented SJNN model is SASSD with respect to (Xι, Yι, Zι, δ). An example is also given to illustrate the effectiveness of the proposed method.

In this paper, the consensus tracking problem is investigated for a class of continuous switched stochastic nonlinear multiagent systems with an event-triggered control strategy. For continuous stochastic multiagent systems via event-triggered protocols, it is rather difficult to avoid the Zeno behavior by the existing methods. Thus, we propose a new protocol design framework for the underlying systems. It is proven that follower agents can almost surely track the given leader signal with bounded errors and no agent exhibits the Zeno behavior by the given control scheme. Finally, two numerical examples are given to illustrate the effectiveness and advantages of the new design techniques.

In this brief, the practical finite-time consensus (FTC) problem is investigated for the second-order heterogeneous switched nonlinear multi-agent systems (MASs), where the subsystems and the switching signal for each agent are different. Mainly due to that agents' dynamics are switched and the unknown nonlinearities in the systems are more general, the practical FTC problem of the MASs is rather difficult to be solved by existing methods. As such, a new protocol design framework for the FTC problem is developed. Then, a novel adaptive protocol is proposed for the switched nonlinear MASs based on the developed design framework and the neural network method. The sufficient conditions for the practical FTC of nonlinear MASs under arbitrary switching are given. Finally, a numerical example is presented to demonstrate the effectiveness of the proposed control scheme.

In this paper, the problems of nonfragile and nonsynchronous synthesis for Bernoulli switched systems are studied. The disturbances in the systems are assumed to have bounded peak in the sense of mean square. With the interval additive gain variations consideration, a nonfragile and nonsynchronous controller is designed subject to a nonsynchronous assumption that the switching signals of the system are not accurately gained for synthesis. A sufficient condition that is dependent on the known sojourn probabilities of switching signals is proposed to ascertain the reachable set of the closed-loop system, which is a hidden Bernoulli model, mean square contained in an ellipsoid-like set. Several nonfragile and nonsynchronous controllers are parameterized to guarantee the corresponding requirements to be satisfied for the closed-loop systems. The effectiveness and advantages of the proposed new design techniques are illustrated by a numerical simulation example.

This paper considers the problems on the mean square exponential synchronization and the almost surely exponential synchronization for neutral stochastic dynamical delay networks with Markovian switching via the single pinning impulsive control. By using a unified approach incorporating the concept of average impulsive interval and Lyapunov function, and the Borel-Cantelli lemma, two criteria on the mean square exponential stability and the almost surely exponential stability for impulsive neutral stochastic delay systems with Markovian switching are derived. It is shown that the impulsive control with a suitable impulsive strength can stochastically stabilize the underlying systems. With the symmetry and irreducibility of the switching topologies, and the obtained stochastic stability criteria, sufficient conditions are derived to determine the mean square exponential synchronization of the underlaying networks via the single pinning impulsive controller, which are given with algebraic inequalities and M -matrix. The almost surely exponential synchronization is also analyzed. Two examples are given to verify the validity of the theoretic results presented.

This paper addresses the control problem of the switched system with bounded actuators and average dwell-time. We present the switching rules for the designed controllers based on two kinds of parametric algebraic Riccati equation. The exponential stability of the switched system under the average-dwell time is ensured by the designed controllers and the switching rules. The dynamic performance of the control system is enhanced greatly by the proposed method. The simulation results illustrate the effectiveness and the usefulness of the obtained theoretical results.

Many assignment problems cannot be solved to optimality in real-time. The existing literature tends to focus on the development of handcrafted heuristics that exploit the structure of a particular assignment problem. We instead seek a general-purpose approach that can automatically learn such heuristics. In this work, we present a deep learning approach that is designed to automatically learn heuristics for a large class of assignment problems. We demonstrate the effectiveness of our approach on the weapon-target assignment (WTA) problem, which is nonlinear and NP-complete. We take non-trivial WTA problem instances and show that our approach reliably finds near-optimal solutions in milliseconds.

This paper considers the problem of asynchronous guaranteed cost control (GCC) for nonlinear Markov jump systems with stochastic quantization. Hidden Markov model is used to describe the nonsynchronous controller and the random quantization phenomenon. Based on Takagi-Sugeno fuzzy technique and Lyapunov function approach, a sufficient condition is obtained, which can not only ensure the asymptotic stability of the closed-loop system and existence of the desired controller, but also can yield the minimal upper bound of GCC performance. Finally, two examples are provided to demonstrate the correctness and reliability of our developed approaches.

This paper focuses on the issue of designing an adaptive event-triggered scheme to the decentralized filtering for a class of networked nonlinear interconnected system. A novel adaptive event-triggered condition is proposed by constructing an adaptive law for the threshold. This new type of threshold mainly depends on the error between the states at the current sampling instant and the latest releasing instant, by which the data release rate is adapted to the variation of the system. The limitation of network bandwidth is alleviated on account of a large amount of "unnecessary" packets being dropped out before accessing the network. Sufficient conditions are derived such that the overall filtering error system under the proposed adaptive data-transmitting scheme is asymptotically stable with a prescribed disturbance attenuation level. An example is given to show the effectiveness of the proposed scheme.

In this paper, a new distributed fault estimation observer with adjustable parameters is designed for a class of nonlinear interconnected systems. The presented fault estimator consists of proportional and integral terms to improve the accuracy of fault estimation. The observer gain matrices of the proposed fault estimation scheme for the underlying systems are calculated based on robust${\mathcal {L}_{2}-\mathcal {L}_{2}}$and${\mathcal {L}_{2}-\mathcal {L}_\infty }$performance. The proposed method achieves a lower performance level in the aspect of quantitative analysis compared with existing fault estimation approaches. A simulation example is provided to demonstrate the effectiveness of the new design method.

This paper addresses the problem of quantized feedback control of nonlinear Markov jump systems (MJSs). The nonlinear plant is represented by a class of fuzzy MJSs with time-varying delay based on a Takagi-Sugeno fuzzy model. The quantized signal is utilized for control purpose and the sector bound approach is exploited to deal with quantization errors. By constructing a Lyapunov function which depends both on mode information and fuzzy basis functions, the reciprocally convex approach is used to derive the criterion which is able to ensure the stochastic stability with a predefined l2- l∞performance of the resulting closed-loop system. The design of the quantized feedback controller is then converted to a convex optimization problem, which can be handled through the linear matrix inequality technique. Finally, a simulation example is presented to verify the effectiveness and practicability of the proposed new design techniques.

This paper investigates the problem of robust H∞control for a class of nonlinear systems with state and input time-varying delays. The nonlinearity is presented by a continuous-time Takagi-Sugeno (T-S) fuzzy model with parameter uncertainties. A sufficient asymptotic stability condition is first proposed by constructing a delay-product-type augmented Lyapunov-Krasovskii functional and utilizing an extended reciprocally convex matrix inequality together with a Wirtinger-based integral inequality. Then, a state feedback controller is derived that guarantees the closed-loop fuzzy system being asymptotically stable with an H∞performance index. Finally, four numerical examples are given to reveal the effectiveness and merits of the developed new design techniques.

This paper investigates the problem of sliding mode observer design for a class of uncertain fuzzy time-delay systems based on an event-triggered strategy. The objective is to design an event-triggered mechanism by utilizing the information of system output and observer function. Based on the delay partitioning method and the Lyapunov-Krasovskii function approach, delaydependent sufficient conditions are proposed to guarantee the overall system, including sliding mode dynamics and error dynamics, to be asymptotically stable with an H∞ performance. Furthermore, an event-triggered sliding mode controller is synthesized to ensure that the system dynamics can be driven to the sliding region near the equilibrium point in finite time. Finally, a verification example is provided to demonstrate the feasibility and efficiency of the theoretical results presented.

This paper is concerned with the problem of generalized H2reduced-order filter design for continuous Takagi-Sugeno fuzzy systems using an event-triggered scheme. For a continuous Takagi-Sugeno fuzzy dynamic system, a reduced-order filter is designed to transform the original model into a linear lower order one. This filter can also approximate the original system with H2performance, with a new type of event-triggered scheme used to decrease the communication loads and computation resources within the network. By transforming the filtering problem to a convex optimization one, conditions are presented to design the fuzzy reduced-order filter. Finally, two illustrative examples are used to verify the feasibility and applicability of the proposed design scheme.

An intuitionistic fuzzy cognitive map (IFCM) is an extension of a fuzzy cognitive map (FCM) that forms a graph-oriented fuzzy map describing both causal relationships between pairs of concepts and the states of concepts via intuitionistic fuzzy sets (IFSs). In contrast with an FCM, an IFCM provides much more flexibility in system modeling. However, IFCMs may lead to confusing or unreasonable results in system modeling since they do not fully consider the negative influence from conventional operations on IFSs, the activation process of concepts, and the problem of aggregating knowledge with different importance levels. To solve the challenges of IFCMs, we propose a high-order IFCM based on evidential reasoning (ER) (IFCMR) theory in this study. First, we introduce an evidential intuitionistic fuzzy aggregation (EIFA) operator and a multiplication operation on IFSs using an ER theory. Second, we establish the theory of IFCMR based on the EIFA operator and the newly introduced multiplication operation on IFSs. Third, we propose a scheme of aggregating IFCMRs with different importance levels using the EIFA operator, which can also be utilized to aggregate conflict knowledge and to determine objective connections in terms of an evidential cognitive map (ECM). Finally, several numerical and practical examples are employed to test and verify the feasibility and validity of IFCMRs in comparison with both IFCMs and ECMs.

This paper presents a relaxed stabilization condition for polynomial-fuzzy-model-based control system by using sum-of-squares (SOS) approach. Full-block S-procedure is utilized to equivalently decouple the fuzzy weighting constraint into two constraints, which can be computed directly with the SOS approach. Besides, a free full-block multiplier imposed by this procedure also contributes to reducing the conservativeness of stability analysis. The copositivity verification relaxation performed on the membership-functions-related constraint can both obtain more relaxed results and reduce the computation complexity. The concept of polynomial Lyapunov function (PLF) with square matrical representation, which simplifies the stabilization condition by avoiding partial derivatives of a polynomial matrix, is also introduced to generalize the existing expression of PLFs. Then, the derived stabilization condition, which is represented in terms of SOS can be numerically solved. Three simulation examples are conducted to illustrate the effectiveness and applicability of the proposed method. These examples also demonstrate the advantages of the proposed method over the existing convex SOS approach.

This paper concentrates on the problem of event triggered adaptive leader-following control for a class of multiagent systems. The considered systems contain completely unknown interconnections and arbitrary asynchronously switching. To handle these nonlinearties, a new hierarchical barrier Lyapunov method is proposed. Based on this method, a novel adaptive fuzzy control strategy is designed to make the output tracking errors of the multiagent systems converge to a small neighborhood of origin. Stability analysis shows that the proposed method can guarantee the compact set conditions of the fuzzy logic systems during the entire design process. Meanwhile, to reduce the computational burden, novel event triggered mechanisms are presented for both the transmission between two connected agents, and the communication between the sensor and the controller in one agent. An illustrative example is presented to verify the effectiveness of the proposed controller.

In this paper, a robust position tracking controller is proposed using an invariant dynamic surface approach in a cascade control system structure. There are two main contributions. The first is to design a dynamic surface giving the desired tracking-performance with a time-varying cutoff frequency automatically adjusted by the proposed auto-tuner, which can enhance the reliability of the resulting control system. The second is to derive the inner- and outer-loop control laws driving the closed-loop trajectories to the dynamic surface, incorporating a disturbance observer with the use of only nominal plant parameters. A reduction of plant parameter dependence can be accomplished by the second contribution. The realistic simulation results confirm the advantages of the proposed technique.

This paper studies the problem of cluster synchronization at exponential rates in both the mean square and almost sure senses for neutral stochastic coupled neural networks with time-varying delay via a periodically intermittent pinning adaptive control strategy. The network topology can be symmetric or asymmetric, with each network node being described by neutral stochastic delayed neural networks. When considering the exponential stabilization in the mean square sense for neutral stochastic delay system, the delay integral inequality approach is used to circumvent the obstacle arising from the coexistence of random disturbance, neutral item, and time-varying delay. The almost surely exponential stabilization is also analyzed with the nonnegative semimartingale convergence theorem. Sufficient criteria on cluster synchronization at exponential rates in both the mean square and almost sure senses of the underlying networks under the designed control scheme are derived. The effectiveness of the obtained theoretical results is illustrated by two examples.

In this paper, a new robust Kalman filtering framework for a linear system with non-Gaussian heavy-tailed and/or skewed state and measurement noises is proposed through modeling one-step prediction and likelihood probability density functions as Gaussian scale mixture (GSM) distributions. The state vector, mixing parameters, scale matrices, and shape parameters are simultaneously inferred utilizing standard variational Bayesian approach. As the implementations of the proposed method, several solutions corresponding to some special GSM distributions are derived. The proposed robust Kalman filters are tested in a manoeuvring target tracking example. Simulation results show that the proposed robust Kalman filters have a better estimation accuracy and smaller biases compared to the existing state-of-the-art Kalman filters.

In this paper, an asynchronous filter is proposed for Markov jump neural networks (NNs) with time delay and quantized measurements where a logarithmic quantizer is employed. The filter and quantizer are both mode-dependent and their modes are asynchronous with that of the NN, which is described by hidden Markov models. By the Lyapunov-Krasovskii functional approach, a sufficient condition is derived and a filter is then designed such that the filtering error dynamics are stochastically mean square stable and strictly (U, L, V)-dissipative. Finally, the effectiveness and practicability of the theoretical results are verified by two examples, including a biological network.

This paper is concerned with the reliable ℒ2- ℒ∞filter design problem for the nonlinear continuous-time Markov jump systems based on Takagi-Sugeno fuzzy model. A stochastic variable is introduced to describe the encountered sensor failures, the value of which is dependent on the considered plant mode based on a hidden Markov process. In practice, generally the information on plant modes is not fully accessible to the reliable filter, which results in the nonsynchronous phenomena between the modes of involved plant and filter, and has a negative effect on the system performance. A hidden Markov model is also adopted to depict such kinds of nonsynchronous phenomena. The filtering error systems are called fuzzy dual hidden Markov jump systems. A sufficient condition, associated to the modes of the plant, sensor failures, and the filter are proposed for the filtering error systems to ensure the stochastic stability and guaranteed ℒ2- ℒ∞performance, based on which the existence condition and explicit design method of a nonsynchronous filter are both given. Finally, two simulation examples illustrate the effectiveness of the proposed approach.

This paper presents a reliable control strategy for quadrotors to achieve a satisfactory tracking performance and provide a stable yaw angle control in the presence of wind disturbances. Based on the prelinearizing transformation and singular perturbation techniques, a quadrotor flight system is decomposed into two interconnected subsystems in different time-scales: 1) the angular rotations and 2) the linear translations. For the fast subsystem running as the inner loop, finite frequency H∞ control is used to enhance robustness and provide adequate decoupling such that each translational dynamic is controlled independently. For the slow subsystem running as the outer loop, H∞ loop shaping control via proportion-integration-differentiation control is used to achieve a desired servo performance even in the presence of wind disturbance. Simulations are performed under different wind conditions to validate the effectiveness of the new control strategy.

This paper deals with the sampled-data stabilization problem for Takagi-Sugeno (T-S) fuzzy genetic regulatory networks with leakage delays. A novel Lyapunov-Krasovskii functional (LKF) is established by the non-uniform division of the delay intervals with triplex and quadruplex integral terms. Using such LKFs for constant and time-varying delay cases, new stability conditions are obtained in the T-S fuzzy framework. Based on this, a new condition for the sampled-data controller design is proposed using a linear matrix inequality representation. A numerical result is provided to show the effectiveness and potential of the developed design method.

In existing works, the filters designed for delayed genetic regulatory networks contain time delay. If the time delay is unknown, the filters do not work in practical applications. In order to overcome the shortcoming in such existing works, this paper investigates the filter design problem of genetic regulatory networks with unknown constant time delay, and a novel adaptive filter is introduced, which can estimate online not only unknown network parameters but also the unknown time delay. By the Lyapunove approach, it is shown that the estimating errors asymptotically converge to the origin. Finally, simulation results are presented to illustrate the effectiveness of the proposed new design method.

This paper investigates the gene circuit control design problem of time-delayed genetic regulatory networks. In the genetic regulatory networks, the time delays are unknown constants, and the genetic regulatory is not conventional SUM regulatory logic and can be modeled to be an unknown nonlinear function of the time-delayed states of the other genes in a cell. By Lyapunov stability, a novel adaptive gene circuit control design approach is proposed for the genetic regulatory networks, where the unknown time delays are estimated online by adaptive algorithms and the unknown regulatory functions are approximated by neural networks. The design approach in this paper is delay-dependent and has less conservatism than the delay-independent approach. From theoretical analysis, the closed-loop system is asymptotically stable and all the signals in the system converge to an adjustable neighborhood of the origin. Finally, a numerical example is given to show the effectiveness of the new design approach.

This paper addresses the problem of delay-dependent robust and reliable $\mathscr {H}_{\infty }$ static output feedback (SOF) control for a class of uncertain discrete-time Takagi-Sugeno fuzzy-affine (FA) systems with time-varying delay and actuator faults in a singular system framework. The Markov chain is employed to describe the actuator faults behaviors. In particular, by utilizing a system augmentation approach, the conventional closed-loop system is converted into a singular FA system. By constructing a piecewise-Markovian Lyapunov-Krasovskii functional, a new $\mathscr {H}_{\infty }$ performance analysis criterion is then presented, where a novel summation inequality and S-procedure are succeedingly employed. Subsequently, thanks to the special structure of the singular system formulation, the piecewise-affine SOF controller design is proposed via a convex program. Lastly, illustrative examples are given to show the efficacy and less conservatism of the presented approach.

In this paper, we investigate the synchronization for heterogeneous network subject to event-triggering communication. The designed controller for each node includes reference generator (RG) and regulator. The predicted value of relative information between intermittent communication can significantly reduce the transmitted information. Based on the event triggering strategy and time-dependent threshold, all RGs can exponentially track the target trajectory. Then by the action of regulator, each node synchronizes with its RG. Meanwhile, a positive lower bound is obtained for the interevent intervals. Numerical example is given to demonstrate the effectiveness of the proposed event triggering strategy.

This paper considers finite-time distributed state estimation for discrete-time nonlinear systems over sensor networks. The Round-Robin protocol is introduced to overcome the channel capacity constraint among sensor nodes, and the multiplicative noise is employed to model the channel fading. In order to improve the performance of the estimator under the situation, where the transmission resources are limited, fading channels with different stochastic properties are used in each round by allocating the resources. Sufficient conditions of the average stochastic finite-time boundedness and the average stochastic finite-time stability for the estimation error system are derived on the basis of the periodic system analysis method and Lyapunov approach, respectively. According to the linear matrix inequality approach, the estimator gains are designed. Finally, the effectiveness of the developed results are illustrated by a numerical example.

This paper deals with the problem of dissipativity-based asynchronous fault detection (FD) for Takagi-Sugeno fuzzy Markov jump systems with network data dropouts. It is assumed that data dropouts happen intermittently from the plant to the FD filter, which is described by Bernoulli process. The hidden Markov model is employed to describe the asynchronous phenomenon between the plant and filter. Based on Lyapunov theory, a sufficient condition is developed to guarantee that the FD system is stochastically stable with strictly dissipative performance. By choosing an appropriate Lyapunov function with the slack matrix technique and Finsler's Lemma, two approaches are proposed to compute filter gains by solving linear matrix inequalities. Finally, an example is provided to illustrate the usefulness and effectiveness of the proposed design methods.

This paper investigates the problem of the fault detection filter design for nonhomogeneous Markovian jump systems by a Takagi-Sugeno fuzzy approach. Attention is focused on the construction of a fault detection filter to ensure the estimation error dynamic stochastically stable, and the prescribed performance requirement can be satisfied. The designed fuzzy model-based fault detection filter can guarantee the sensitivity of the residual signal to faults and the robustness of the external disturbances. By using the cone complementarity linearization algorithm, the existence conditions for the design of fault detection filters are provided. Meanwhile, the error between the residual signal and the fault signal is made as small as possible. Finally, a practical application is given to illustrate the effectiveness of the proposed technique.

This paper focuses on the dissipativity-based asynchronous filtering problem for a class of discrete-time Takagi-Sugeno fuzzy Markov jump systems subject to randomly occurred quantization. Considering the random fluctuations of network conditions, the randomly occurred quantization is introduced to describe the quantization phenomenon appearing in a probabilistic way. To take full advantage of the partial information of system modes for the desired system performance, we adopt the asynchronous filter in which mode transition matrix is nonhomogeneous. The mode-dependent time-varying delays are introduced, which have different bounds for different system modes. Via fuzzy-mode-dependent Lyapunov functional approach that can reduce conservatism, a sufficient condition on the existence of the asynchronous filter is derived such that the filtering error system is stochastically stable and strictly (Q, S, R)-dissipative. Then, the gains of the filter are obtained by solving a set of linear matrix inequalities (LMIs). An example is utilized to illustrate the validity of the developed filter design technique where the relationships among optimal dissipative performance indices, delays, quantization parameter, and the degree of asynchronous jumps are given.

This paper is concerned with the synchronization of complex networks subject to partial couplings and Takagi-Sugeno (T-S) fuzzy nodes, by adopting the aperiodic sampled-data strategy. A decoupling method is utilized to handle the partial couplings among connected nodes, which enables us to investigate each channel of complex networks independently. On the basis of the input-delay approach, the hybrid system about each channel is reformulated to a continuous time-varying delay system. Then, the free-weighting matrix approach and a novel continuous Lyapunov functional are adopted to capture the information of sampling pattern. Sufficient conditions are obtained to ensure that the complex networks achieves synchronization with the target node. Furthermore, the proposed strategy is extended to more general case, in which there exist constant transmission time delays in the local interactions of connected nodes. Based on Wirtinger's inequality, a simplified and efficient synchronization strategy is proposed. Moreover, the corresponding optimization problem about the maximal sampling interval upper bound is addressed as well. Finally, the communication frequency reduction potential of the proposed synchronization strategy is well demonstrated via a numerical example.

Adaptive fuzzy control via command filtering is proposed for uncertain strict-feedback nonlinear systems with unknown nonsymmetric dead-zone input signals in this paper. The command filtering is utilized to cope with the inherent explosion of the complexity problem of the classical backstepping method, and the error compensation mechanism is introduced to overcome the drawback of the dynamics surface approach. In addition, by utilizing the bound information of dead-zone slopes, a new adaptive fuzzy method that does not need to establish the inverse of the dead zone is presented for the unknown nonlinear systems. Compared with existing results, the advantages of the developed scheme are that the compensating signals are designed to eliminate the filtering errors and only one adaptive parameter is required, which will make the proposed control scheme more effective for practical systems. An example of position tracking control for the electromechanical system is given to demonstrate the usefulness and potential of the new design scheme.

In copper removal process control, the commonly used technique is the so-called rule-based control, which is largely dependent upon the operators' experience, likely leading to unstable process production due to each individual's characters and favors. In this paper, to enhance the effectiveness of process control, a controllable-domain-based fuzzy rule extraction strategy is proposed. New definitions of representative controlled samples are introduced, by which the input variable space is divided into several controllable domains by applying positive and unlabeled learning algorithm. Also, the unreasonable removed and the controllable domains are accordingly determined. Then, support vector machine method is employed to extract fuzzy control rules for different domains. Finally, an industrial experiment is presented to demonstrate the effectiveness and advantages of the developed new design scheme.

This paper investigates the neural-network-based adaptive control problem for a class of continuous-time nonlinear systems with actuator faults and external disturbances. The model uncertainties in the system are not required to satisfy the norm-bounded assumption, and the exact information for components faults and external disturbance is totally unknown, which represents more general cases in practical systems. An indirect adaptive backstepping control strategy is proposed to cope with the stabilization problem, where the unknown nonlinearity is approximated by the adaptive neural-network scheme, and the loss of effectiveness of actuators faults and the norm bounds of exogenous disturbances are estimated via designed online adaptive updating laws. The developed adaptive backstepping control law can ensure the asymptotic stability of the fault closed-loop system despite of unknown nonlinear function, actuator faults, and disturbances. Finally, an application example based on spacecraft attitude regulation is provided to demonstrate the effectiveness and the potential of the developed new neural adaptive control approach.

This paper is concerned with the exponential synchronization issue of general chaotic neural networks subject to nonuniform sampling and control packet missing in the frame of the zero-input strategy. Based on this strategy, we make use of the switched system model to describe the synchronization error system. First, when the missing of control packet does not occur, an exponential stability criterion with less conservatism is established for the resultant synchronization error systems via a superior time-dependent Lyapunov functional and the convex optimization approach. The characteristics induced by nonuniform sampling can be used to the full because of the structure and property of the constructed Lyapunov functional, that is not necessary to be positive definite except sampling times. Then, a criterion is obtained to guarantee that the general chaotic neural networks are synchronous exponentially when the missing of control packet occurs by means of the average dwell-time technique. An explicit expression of the sampled-data static output feedback controller is also gained. Finally, the effectiveness of the proposed new design methods is shown via two examples.

The problem of dissipativity-based resilient filtering for discrete-time periodic Markov jump neural networks in the presence of quantized measurements is investigated in this paper. Due to the limited capacities of network medium, a logarithmic quantizer is applied to the underlying systems. Considering the fact that the filter is realized through a network, randomly occurring parameter uncertainties of the filter are modeled by two mode-dependent Bernoulli processes. By establishing the mode-dependent periodic Lyapunov function, sufficient conditions are given to ensure the stability and dissipativity of the filtering error system. The filter parameters are derived via solving a set of linear matrix inequalities. The merits and validity of the proposed design techniques are verified by a simulation example.

This paper focuses on the problem of adaptive output-constrained neural tracking control for uncertain nonstrict-feedback systems in the presence of unknown symmetric output dead zone and input saturation. A Nussbaum-type function-based dead-zone model is introduced such that the dynamic surface control approach can be used for controller design. The variable separation technique is employed to decompose the unknown function of entire states in each subsystem into a series of smooth functions. Radial basis function neural networks are utilized to approximate the unknown black-box functions derived from Young's inequality. With the help of auxiliary first-order filters, the dimensions of neural network input are reduced in each recursive design. A main advantage of the proposed method is that for an -order nonlinear system, only one adaptation parameter needs to be tuned online. It is rigorously shown that the proposed output-constrained controller guarantees that all the closed-loop signals are semiglobal uniformly ultimately bounded and the tracking error never violates the output constraint.

This paper considers the nonfragile $H_\infty $ estimation problem for a class of complex networks with switching topologies and quantization effects. The network architecture is assumed to be dynamic and evolves with time according to a random process subject to a sojourn probability. The coupled signal is to be quantized before transmission due to power and bandwidth constraints, and the quantization errors are transformed into sector-bounded uncertainties. The concept of nonfragility is introduced by inserting randomly occurred uncertainties into the estimator parameters to cope with the unavoidable small gain variations emerging from the implementations of estimators. Both the quantizers and the estimators have several operation modes depending on the switching signal of the underlying network structure. A sufficient condition is provided via a linear matrix inequality approach to ensure the estimation error dynamic to be stochastically stable in the absence of external disturbances, and the $H_\infty $ performance with a prescribed index is also satisfied. Finally, a numerical example is presented to clarify the validity of the proposed method.

This paper studies the issue of robust state estimation for coupled neural networks with parameter uncertainty and randomly occurring distributed delays, where the polytopic model is employed to describe the parameter uncertainty. A set of Bernoulli processes with different stochastic properties are introduced to model the randomly occurrences of the distributed delays. Novel state estimators based on the local coupling structure are proposed to make full use of the coupling information. The augmented estimation error system is obtained based on the Kronecker product. A new Lyapunov function, which depends both on the polytopic uncertainty and the coupling information, is introduced to reduce the conservatism. Sufficient conditions, which guarantee the stochastic stability and the performance of the augmented estimation error system, are established. Then, the estimator gains are further obtained on the basis of these conditions. Finally, a numerical example is used to prove the effectiveness of the results.

This paper investigates the global pinning synchronization problem for a class of complex networks with aperiodic samplings. Combined with the Writinger-based integral inequality, a new less conservative criterion is presented to guarantee the global pinning synchronization of the complex network. Furthermore, a novel condition is proposed under which the complex network is globally pinning synchronized with a given performance index. It is shown that the performance index has a positive correlation with the upper bound of the sampling intervals. Finally, the validity and the advantage of the theoretic results obtained are verified by means of the applications in Chua's circuit and pendulum.

The containment control problem for generally linear multiagent systems with aperiodic sampling intervals and measurement size reduction is considered in this paper. Under the assumption that the sampling interval changes from a finite set, an improved protocol is proposed, such that a larger sampling interval can be obtained to achieve containment. By using the properties of Laplacian matrix and the newly developed protocol, the containment control problem is transformed into the stability problem of a discrete-time switched linear system. A sufficient condition is obtained that ensures all the followers converge to the convex hull formed by the state of leaders, and such a sufficient condition is presented in terms of linear matrix inequalities, which are independent of the node of network. To further reduce the communication among agents, a switching-type measurement size reduction scheme is introduced. An optimization problem is proposed for the corresponding controller design. Finally, two simulation studies are conducted to show the effectiveness and advantage of the proposed control algorithms.

In this paper, the problem of distributed event-triggered pinning control for practical consensus of multiagent systems (MASs) with quantized communication based on a directed graph is investigated. The pinning control for practical consensus of MASs with uniform quantizer is first discussed. Then, in order to decrease communication load of interagent, the event-triggered quantized communication protocol is designed. The nonsmooth analysis and Gronwall's inequality approach is used to guarantee the existence of a solution to the resulting closed-loop system. It is shown that practical consensus is reachable through the event-triggered control and converges to a consensus set. Moreover, “Zeno phenomenon” can be excluded. Finally, an example is given to validate the feasibility and efficiency of the proposed new design method.

In this paper, we study the state estimation and optimal control [i.e., linear quadratic Gaussian (LQG) control] problems for networked control systems in which control inputs, observations, and packet acknowledgments (ACKs) are randomly lost. The packet ACK is a signal that is transmitted from the actuator to notice the estimator the occurence of control packet loss. For such systems, we obtain the optimal estimator, which is consisted of exponentially increasing terms. For the solvability of the LQG problem, we come to a conclusion that in general even the optimal LQG control exists, it is impossible and unnecessary to be obtained as its calculation is not only technically difficult but also computationally prohibitive. This issue motivates us to design a suboptimal LQG controller for the underlying systems. We first develop a suboptimal estimator by using the estimator gain in each term of the optimal estimator. Then we derive a suboptimal LQG controller and establish the conditions for stability of the closed-loop systems. Examples are given to illustrate the effectiveness and advantages of the proposed design scheme.

This paper is concerned with the problem of reliable dissipative control for Takagi-Sugeno fuzzy systems with Markov jumping parameters. Considering the influence of actuator faults, a sufficient condition is developed to ensure that the resultant closed-loop system is stochastically stable and strictly ( Q, S,R )-dissipative based on a relaxed approach in which mode-dependent and fuzzy-basis-dependent Lyapunov functions are employed. Then a reliable dissipative control for fuzzy Markov jump systems is designed, with sufficient condition proposed for the existence of guaranteed stability and dissipativity controller. The effectiveness and potential of the obtained design method is verified by two simulation examples.

This paper studies the problem of adaptively adjusted event-triggering mechanism-based fault detection for a class of discrete-time networked control system (NCS) with applications to aircraft dynamics. By taking into account the fault occurrence detection progress and the fault occurrence probability, and introducing an adaptively adjusted event-triggering parameter, a novel event-triggering mechanism is proposed to achieve the efficient utilization of the communication network bandwidth. Both the sensor-to-control station and the control station-to-actuator network-induced delays are taken into account. The event-triggered sensor and the event-triggered control station are utilized simultaneously to establish new network-based closed-loop models for the NCS subject to faults. Based on the established models, the event-triggered simultaneous design of fault detection filter (FDF) and controller is presented. A new algorithm for handling the adaptively adjusted event-triggering parameter is proposed. Performance analysis verifies the effectiveness of the adaptively adjusted event-triggering mechanism, and the simultaneous design of FDF and controller.

This paper is concerned with the observer-based fuzzy output-feedback control for stochastic nonlinear multiple time-delay systems. On the basis of the consistent form of virtual input signals and increasing characteristics of the system upper bound functions, a variable splitting technique is employed to surmount the difficulty occurred in the nonlower-triangular form. In the controller design procedure, a state observer is first designed, and then an adaptive fuzzy output-feedback control method is presented by combining backstepping design together with fuzzy systems' universal approximation capability. The proposed adaptive controller guarantees the semi-global boundedness of closed-loop system trajectories in terms of fourth-moment. Two simulation examples are displayed to demonstrate the feasibility of the suggested controller.

This paper considers the problem of adaptive neural control of nonlower triangular nonlinear systems with unmodeled dynamics and dynamic disturbances. The design difficulties appeared in the unmodeled dynamics and nonlower triangular form are handled with a dynamic signal and a variable partition technique for the nonlinear functions of all state variables, respectively. It is shown that the proposed controller is able to ensure the semi-global boundedness of all signals of the resulting closed-loop system. Furthermore, the system output is ensured to converge to a small domain of the given trajectories. The main advantage about this research is that a neural networks-based tracking control method is developed for uncertain nonlinear systems with unmodeled dynamics and nonlower triangular form. Simulation results demonstrate the feasibility of the newly presented design techniques.

In this paper, the problem of output synchronization is investigated for the heterogeneous network with an uncertain leader. It is assumed that parameter perturbations influence the nonidentical linear agents, whose outputs are controlled to track the output of an uncertain leader. Based on the hierarchical structure of the communication graph, a novel control scheme is proposed to guarantee the output synchronization. As there exist parameter uncertainties in the models of the agents, the internal model principle is used to gain robustness versus plant parameter uncertainties. Furthermore, as the precise model of the leader is also not available, the adaptive control principle is adopted to tune the parameters in the local controllers. The developed new technique is able to simultaneously handle uncertainties in the follower parameters as well as the leader parameters. The agents in the upper layers will be treated as the exosystems of the agents in the lower layers. The local controllers are constructed in a sequential order. It is shown that the output synchronization can be achieved globally asymptotically and locally exponentially. Finally, a simulation example is given to illustrate the effectiveness and potential of the theoretic results obtained.

In this paper, the reachable set estimation problem is investigated for Markovian jump neural networks (NNs) with time-varying delays and bounded peak disturbances. Our goal is to find a set as small as possible which bounds all the state trajectories of the NNs under zero initial conditions. In the framework of Lyapunov-Krasovskii theorem, a newly-found summation inequality combined with the reciprocally convex approach is used to bound the difference of the proposed Lyapunov functional. A new less conservative condition dependent on the upper bound, the lower bound and the delay range of the time delay is established to guarantee that the state trajectories are bounded within an ellipsoid-like set. Then the result is extended to the case with incomplete transition probabilities and a more general condition is derived. Finally, examples including a genetic regulatory network are given to demonstrate the usefulness and the effectiveness of the results obtained in this paper.

In this paper, command filtering-based fuzzy control is designed for uncertain multi-input multioutput (MIMO) nonlinear systems with saturation nonlinearity input. First, the command filtering method is employed to deal with the explosion of complexity caused by the derivative of virtual controllers. Then, fuzzy logic systems are utilized to approximate the nonlinear functions of MIMO systems. Furthermore, error compensation mechanism is introduced to overcome the drawback of the dynamics surface approach. The developed method will guarantee all signals of the systems are bounded. The effectiveness and advantages of the theoretic result are obtained by a simulation example.

In this paper, a novel adjustable parameter (AP)-based distributed fault estimation observer (DFEO) is proposed for multiagent systems (MASs) with the directed communication topology. First, a relative output estimation error is defined based on the communication topology of MASs. Then a DFEO with AP is constructed with the purpose of improving the accuracy of fault estimation. Based on${H} _{{\infty }}$and${H} _{{2}}$with pole placement, multiconstrained design is given to calculate the gain of DFEO. Finally, simulation results are presented to illustrate the feasibility and effectiveness of the proposed DFEO design with AP.

This paper is concerned with the energy-efficient distributed filtering in sensor networks, and a unified switched system approach is proposed to achieve this goal. For the system under study, the measurement is first sampled under nonuniform sampling periods, then the local measurement elements are selected and quantized for transmission. Then, the transmission rate is further reduced to save constrained power in sensors. Based on the switched system approach, a unified model is presented to capture the nonuniform sampling, the measurement size reduction, the transmission rate reduction, the signal quantization, and the measurement missing phenomena. Sufficient conditions are obtained such that the filtering error system is exponentially stable in the mean-square sense with a prescribed H∞ performance level. Both simulation and experiment studies are given to show the effectiveness of the proposed new design technique.

The problem of resilient energy-to-peak filtering for a class of uncertain continuous-time nonlinear systems is investigated in this paper. A Takagi-Sugeno fuzzy model with norm-bounded uncertainties is used to represent the nonlinear plant. Attention is focused on the design of an energy-to-peak filter such that the filtering error system is asymptotically stable and the prescribed energy-to-peak filtering performance is guaranteed, where the designed filter is assumed to have additive gain variations. The proposed design is aimed at all filter matrices with gain variations, which improves the existing results on resilient energy-to-peak filtering for continuous-time systems. A simulation example is provided to show the effectiveness of the proposed methods.

This paper deals with the fault detection filter design for a nonlinear discrete-time system in the Takagi-Sugeno fuzzy form with faults and unknown inputs. Both unknown input and fault frequencies are assumed to be known and to reside in low-/middle-/high-frequency ranges. A filter is proposed in the finite-frequency domain to reduce the conservatism generated by those designed in the entire-frequency domain. In order to guarantee the best robustness to disturbances and sensitivity to faults, the developed filter combines the H_/H∞performances. The asymptotic stability of the filtering error dynamics is ensured by using a fuzzy Lyapunov function and a linear matrix inequality approach. Finally, two examples are presented to validate the proposed new design techniques.

This paper develops a new fuzzy dynamic outputfeedback control scheme for Takagi-Sugeno (T-S) fuzzy systems with time-varying input delay and output constraints based on (Q, S, R)-α-dissipativity. The proposed controller, called a (Q, S, R)-α-dissipative output-feedback fuzzy controller, takes into consideration the abstract energy, storage function, and supply rate for the disturbance attenuation and provides a unified framework that can incorporate existing results for H∞ and passivity controllers as special cases for T-S fuzzy systems with time-varying input delay and output constraints. A dynamic parallel distributed compensator is used to design the (Q, S, R)-α-dissipative output-feedback fuzzy controller to ensure the asymptotic stability and strict (Q, S, R)-α-dissipativity of closed-loop systems described by a T-S fuzzy model that satisfies some output constraints. By employing the reciprocally convex approach, a new set of delay-dependent conditions for the desired controller is formulated in terms of the linear matrix inequality. The effectiveness and the applicability of the proposed design techniques are validated by an example of control for active suspension systems for different road conditions.

The paper is concerned with the H∞and I2-I∞filtering design problems for discrete-time nonlinear switched systems with quantized measurements using the Takagi-Sugeno (T-S) fuzzy model. The systems under consideration inherently combine features of the switched hybrid systems and the T-S fuzzy systems. The sector bound approach is employed to deal with quantization effects. Based on the fuzzy-basis-dependent Lyapunov function, sufficient conditions are established such that the filtering error system is stochastically stable and a prescribed noise attenuation level in an H∞or I2-I∞sense is achieved. Both numerical and practical examples are provided to show the feasibility and efficiency of the design schemes.

In this paper, we consider the state-feedback and static output feedback control problems of continuous singular interval-valued Takagi-Sugeno fuzzy systems with mismatched membership functions. First, based on the Lyapunov stability theory, a state-feedback control criterion is proposed to guarantee the closed-loop system to be admissible. Second, the result is extended to the static output feedback control problem, and a sufficient condition for synthesis is derived in terms of strict linear matrix inequalities, which eliminates the disadvantages in some existing results, such as same output matrices and the bilinear matrix inequality problem. To obtain less conservative results, the information of mismatched membership functions is employed. Finally, numerical examples are given to illustrate the effectiveness of the proposed techniques.

This paper proposes a new adaptive controller for a class of uncertain nonlinear systems with a quantized signal. Fuzzy logic systems are utilized to approximate nonlinear terms without imposing prior matching conditions required. A hysteretic type of quantizer is incorporated to reduce chattering. A new adaptive backstepping controller is designed to guarantee that the underlying uncertain nonlinear system is semiglobally uniformly ultimately bounded. Two numerical examples are presented to demonstrate the effectiveness and potential of the proposed techniques.

The particle filter (PF) is a popular filtering algorithm in various localization problems represented by nonlinear state-space models. Although the PF can provide accurate localization results, it often fails in localization because of the sample impoverishment phenomenon. In this paper, we propose a novel nonlinear filtering method that combines a PF with a robust filter, called a finite impulse response (FIR) filter, in order to accomplish accurate and reliable localization. The proposed filter is called the composite particle/FIR filter (CPFF). In the CPFF framework, the PF is the main filter used in normal situations. When PF failures occur, the FIR filter is used to recover the PF from failures. To detect PF failures, a new decision-making algorithm is proposed in this paper. The proposed CPFF is applied to indoor human localization using a wireless sensor network. The CPFF is accurate and reliable under conditions in which the pure PF typically exhibits degraded accuracy or failures in localization.

Industrial processes are often organized using mechanical systems with multiple degrees-of-freedom (DOF). For real-time operation of such systems in noise environments, fast, optimal, and robust estimators are required. In this paper, information gathering about multi-DOF system states is provided using the optimal finite impulse response (OFIR) filter. To use this filter in real time, a fast iterative algorithm is developed with a pseudocode available for immediate use. Although the iterative algorithm utilizes Kalman recursions, it is more robust against uncertainties and model errors owing to the transversal structure. We use this algorithm to estimate state in the 1-DOF torsion system and the 3-DOF helicopter system.

In this paper, we investigate the adaptive exponential synchronization in both the mean square and the almost sure senses for an array of N identical Markovian stochastic coupled neural networks of neutral-type with time-varying delay and random coupling strength. The generalized Lyapunov theorem of the exponential stability in the mean square for the neutral stochastic Markov system with the time-varying delay is first established. The time-varying delay in the system is assumed to be a bounded measurable function. Then, sufficient conditions to guarantee the exponential synchronization in the mean square for the underlying system are developed under an adaptive feedback controller, which are given in terms of the M -matrix and the algebraic inequalities. Under the same conditions, the almost sure exponential synchronization is also presented. A numerical example is given to show the effectiveness and potential of the proposed theoretical results.

This paper examines the problem of asymptotic stability for Markovian jump generalized neural networks with interval time-varying delays. Markovian jump parameters are modeled as a continuous-time and finite-state Markov chain. By constructing a suitable Lyapunov-Krasovskii functional (LKF) and using the linear matrix inequality (LMI) formulation, new delay-dependent stability conditions are established to ascertain the mean-square asymptotic stability result of the equilibrium point. The reciprocally convex combination technique, Jensen's inequality, and the Wirtinger-based double integral inequality are used to handle single and double integral terms in the time derivative of the LKF. The developed results are represented by the LMI. The effectiveness and advantages of the new design method are explained using five numerical examples.

This paper investigates the problem of exponential passive filtering for a class of stochastic neutral-type neural networks with both semi-Markovian jump parameters and mixed time delays. Our aim is to estimate the states by designing a Luenberger-type observer, such that the filter error dynamics are mean-square exponentially stable with an expected decay rate and an attenuation level. Sufficient conditions for the existence of passive filters are obtained, and a convex optimization algorithm for the filter design is given. In addition, a cone complementarity linearization procedure is employed to cast the nonconvex feasibility problem into a sequential minimization problem, which can be readily solved by the existing optimization techniques. Numerical examples are given to demonstrate the effectiveness of the proposed techniques.

This paper is concerned with the problem of reliable switched controller design for a class of discrete-time Takagi-Sugeno fuzzy systems with randomly occurring infinitedistributed delays and quantization as well as actuator failures. A random Bernoulli process is used to describe the stochastic infinite-distributed delays. Due to limited communication capacity, the control signal is quantized before being transmitted to the actuator by the logarithmic quantizer. We apply the switching mechanisms to categorize the stochastic behavior of actuator faults. Based on the parallel distributed compensation, the switched feedback controller is designed. By the fuzzy-basisdependent Lyapunov functional approach, sufficient conditions are obtained to ensure that the resulting closed-loop system is exponentially stable in the mean-square sense with a given l2-l∞performance index. Then, a numerical example is presented to demonstrate the effectiveness of the proposed new design method.

This paper proposes a novel delay-dependent approach to the piecewise-affine H-infinity filter design for discrete-time state-delayed nonlinear systems. The nonlinear plant is expressed by a Takagi-Sugeno fuzzy-affine model and the state delay is considered to be time-varying with available lower and upper bounds. The purpose is to design an admissible filter that guarantees the asymptotic stability of the resulting filtering error system (FES) with a prescribed disturbance attenuation level in an H-infinity sense. By applying a new piecewise-fuzzy Lyapunov-Krasovskii functional, combined with a novel summation inequality, improved reciprocally convex inequality and S-procedure, the H-infinity performance analysis criterion is first developed for the FES. Furthermore, the filter synthesis is carried out by some elegant convexification techniques. Finally, simulation examples are employed to confirm the effectiveness and less conservatism of the proposed methods.

This paper investigates the problems of robust H∞control for a class of discrete-time nonhomogenous Markovian jump linear systems (NMJLSs) by a multistep Lyapunov function (LF) approach. The proposed multistep LF is allowed to increase during the period of several sampling time step ahead of the current time within the Jump mode. First, a less conservative stability criterion is derived based on this multistep LF approach. Second, an H∞performance is analyzed under the multistep case by properly dealing with the knowledge of future states and exogenous noises. These two results are then employed to facilitate a robust H∞control design for NMJLSs, which yields a better disturbance attenuation performance as compared with the existing results. Numerical examples are included to elucidate the effectiveness of the developed results.

This paper investigates the problem of nonfragile guaranteed cost control for discrete-time Takagi-Sugeno fuzzy Markov jump systems with time-varying delays. With the help of the parallel distributed compensation, a nonfragile fuzzy controller is designed. Then via Lyapunov-Krasovskii functional approach, sufficient conditions are obtained ensuring that the resulting closed-loop system is asymptotically stable with an upper bound of the guaranteed cost index. The optimal upper bound of the guaranteed cost index and the controller gain can be achieved via the optimization technique. Finally, an example is presented to show the effectiveness of the proposed new design techniques.

This paper investigates the adaptively adjusted event-triggering mechanism-based fault detection for a discrete-time networked control system (NCS). The fault occurrence detection progress, the fault occurrence probability, and an adaptively adjusted event-triggering parameter are taken into account to construct a novel event-triggering mechanism. The event-triggered sensor and the event-triggered control station are used simultaneously to establish new network-based closed-loop models for the NCS subject to an actuator fault, and the sensor-to-control station and the control station-to-actuator network-induced delays. Based on the established models, the event-triggered simultaneous design of a fault detection filter and a controller is presented. A new algorithm for handling the adaptively adjusted event-triggering parameter is proposed. We analyse the performance of the simultaneous design of a fault detection filter and a controller to verify its effectiveness.

This paper focuses on automatic fuzzy clustering problem and proposes a novel automatic fuzzy clustering method that employs an extended membrane system with active membranes that has been designed as its computing framework. The extended membrane system has a dynamic membrane structure; since membranes can evolve, it is particularly suitable for processing the automatic fuzzy clustering problem. A modification of a differential evolution (DE) mechanism was developed as evolution rules for objects according to membrane structure and object communication mechanisms. Under the control of both the object's evolution-communication mechanism and the membrane evolution mechanism, the extended membrane system can effectively determine the most appropriate number of clusters as well as the corresponding optimal cluster centers. The proposed method was evaluated over 13 benchmark problems and was compared with four state-of-the-art automatic clustering methods, two recently developed clustering methods and six classification techniques. The comparison results demonstrate the superiority of the proposed method in terms of effectiveness and robustness.

In this letter, we propose a new approach to the design of a digital phase-locked loop (DPLL) with a finite impulse response (FIR) structure, deadbeat property, and H∞performance. This DPLL is called the deadbeat H∞FIR DPLL (DHFDPLL). The proposed DHFDPLL ensures the H∞performance against incorrect information on noise and has intrinsic robustness against quantization effects because of the FIR structure. Demonstrative simulations are provided to show that the DHFDPLL exhibits excellent robustness against effects of incorrect noise and quantization compared with the existing DPLLs.

This letter proposes a new moving-average form of digital phase-locked loop (DPLL) that uses the average value of measurements on a memory horizon and the correction term to estimate phase information. This ensures the desired unbiasedness property for the phase information. A new formula for the optimal memory size of the proposed DPLL with minimization of the expected squared phase error is established. A numerical example is given to show that the developed DPLL has superior robustness against quantization and incorrect noise compared to the existing DPLLs.

In this paper, the exponential stability in p th( p > 1 )-moment for neutral stochastic Markov systems with time-varying delay is studied. The derived stability conditions comprise two forms: 1) the delay-independent stability criteria which are obtained by establishing an integral inequality and 2) the delay-dependent stability criteria which are captured by using the theory of the functional differential equations. As its applications, the obtained stability results are used to investigate the exponential stability in p th( p > 1 )-moment for the neutral stochastic neural networks with time-varying delay and Markov switching, and the globally exponential adaptive synchronization for the neutral stochastic complex dynamical systems with time-varying delay and Markov switching, respectively. On the delay-independent criteria, sufficient conditions are given in terms of M -matrix and thus are easy to check. The delay-dependent criteria are presented in the forms of the algebraic inequalities, and the least upper bound of the time-varying delay is also provided. The primary advantages of these obtained results over some recent and similar works are that the differentiability or continuity of the delay function is not required, and that the difficulty stemming from the presence of the neutral item and the Markov switching is overcome. Three numerical examples are provided to examine the effectiveness and potential of the theoretic results obtained.

In this paper, the problem of distributed adaptive fuzzy control is investigated for high-order uncertain nonlinear multiagent systems on directed graph with a fixed topology. It is assumed that only the outputs of each follower and its neighbors are available in the design of its distributed controllers. Equivalent output injection sliding mode observers are proposed for each follower to estimate the states of itself and its neighbors, and an observer-based distributed adaptive controller is designed for each follower to guarantee that it asymptotically synchronizes to a leader with tracking errors being semi-globally uniform ultimate bounded, in which fuzzy logic systems are utilized to approximate unknown functions. Based on algebraic graph theory and Lyapunov function approach, using Filippov-framework, the closed-loop system stability analysis is conducted. Finally, numerical simulations are provided to illustrate the effectiveness and potential of the developed design techniques.

This paper studies the problem of event-triggered fault detection filter (FDF) and controller coordinated design for a continuous-time networked control system (NCS) with biased sensor faults. By considering sensor-to-FDF network-induced delays and packet dropouts, which do not impose a constraint on the event-triggering mechanism, and proposing the simultaneous network bandwidth utilization ratio and fault occurrence probability-based event-triggering mechanism, a new closed-loop model for the considered NCS is established. Based on the established model, the event-triggered H∞performance analysis, and FDF and controller coordinated design are presented. The combined mutually exclusive distribution and Wirtinger-based integral inequality approach is proposed for the first time to deal with integral inequalities for products of vectors. This approach is proved to be less conservative than the existing Wirtinger-based integral inequality approach. The designed FDF and controller can guarantee the sensitivity of the residual signal to faults and the robustness of the NCS to external disturbances. The simulation results verify the effectiveness of the proposed event-triggering mechanism, and the FDF and controller coordinated design.

This paper is concerned with the consensus of multiagent systems with nonlinear dynamics through the use of aperiodic sampled-data controllers, which are more flexible than classical periodic sampled-data controllers. By input delay approach, the resulting sampled-data system is reformulated as a continuous system with time-varying delay in the control input. A continuous Lyapunov functional, which captures the information on sampling pattern, together with the free-weighting matrix method, is then used to establish a sufficient condition for consensusability. For a more general case that the sampled-data controllers are subject to constant input delays, a novel discontinuous Lyapunov functional is introduced on the basis of the vector extension of Wirtinger's inequality. This functional can lead to simplified and efficient stability conditions for computation and optimization. Further results on the estimate of maximal allowable sampling interval upper bound is given as well. Numerical example is provided to show the effectiveness and merits of the proposed protocol.

In this paper, an approximated-based adaptive fuzzy control approach with only one adaptive parameter is presented for a class of single input single output strict-feedback nonlinear systems in order to deal with phenomena like nonlinear uncertainties, unmodeled dynamics, dynamic disturbances, and unknown time delays. Lyapunov-Krasovskii function approach is employed to compensate the unknown time delays in the design procedure. By combining the advances of the hyperbolic tangent function with adaptive fuzzy backstepping technique, the proposed controller guarantees the semi-globally uniformly ultimately boundedness of all the signals in the closed-loop system from the mean square point of view. Two simulation examples are finally provided to show the superior effectiveness of the proposed scheme.

In this paper, tracking control problems are investigated for a class of uncertain nonlinear systems in lower triangular form. First, a state-feedback controller is designed by using adaptive backstepping technique and the universal approximation ability of fuzzy logic systems. During the design procedure, a developed method with less computation is proposed by constructing one maximum adaptive parameter. Furthermore, adaptive controllers with nonsymmetric dead-zone are also designed for the systems. Then, a sampled-data control scheme is presented to discretize the obtained continuous-time controller by using the forward Euler method. It is shown that both proposed continuous and discrete controllers can ensure that the system output tracks the target signal with a small bounded error and the other closed-loop signals remain bounded. Two simulation examples are presented to verify the effectiveness and applicability of the proposed new design techniques.

This paper investigates the dynamic output feedback control problem for interval type-2 (IT2) fuzzy systems. A switched output feedback controller, which depends on the values of membership functions, is constructed. The membership functions of IT2 fuzzy systems contain parameter uncertainties and are different from the type-1 Takagi-Sugeno fuzzy systems. Based on the IT2 fuzzy set theory, the parameter uncertainties can be effectively obtained by upper and lower membership functions. A novel IT2 switched output feedback controller is designed to ensure that the closed-loop system is asymptotically stable with an H∞ performance. Finally, a mass-spring-damping system is proposed to show the feasibility and the merit of the proposed scheme over the other existing ones.

In this paper, a novel adaptive sliding mode controller is designed for Takagi-Sugeno (T-S) fuzzy systems with actuator saturation and system uncertainty. By the delta operator approach, the discrete-time nonlinear system is described by a T-S fuzzy model with unmeasurable state. By singular value decomposition of system input matrix, a reduced-order system is obtained for the design of sliding mode surface. A new adaptive sliding mode controller based on system output is presented to guarantee that the closed-loop system is uniformly ultimately bounded. Four examples are provided to illustrate the effectiveness and applicability of the proposed control scheme.

This paper is concerned with the dissipativity analysis and design of discrete Markovian jumping neural networks with sector-bounded nonlinear activation functions and time-varying delays represented by Takagi-Sugeno fuzzy model. The augmented fuzzy neural networks with Markovian jumps are first constructed based on estimator of Luenberger observer type. Then, applying piecewise Lyapunov-Krasovskii functional approach and stochastic analysis technique, a sufficient condition is provided to guarantee that the augmented fuzzy jump neural networks are stochastically dissipative. Moreover, a less conservative criterion is established to solve the dissipative state estimation problem by using matrix decomposition approach. Furthermore, to reduce the computational complexity of the algorithm, a dissipative estimator is designed to ensure stochastic dissipativity of the error fuzzy jump neural networks. As a special case, we have also considered the mixed H∞and passive analysis of fuzzy jump neural networks. All criteria can be formulated in terms of linear matrix inequalities. Finally, two examples are given to show the effectiveness and potential of the new design techniques.

In this brief, the problems of the mixed H-infinity and passivity performance analysis and design are investigated for discrete time-delay neural networks with Markovian jump parameters represented by Takagi-Sugeno fuzzy model. The main purpose of this brief is to design a filter to guarantee that the augmented Markovian jump fuzzy neural networks are stable in mean-square sense and satisfy a prescribed passivity performance index by employing the Lyapunov method and the stochastic analysis technique. Applying the matrix decomposition techniques, sufficient conditions are provided for the solvability of the problems, which can be formulated in terms of linear matrix inequalities. A numerical example is also presented to illustrate the effectiveness of the proposed techniques.

This paper focuses on dynamic learning from neural control for a class of nonlinear strict-feedback systems with predefined tracking performance attributes. To reduce the number of neural network (NN) approximators used and make the convergence of neural weights verified easily, state variables are introduced to transform the state-feedback control of the original strict-feedback systems into the output-feedback control of the system in the normal form. Then, using the output error transformation based on performance functions, the constrained tracking control problem of the normal systems is transformed into the stabilization problem of an equivalent unconstrained one. By combining the backstepping method, a high-gain observer with radial basis function (RBF) NNs, a novel adaptive neural control (ANC) scheme is proposed to guarantee the predefined tracking error performance as well as the ultimate boundedness of all other closed-loop signals. In particular, only one NN is employed to approximate the lumped unknown system dynamics during the controller design. Under the satisfaction of the partial persistent excitation condition for RBF NNs, the proposed stable ANC scheme is shown to be capable of achieving knowledge acquisition, expression, and storage of unknown system dynamics. The stored knowledge is reused to develop a neural learning controller for improving the control performance of the closed-loop system. When the initial condition satisfies the predefined performance, the proposed neural learning control can still guarantee the predefined tracking performance. Simulation results on a third-order one-link robot are given to show the effectiveness of the proposed method.

This brief is concerned with the problem of intelligent tracking control for a class of high-order nonlinear systems with completely unknown nonlinearities. An intelligent adaptive control algorithm is presented by combining the adaptive backstepping technique with the neural networks' approximation ability. It is shown that the practical output tracking performance of the system is achieved using the proposed state-feedback controller under two mild assumptions. In particular, by introducing a parameter in the derivations, the tracking error between the time-varying target signal and the output can be reduced via tuning the controller design parameters. Moreover, in order to solve the problem of overparameterization, which is a common issue in adaptive control design, a controller with one adaptive law is also designed. Finally, simulation results are given to show the effectiveness of the theoretical approaches and the potential of the proposed new design techniques.

In this paper, an adaptive neural fault-tolerant control scheme is proposed for the three degrees of freedom model helicopter, subject to system uncertainties, unknown external disturbances, and actuator faults. To tackle system uncertainty and nonlinear actuator faults, a neural network disturbance observer is developed based on the radial basis function neural network. The unknown external disturbance and the unknown neural network approximation errors are treated as a compound disturbance that is estimated by another nonlinear disturbance observer. A disturbance observer-based adaptive neural fault-tolerant control scheme is then developed to track the desired system output in the presence of system uncertainty, external disturbance, and actuator faults. The stability of the whole closed-loop system is analyzed using the Lyapunov method, which guarantees the convergence of all closed-loop signals. Finally, the simulation results are presented to illustrate the effectiveness of the new control design techniques.

This paper investigates the output consensus control problem of uncertain second-order nonlinear multiagent systems with unknown nonlinear dead zone, where the communication networks can be described as a directed graph with a fixed topology. The control input in each follower is assumed to be subject to unknown nonlinear dead zone. By the function approximation capability of neural networks (NNs), a distributed adaptive NN-based controller is designed for each follower in the graph such that all the outputs of the followers can asymptotically synchronize to the output of the leader with synchronization errors being semiglobally uniformly ultimately bounded. Based on Lyapunov stability theory and algebraic graph theory, analysis is conducted on stability and parameter convergence of the proposed algorithm. Finally, an example is provided to validate the theoretical results.

This paper addresses the problems of stability and stabilization for a class of discrete-time Takagi-Sugeno fuzzy systems with semi-Markov stochastic uncertainties. By means of the semi-Markov kernel, the probability density function of the sojourn time for different modes in describing the underlying semi-Markov stochastic uncertainties can be mode-dependent in contrast with the previous studies. Both the sojourn-time-independent and sojourn-time-dependent Lyapunov functions are proposed, by which the stability criteria are obtained in terms of a new σ-error mean square stability concept, and the latter is demonstrated to be less conservative and more practical than some existing results. Then, control synthesis problem is investigated and the sufficient conditions on the existence of admissible mode-dependent state-feedback stabilizing controller are developed. A numerical example and a cart-pendulum system are given to show the effectiveness and potential of the new design techniques.

In this paper, the resilient H∞filtering problem for a class of discrete-time Markov jump systems with unideal measurements is investigated. The unideal measurements contain both quantization and missing measurements simultaneously, which occur randomly satisfying two mutually independent Bernoulli distribute white sequences. A unified model is used to describe the unideal measurements phenomena, and a norm-bounded additive gain perturbation is introduced to model the resilient filter. A mode-dependent full-order filter is designed such that the filtering error system is stochastically stable with an ensured H∞performance index. An application on a single-link robot arm is provided to verify the theoretical results.

In this article, stability analysis for neutral stochastic neural networks with time-varying delay and Markovian jumping parameters is studied. By utilizing the theory of functional differential equations, some results about exponential stability criteria in p (p > 1)-moment, which are delay-dependent, are given. The primary advantages of these obtained results over some recent and similar works are that the differentiability or continuity of the delay function is not required, and that the difficulty stemming from the existence of the neutral item is overcome. A numerical example is given to examine the correctness of the derived result.

This paper is concerned with formation control and stability analysis for multi-agent systems. The individual sensing range is taken as limited and agents are assumed not able to communicate. The concept of extendible periodic formation is established to represent a class of large-scale systems and later used in the proof of stability. In the control strategy, the periodic formation is first partially reached with some misalignments. Then, in order to eliminate the misalignments, a multi-shape matching algorithm is designed to identify them and reach the predefined formation. The concept of group control edge is proposed, whose different types are analyzed. It is shown that under optimal sizes, the convergence rate could be maximized. Moreover, a premature stop strategy is presented that could reduce computation burden. Analysis is made on numerical examples to demonstrate the performance and merits of the proposed method.

This paper investigates the cooperative control problem of uncertain high-order nonlinear multi-agent systems on directed graph with a fixed topology. Each follower is assumed to have an unknown controlling effect which depends on its own state. By the Nussbaum-type gain technique and the function approximation capability of neural networks, a distributed adaptive neural networks-based controller is designed for each follower in the graph such that all followers can asymptotically synchronize the leader with tracking errors being semi-globally uniform ultimate bounded. Analysis of stability and parameter convergence of the proposed algorithm are conducted based on algebraic graph theory and Lyapunov theory. Finally, a example is provided to validate the theoretical results. Note to Practitioners-Many practical applications can be modeled as uncertain high-order nonlinear multi-agent systems, whose node' controlling effects are state-dependent. In most relevant literatures, however, it is often assumed that each node' controlling effects are equal to one. How to cooperative control for the systems has become one main focus of control researches. Therefore, in this paper, an adaptive cooperative control scheme is proposed for such multi-agent systems. Finally, the effectiveness of the control strategies is illustrated via simulation study.

This paper is concerned with the problems of receding horizon stabilization and disturbance attenuation for neural networks with time-varying delay. New delay-dependent conditions on the terminal weighting matrices of a new finite horizon cost functional for receding horizon stabilization are established for neural networks with time-varying or time-invariant delays using single- and double-integral Wirtinger-type inequalities. Based on the results, delay-dependent sufficient conditions for the receding horizon disturbance attenuation are given to guarantee the infinite horizon H∞ performance of neural networks with time-varying or time-invariant delays. Three numerical examples are provided to illustrate the effectiveness of the proposed approach.

In this paper, the problem of fuzzy control for nonlinear networked control systems with packet dropouts and parameter uncertainties is studied based on the interval type-2 fuzzy-model-based approach. In the control design, the intermittent data loss existing in the closed-loop system is taken into account. The parameter uncertainties can be represented and captured effectively via the membership functions described by lower and upper membership functions and relative weighting functions. A novel fuzzy state-feedback controller is designed to guarantee the resulting closed-loop system to be stochastically stable with an optimal performance. Furthermore, to make the controller design more flexible, the designed controller does not need to share membership functions and amount of fuzzy rules with the model. Some simulation results are provided to demonstrate the effectiveness of the proposed results.

This paper investigates the consensus problem of multiple Euler-Lagrange systems under directed topology. Unlike the common assumptions on continuous-time information exchanges, a more realistic sampled-data communication strategy is proposed with probabilistic occurrence of time-varying delays. Both of the sampling period and the delays are assumed to be time-varying, which is more general in some practical situations. In addition, the relative coordinate derivative information is not required in the distributed controllers such that the communication network burden can be further reduced. In particular, a distinct feature of the proposed scheme lies in the fact that it can effectively reduce the energy consumption. By employing the stochastic analysis techniques, sufficient conditions are established to guarantee that the consensus can be achieved. Finally, a numerical example is provided to illustrate the applicability and benefits of the theoretical results.

This paper addresses the exponential H∞ filtering problem for a class of discrete-time switched neural networks with random time-varying delays. The involved delays are assumed to be randomly time-varying which are characterized by introducing a Bernoulli stochastic variable. Effects of both variation range and distribution probability of the time delays are considered. The nonlinear activation functions are assumed to satisfy the sector conditions. Our aim is to estimate the state by designing a full order filter such that the filter error system is globally exponentially stable with an expected decay rate and a H∞ performance attenuation level. The filter is designed by using a piecewise Lyapunov-Krasovskii functional together with linear matrix inequality (LMI) approach and average dwell time method. First, a set of sufficient LMI conditions are established to guarantee the exponential mean-square stability of the augmented system and then the parameters of full-order filter are expressed in terms of solutions to a set of LMI conditions. The proposed LMI conditions can be easily solved by using standard software packages. Finally, numerical examples by means of practical problems are provided to illustrate the effectiveness of the proposed filter design.

This paper is concerned with the problem of reliable mixed H ∞ and passivity-based control for a class of stochastic Takagi-Sugeno (TS) fuzzy systems with Markovian switching and probabilistic time varying delays. Different from the existing works, the H∞ and passivity control problem with probabilistic occurrence of time-varying delays and actuator failures is considered in a unified framework, which is more general in some practical situations. The main aim of this paper is to design a reliable mixed H∞ and passivity-based controller such that the stochastic TS fuzzy system with Markovian switching is stochastically stable with a prescribed mixed H∞ and passivity performance level γ > 0 . Based on the Lyapunov-Krasovskii functional (LKF) involving lower and upper bound of probabilistic time delay and convex combination technique, a new set of delay-dependent sufficient condition in terms of linear matrix inequalities (LMIs) is established for obtaining the required result. Finally, a numerical example based on the modified truck-trailer model is given to demonstrate the effectiveness and applicability of the proposed design techniques.

This paper addresses the problem of robust fuzzy L2-L∞ filtering for a class of uncertain nonlinear discrete-time Markov jump systems (MJSs) with nonhomogeneous jump processes. The Takagi-Sugeno fuzzy model is employed to represent such nonlinear nonhomogeneous MJS with norm-bounded parameter uncertainties. In order to decrease conservation, a polytope Lyapunov function which evolves as a convex function is employed, and then, under the designed mode-dependent and variation-dependent fuzzy filter which includes the membership functions, a sufficient condition is presented to ensure that the filtering error dynamic system is stochastically stable and that it has a prescribed L2-L∞ performance index. Two simulated examples are given to demonstrate the effectiveness and advantages of the proposed techniques.

This paper considers the problem of discrete-time adaptive position tracking control for a interior permanent magnet synchronous motor (IPMSM) based on fuzzy-approximation. Fuzzy logic systems are used to approximate the nonlinearities of the discrete-time IPMSM drive system which is derived by direct discretization using Euler method, and a discrete-time fuzzy position tracking controller is designed via backstepping approach. In contrast to existing results, the advantage of the scheme is that the number of the adjustable parameters is reduced to two only and the problem of coupling nonlinearity can be overcome. It is shown that the proposed discrete-time fuzzy controller can guarantee the tracking error converges to a small neighborhood of the origin and all the signals are bounded. Simulation results illustrate the effectiveness and the potentials of the theoretic results obtained.

This paper addresses the problem of fault estimation observer design with finite-frequency specifications for discrete-time Takagi-Sugeno (T-S) fuzzy systems. First, for such T-S fuzzy models, an H∞ fault estimation observer with pole-placement constraint is proposed to achieve fault estimation. Based on the generalized Kalman-Yakubovich-Popov lemma, the given finite-frequency observer possesses less conservatism compared with the design of the entire-frequency domain. Furthermore, the performance of the presented fault estimation observer is further enhanced by adding the degree of freedom. Finally, two examples are presented to illustrate the effectiveness of the proposed strategy.

This paper is concerned with H∞ control problem for a class of discrete-time Takagi-Sugeno fuzzy Markov jump systems with time-varying delays under unreliable communication links. It is assumed that the data transmission between the plant and the controller are subject to randomly occurred packet dropouts satisfying Bernoulli distribution and the dropout rate is uncertain. Based on a fuzzy-basis-dependent and mode-dependent Lyapunov function, the existence conditions of the desired H∞ state-feedback controllers are derived by employing the scaled small gain theorem such that the closed-loop system is stochastically stable and achieves a guaranteed H∞ performance. The gains of the controllers are constructed by solving a set of linear matrix inequalities. Finally, a practical example of robot arm is provided to illustrate the performance of the proposed approach.

This paper is concerned with the resilient H∞ filtering problem for a class of discrete-time Markov jump neural networks (NNs) with time-varying delays, unideal measurements, and multiplicative noises. The transitions of NNs modes and desired mode-dependent filters are considered to be asynchronous, and a nonhomogeneous mode transition matrix of filters is used to model the asynchronous jumps to different degrees that are also mode-dependent. The unknown time-varying delays are also supposed to be mode-dependent with lower and upper bounds known a priori. The unideal measurements model includes the phenomena of randomly occurring quantization and missing measurements in a unified form. The desired resilient filters are designed such that the filtering error system is stochastically stable with a guaranteed H∞ performance index. A monotonicity is disclosed in filtering performance index as the degree of asynchronous jumps changes. A numerical example is provided to demonstrate the potential and validity of the theoretical results.

In this paper, an approximation-based adaptive tracking control approach is proposed for a class of multiinput multioutput nonlinear systems. Based on the method of neural network, a novel adaptive controller is designed via backstepping design process. Furthermore, by introducing Nussbaum function, the issue of unknown control directions is handled. In the backstepping design process, the dynamic surface control technique is employed to avoid differentiating certain nonlinear functions repeatedly. Moreover, in order to reduce the number of adaptation laws, we do not use the neural networks to directly approximate the unknown nonlinear functions but the desired control signals. Finally, we provide two examples to illustrate the effectiveness of the proposed approach.

In this paper, first, relationship between bipolar-valued fuzzy set and fuzzy set with its extensions is discussed. Second, a new order relation about bipolar-valued fuzzy sets is introduced. Contrary to the existing YinYang order relation about bipolar-valued fuzzy sets, which focuses on the “equilibrium” monotonicity, the new proposed order relation is concerned with “preference” monotonicity. And then, some new operations and related properties about the new defined order relation are presented. Third, by combining bipolar-valued fuzzy set with the rough set theory, the concept of the bipolar-valued rough fuzzy set is developed, which is the first attempt to consider inconsistent bipolarity into rough set theory. Particularly, by introducing two new operations to the rough set theory, the widely existing information losing problem in the computation process is solved. Furthermore, parameter-related and parameter-free rough degrees about the bipolar-valued fuzzy sets in a crisp approximation space are introduced. Finally, the bipolar-valued fuzzy decision information system is given; then, both the attribute reduction method and the knowledge discovery method based on the proposed roughness degree are presented. An example is included to show the feasibility and potential of the obtained theoretical results.

In this paper, the problem of induced L2disturbance attenuation control is investigated for discrete-time Takagi-Sugeno (T-S) fuzzy delta operator systems with time-varying delays and disturbance input via an input-output approach. A model transformation method is utilized to approximate the time-varying delay in T-S fuzzy delta operator systems. By applying the scaled small gain (SSG) theorem and Lyapunov-Krasovskii functional approach, a sufficient condition is established to guarantee that the closed-loop system is asymptotically stable and has an induced L2disturbance attenuation performance. The existence condition of the dynamic output-feedback controller can be solved via convex optimization problems. Finally, simulation results are given to demonstrate the feasibility and effectiveness of the proposed method.

In this paper, the problem of fuzzy predictive control of nonlinear networked control systems subject to parameter uncertainties and defective communication links is studied. Stochastic variables with Bernoulli random binary distribution are used to represent the defective communication links with packets loss occurring intermittently between the controller and the physical plant. An interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy model is employed to describe the nonlinear plant subject to parameter uncertainties, which can be captured with the lower and upper membership functions. The IT2 fuzzy model and IT2 fuzzy controller are not required to share the same lower and upper membership functions. In order to design the state-feedback fuzzy model predictive controller, an optimization problem which minimizes the upper bound of a quadratic objective function subject to input constraints and packets dropout is formulated and solved at every sampling instant in the finite time horizon. By introducing some slack matrices, less conservative conditions are developed for system stability analysis. Two examples are given to demonstrate the effectiveness and merits of the proposed new design techniques.

In this paper, the model approximation problem is investigated for a Takagi-Sugeno fuzzy switched system with stochastic disturbance. For a high-order considered system, our attention is focused on the construction of a reduced-order model, which not only approximates the original system well with a Hankel-norm performance but translates it into a lower dimensional fuzzy switched system as well. By using the average dwell time approach and the piecewise Lyapunov function technique, a sufficient condition is first proposed to guarantee the mean-square exponential stability with a Hankel-norm error performance for the error system. The model approximation is then converted into a convex optimization problem by using a linearization procedure. Finally, simulations are provided to illustrate the effectiveness of the proposed theory.

This paper is concerned with the problem of dissipative control for Takagi-Sugeno fuzzy systems under time-varying sampling with a known upper bound on the sampling intervals. Based on the time-dependent Lyapunov-Krasovskii functional approach, which makes full use of the available information about the actual sampling pattern, a sufficient condition is established to guarantee the sampled-data systems to be exponentially stable and strictly (Q, S, R)-γ-dissipative. Based on the criterion, a design algorithm for the desired sampled-data controller is proposed. The effectiveness and benefits of the results developed in this paper is demonstrated by a controller design for a truck-trailer system.

This paper addresses the problem of observer-based output feedback controller designs for discrete-time T–S fuzzy systems based on a relaxed approach in which the fuzzy Lyapunov functions are used. Different from the existing two-step method, a single-step linear matrix inequality method is provided for the observer-based controller design. It is shown that the controller and observer parameters can be obtained by solving a set of strict linear matrix inequalities that are numerically feasible with commercially available software. The new design method not only overcomes the drawback induced by the two-step approach but also provides less conservative results over some existing results. Finally, the effectiveness of the proposed approach is demonstrated by an example.

In this paper, we investigate the filtering problem of discrete-time Takagi-Sugeno (T-S) fuzzy uncertain systems subject to time-varying delays. A reduced-order filter is designed. With the augmentation technique, a filtering error system with delayed states is obtained. In order to deal with time delays in system states, the filtering error system is first transformed into two interconnected subsystems. By using a two-term approximation for the time-varying delay, sufficient delay-dependent conditions of finite-time boundedness and H∞performance of the filtering error system are derived with the Lyapunov function. Based on these conditions, the filter design methods are proposed and the filter gain matrices can be obtained by calculating a set of linear matrix inequalities. A numerical example is used to illustrate the effectiveness of the proposed approaches.

In this paper, a novel design method for adaptive tracking control is proposed for robot finger dynamics. First, the dynamics are described by considering the robot finger as a large-scale system since it has many joints and multi-degrees of freedoms (DOFs). Second, by employing the direct adaptive fuzzy approximation method to approximate the unknown and desired control input signals instead of the unknown nonlinear functions, the number of adaptive design parameters obtained in the control design process is greatly reduced. Moreover, it is shown that the designed controller can guarantee all the signals in the closed-loop system to be semiglobally uniformly ultimately bounded. Finally, simulations are conducted on the Puma 560 robot manipulator, and the results show the effectiveness of the developed control design approach.

This brief considers the problem of neural networks (NNs)-based adaptive dynamic surface control (DSC) for permanent magnet synchronous motors (PMSMs) with parameter uncertainties and load torque disturbance. First, NNs are used to approximate the unknown and nonlinear functions of PMSM drive system and a novel adaptive DSC is constructed to avoid the explosion of complexity in the backstepping design. Next, under the proposed adaptive neural DSC, the number of adaptive parameters required is reduced to only one, and the designed neural controllers structure is much simpler than some existing results in literature, which can guarantee that the tracking error converges to a small neighborhood of the origin. Then, simulations are given to illustrate the effectiveness and potential of the new design technique.

A high performance H∞controller designed to control an Active Magnetic Bearing (AMB) system is considered in this paper. A digital signal processor (DSP) is used to implement the designed controller in real-time. The designed H∞controller is compared with a conventional lead-lag type compensator and the on-board analogue controller of the AMB under various conditions and disturbances acting on the system. Designing the controller based on classical methods could become tedious, especially for systems with high order model. By contrast, H∞controller design requires only two properly designed weighting functions regardless of the model order. These weighting functions are selected to reflect the trade-off between the system's performance and robustness. In this paper we show that appropriate selection of these weighting functions is the key to a practically useful controller in terms of co-norm optimization. The experimental results have verified the enhanced performance and robustness of the system under H∞control in the presence of disturbances.

In this paper, two adaptive observer-based strategies are proposed for control of nonlinear processes using input/output (I/O) data. In the two strategies, pseudo-partial derivative (PPD) parameter of compact form dynamic linearization and PPD vector of partial form dynamic linearization are all estimated by the adaptive observer, which are used to dynamically linearize a nonlinear system. The two proposed control algorithms are only based on the PPD parameter estimation derived online from the I/O data of the controlled system, and Lyapunov-based stability analysis is used to prove all signals of close-loop control system are bounded. A numerical example, a steam-water heat exchanger example and an experimental test show that the proposed control algorithm has a very reliable tracking ability and a satisfactory robustness to disturbances and process dynamics variations.

This paper focuses on approximation-based adaptive neural control of a class of nonlinear non-strict-feedback systems. Based on the structural characteristic and the monotonously increasing property of the system bounding functions, a variable separation method is first developed. By this method, an approximation-based adaptive backstepping approach is proposed for a class of nonlinear non-strict-feedback systems. It is shown that the proposed controller guarantees semi-global boundedness of all the signals in the closed-loop systems. Three examples are used to illustrate the effectiveness of the proposed approach.

This paper investigates the problem of sampled-data H∞ control of uncertain active suspension systems via fuzzy control approach. Our work focuses on designing state-feedback and output-feedback sampled-data controllers to guarantee the resulting closed-loop dynamical systems to be asymptotically stable and satisfy H∞ disturbance attenuation level and suspension performance constraints. Using Takagi-Sugeno (T-S) fuzzy model control method, T-S fuzzy models are established for uncertain vehicle active suspension systems considering the desired suspension performances. Based on Lyapunov stability theory, the existence conditions of state-feedback and output-feedback sampled-data controllers are obtained by solving an optimization problem. Simulation results for active vehicle suspension systems with uncertainty are provided to demonstrate the effectiveness of the proposed method.

In this paper, the problem of adaptive active fault-tolerant control for a class of nonlinear systems with unknown actuator fault is investigated. The actuator fault is assumed to have no traditional affine appearance of the system state variables and control input. The useful property of the basis function of the radial basis function neural network (NN), which will be used in the design of the fault tolerant controller, is explored. Based on the analysis of the design of normal and passive fault tolerant controllers, by using the implicit function theorem, a novel NN-based active fault-tolerant control scheme with fault alarm is proposed. Comparing with results in the literature, the fault-tolerant control scheme can minimize the time delay between fault occurrence and accommodation that is called the time delay due to fault diagnosis, and reduce the adverse effect on system performance. In addition, the FTC scheme has the advantages of a passive fault-tolerant control scheme as well as the traditional active fault-tolerant control scheme's properties. Furthermore, the fault-tolerant control scheme requires no additional fault detection and isolation model which is necessary in the traditional active fault-tolerant control scheme. Finally, simulation results are presented to demonstrate the efficiency of the developed techniques.

In this paper, the problem of reliable filter design with strict dissipativity has been investigated for a class of discrete-time T-S fuzzy time-delay systems. Our attention is focused on the design of a reliable filter to ensure a strictly dissipative performance for the filtering error system. Based on the reciprocally convex approach, firstly, a sufficient condition of reliable dissipativity analysis is proposed for T-S fuzzy systems with time-varying delays and sensor failures. Then, a reliable filter with strict dissipativity is designed by solving a convex optimization problem, which can be efficiently solved by standard numerical algorithms. Finally, numerical examples are provided to illustrate the effectiveness of the developed techniques.

In this paper, the distributed finite-time containment control problem for double-integrator multiagent systems with multiple leaders and external disturbances is discussed. In the presence of multiple dynamic leaders, by utilizing the homogeneous control technique, a distributed finite-time observer is developed for the followers to estimate the weighted average of the leaders' velocities at first. Then, based on the estimates and the generalized adding a power integrator approach, distributed finite-time containment control algorithms are designed to guarantee that the states of the followers converge to the dynamic convex hull spanned by those of the leaders in finite time. Moreover, as a special case of multiple dynamic leaders with zero velocities, the proposed containment control algorithms also work for the case of multiple stationary leaders without using the distributed observer. Simulations demonstrate the effectiveness of the proposed control algorithms.

This paper investigates the problem of local synchronization of chaotic neural networks with sampled-data and actuator saturation. A new time-dependent Lyapunov functional is proposed for the synchronization error systems. The advantage of the constructed Lyapunov functional lies in the fact that it is positive definite at sampling times but not necessarily between sampling times, and makes full use of the available information about the actual sampling pattern. A local stability condition of the synchronization error systems is derived, based on which a sampled-data controller with respect to the actuator saturation is designed to ensure that the master neural networks and slave neural networks are locally asymptotically synchronous. Two optimization problems are provided to compute the desired sampled-data controller with the aim of enlarging the set of admissible initial conditions or the admissible sampling upper bound ensuring the local synchronization of the considered chaotic neural networks. A numerical example is used to demonstrate the effectiveness of the proposed design technique.

In this paper, the problem of adaptive synchronization is investigated for stochastic neural networks of neutral-type with Markovian switching parameters. Using the M-matrix approach and the stochastic analysis method, some sufficient conditions are obtained to ensure three kinds of adaptive synchronization for the stochastic neutral-type neural networks. These three kinds of adaptive synchronization include the almost sure asymptotical synchronization, exponential synchronization in p th moment and almost sure exponential synchronization. Some numerical examples are provided to illustrate the effectiveness and potential of the proposed design techniques.

This paper is concerned with the problems of D-stability and nonfragile control for a class of discrete-time descriptor Takagi-Sugeno (T-S) fuzzy systems with multiple state delays. D-stability criteria are proposed to ensure that all the poles of the descriptor T-S fuzzy system are located within a disk contained in the unit circle. Furthermore, a sufficient condition is presented such that the closed-loop system is regular, causal, and D-stable, in spite of parameter uncertainties and multiple state delays. The corresponding solvability conditions for the desired fuzzy-rule-dependent nonfragile controllers are also established. Finally, examples are given to show the effectiveness and advantages of the proposed techniques.

It is well known that there always exist some level of time delay between fault occurrence and fault accommodation, which is called as the time delay due to fault diagnosis (TDDTFD) in this paper. TDDTFD may cause severe loss of system performance and stability. This paper investigates the TDDTFD's adverse effect on the system performance. First, a fault diagnosis (FD) model is constructed to diagnose sensor faults which integrate time-varying gain and bias faults, where a novel FD algorithm is proposed, which removes the classical assumption that the time derivative of the output error should be known. Meanwhile, the time spent at each step in FD and its analytical expression are derived strictly. Further, the analysis of the system performance degraded by TDDTFD is developed, and the conditions under which the magnitudes of sensor faults should be satisfied such that the state of the faulty system controlled by the normal controller remains bounded during TDDTFD are derived. In addition, the corresponding solutions are proposed to minimize the adverse effect of the time delay. Finally, simulation results of near-space vehicle attitude dynamics are presented to demonstrate the efficiency of the proposed approach.

In this paper, the cooperative adaptive fault tolerant fuzzy tracking control (CAFTFTC) problem of networked high-order multiagent with time-varying actuator faults is studied, and a novel CAFTFTC scheme is proposed to guarantee that all follower nodes asymptotically synchronize a leader node with tracking errors converging to a small adjustable neighborhood of the origin in spite of actuator faults. The leader node is modeled as a higher order nonautonomous nonlinear system. It acts as a command generator giving commands only to a small portion of the networked group. Each follower is assumed to have nonidentical unknown nonlinear dynamics, and the communication network is also assumed to be a weighted directed graph with a fixed topology. A distributed robust adaptive fuzzy controller is designed for each follower node such that the tracking errors are cooperative uniform ultimate boundedness (CUUB). Moreover, these controllers are distributed in the sense that the controller designed for each follower node only requires relative state information between itself and its neighbors. The adaptive compensation term of the optimal approximation errors and external disturbances is adopted to reduce the effects of the errors and disturbances, which removes the assumption that the upper bounds of unknown function approximation errors and disturbances should be known. Analysis of stability and parameter convergence of the proposed algorithm are conducted that are based on algebraic graph theory and Lyapunov theory. Comparing with results in the literature, the CAFTFTC scheme can minimize the time delay between fault occurrence and accommodation and reduce its adverse effect on system performance. In addition, the FTC scheme requires no additional fault isolation model, which is necessary in the traditional active FTC scheme. Finally, an example is provided to validate the theoretical results.

In this paper, we present the definition of fuzzy n-ellipsoid numbers that are a special kind n-dimensional fuzzy numbers which are not only more objective and more rational in expressing uncertain multichannel digital information than are fuzzy n-cell numbers but keep the convenience being used in applications and researches of the theory as well. We also obtain two representation results. Then, for the sake of the application of fuzzy n-ellipsoid numbers, we define some special kinds of fuzzy n-ellipsoid numbers, investigate their properties, set up a specific iterative algorithm of their membership function value, and prove the convergence of the iterative algorithm. And then, we establish an algorithmic version of constructing fuzzy n-ellipsoid numbers to express an object that is characterized by a group of uncertain multichannel digital information, as well as give practical examples to show the application and rationality of the proposed techniques.

In this paper, a sampled-data fuzzy controller is designed to stabilize a class of chaotic systems. A Takagi-Sugeno (T-S) fuzzy model is employed to represent the chaotic systems. Based on this general model, the exponential stability issue of the closed-loop systems with an input constraint is first investigated by a novel time-dependent Lyapunov functional, which is positive definite at sampling times but not necessary between the sampling times. Then, two sufficient conditions are developed for sampled-data fuzzy controller synthesis of the underlying T-S fuzzy model with or without input constraint. All the proposed results in this paper depend on both the upper and lower bounds on a sampling interval, and the available information about the actual sampling pattern is fully utilized. The proposed sampled-data fuzzy control scheme is successfully applied to the chaotic Lorenz system, which is shown to be effective and less conservative compared with existing results.

In this paper, novel stability conditions for Takagi-Sugeno (T-S) fuzzy systems are presented. The so-called nonquadratic membership-dependent Lyapunov function is first proposed, which is formulated in a higher order form of both the system states and the normalized membership functions than existing techniques in the literature. Then, new membership-dependent stability conditions are developed by the new Lyapunov function approach. It is shown that the conservativeness of the obtained criteria can be further reduced as the degree of the Lyapunov function increases. Two numerical examples are given to demonstrate the effectiveness and less conservativeness of the obtained theoretical results.

This paper is concerned with the problem of adaptive neural tracking control for a class of uncertain pure-feedback nonlinear systems. Using the implicit function theorem and backstepping technique, a practical robust adaptive neural control scheme is proposed to guarantee that the tracking error converges to an adjusted neighborhood of the origin by choosing appropriate design parameters. In contrast to conventional Lyapunov-based design techniques, an alternative Lyapunov function is constructed for the development of control law and learning algorithms. Differing from the existing results in the literature, the control scheme does not need to compute the derivatives of virtual control signals at each step in backstepping design procedures. Furthermore, the scheme requires the desired trajectory and its first derivative rather than its first n derivatives. In addition, the useful property of the basis function of the radial basis function, which will be used in control design, is explored. Simulation results illustrate the effectiveness of the proposed techniques.

This paper investigates the problem of sampled-data stabilization for neural-network-based control systems with an optimal guaranteed cost. Using time-dependent Lyapunov functional approach, some novel conditions are proposed to guarantee the closed-loop systems exponentially stable, which fully use the available information about the actual sampling pattern. Based on the derived conditions, the design methods of the desired sampled-data three-layer fully connected feedforward neural-network-based controller are established to obtain the largest sampling interval and the smallest upper bound of the cost function. A practical example is provided to demonstrate the effectiveness and feasibility of the proposed techniques.

This paper is concerned with the problems of passivity and passification for a class of uncertain switched systems subject to stochastic disturbance and time-varying delay. The passivity property is adopted to analyze the influence of the external disturbance on such systems to achieve prescribed attenuation levels. Based on average dwell time approach, free-weighting matrix method, and Jensen's integral inequality, delay-dependent sufficient conditions are obtained in terms of linear matrix inequalities, which ensure the uncertain switched stochastic time-delay system to be robustly mean-square exponentially stable and stochastically passive. Then, the switched passive controllers are synthesized by linearization techniques. Finally, two numerical examples are given to illustrate the effectiveness of the proposed methods.

This paper is concerned with the problem of induced l2 filter design for a class of discrete-time Takagi-Sugeno fuzzy Itô stochastic systems with time-varying delays. Attention is focused on the design of the desired filter to guarantee an induced l2 performance for the filtering error system. A new comparison model is proposed by employing a new approximation for the time-varying delay state, and then, sufficient conditions for the obtained filtering error system are derived by this comparison model. A desired filter is constructed by solving a convex optimization problem, which can be efficiently solved by standard numerical algorithms. Finally, simulation examples are provided to illustrate the effectiveness of the proposed approaches.

This paper is concerned with analyzing input-to-state stability (ISS) for a class of switched nonlinear systems with time delays under asynchronous switching. Due to the existence of switching delay, the switching of the controller does not coincide accurately with the switching of the system.When the subsystem is stabilized with the matched controller, the subsystem is ISS; otherwise, the subsystem may be not ISS. We establish efficient condition, in terms of an upper bound on the switching delay, and in terms of a lower bound on the matched time intervals for the subsystem and the controller, which ensures ISS for the whole switched nonlinear system. Finally, an illustrative example is presented to demonstrate the efficacy of the results.

In this paper, the problem of sampled-data synchronization for Markovian jump neural networks with time-varying delay and variable samplings is considered. In the framework of the input delay approach and the linear matrix inequality technique, two delay-dependent criteria are derived to ensure the stochastic stability of the error systems, and thus, the master systems stochastically synchronize with the slave systems. The desired mode-independent controller is designed, which depends upon the maximum sampling interval. The effectiveness and potential of the obtained results is verified by two simulation examples.

This paper is concerned with the network delay compensation problem for nonlinear networked control systems (NCSs). By taking full advantage of the characteristics of the packet-based transmission in NCSs, new network delay compensation approaches are proposed to actively compensate the network communication delay under the fuzzy control framework. The nonlinear plant is represented by a Takagi-Sugeno fuzzy model, and the predictive control input packets are constructed based on parallel distributed compensation technique. Both state and output feedback fuzzy delay compensation controllers are designed. Finally, two examples are provided to illustrate the effectiveness and applicability of the developed techniques.