Dynamic positioning (DP) stands as the keystone underpinning ships’ operations in the abyssal depths and remote oceans. This paper proposes an event-driven disturbance rejection approximate optimal dynamic positioning scheme for surface ships with prescribed performance via reinforcement learning (RL). Firstly, a disturbance observer is established to achieve the online estimations of marine environmental disturbances such that the undesirable disturbance effects on control performance can be reduced. Meanwhile, the positioning error transformations with the prescribed performance function is established to combine with the backstepping method and the virtual controller can be designed to constrain the positioning error. Then, for the velocity error surface, the RL method provides the actor-critic architecture to approximate the actual optimal commanded control law and minimized the specified performance index function. Subsequently, the optimal commanded control design incorporates the event-driven mechanism to avoid communication resource waste as well as actuator wear and tear. By applying the Lyapunov stability theory, it is proven that the designed RL control law forces the ship’s actual position and yaw angle to converge to desired target with prescribed performance while ensuring all signals in the closed-loop system are uniformly ultimately bounded. The feature of this paper is the reasonable integration of prescribed performance and RL, which ensures that the performance index function of the ship is minimized while keeping the DP error within the safety range. Finally, simulations with comparisons based on a sea launch ship show the effectiveness further validating the superiority of the RL control scheme.

This article delves into an optimal fixed-time tracking control scheme for human-in-the-loop (HiTL) multiagent systems (MASs) against actuator faults. For adapting various complex environments, an HiTL optimal control protocol is developed based on the simplified optimal control. Furthermore, the synchronization error containing the leader input is embedded into the cost function, which enables the achievement of the optimal control objective and ensures the execution of the tracking control under the HiTL control. By adding exponential terms, a novel reinforcement learning (RL) algorithm satisfying the fixed-time form is proposed to attain the optimal fixed-time controller, which prompts the convergence rate of system signals while ensuring minimum energy consumption effectively. Meanwhile, actuator faults are considered and compensated in the controller design process to attain exceptional system performance. Consequently, the presented optimal control scheme ensures that all signals of the closed-loop system maintain bounded in a fixed time. The simulation results verify the feasibility of the presented control method.

The topology of a communication system is crucial in determining data transmission. Although significant research has been conducted on the integration of control and communication, existing studies on communication for control systems predominantly emphasize control aspects and warrant further exploration. Furthermore, there is a lack of research on the impacts of topology changes on control systems. This article aims to establish a connection between control and communication via communication topology, examining how communication topologies affect controllers. This article also analyzes the relationship between communication and control in depth. For static topologies, specific controller forms are derived from a general controller to illustrate the impacts of static topologies on controllers. In dynamic topologies, communication is nondeterministic, so whether a controller can receive data from other nodes is nondeterministic. Therefore, controller forms in which some coefficients are random variables following a probability distribution are derived. We utilize them to establish a close connection between control and communication. Furthermore, extensive simulations are conducted to investigate the impact of different topologies on the control system.

A novel thruster fault detection (FD) strategy is investigated in this article for unmanned marine vehicles (UMVs) under external disturbances and aperiodic denial-of-service (DoS) attacks. To address the challenge of the inability to timely detect DoS attacks, the UMV under consideration and its corresponding filters are initially modeled under the framework of an asynchronous switched system. Then, sufficient conditions guaranteeing the system to be exponentially stable and with the prescribed performances are derived by leveraging the model-dependent average dwell time (MDADT) and piecewise Lyapunov functions (PLFs). Simultaneously, the lower bound of the tolerable sleep interval and the upper bound of the DoS attack interval are rigorously calculated. The design criteria of the FD filters are subsequently obtained through the employment of some decoupling techniques. Finally, the simulations on a UMV demonstrate the effectiveness of the proposed methods.

This paper presents a fuzzy event-driven optimal quantized feedback control strategy for unmanned surface vehicle (USV) trajectory tracking under multiple practical constraints, including limited maritime communication resources, model uncertainties, thruster saturation, and positioning constraints. To address the communication limitation, a signal quantization and event-driven mechanism is introduced, where the thruster quantization is characterized by a linear analytical model. A fuzzy adaptive observer (FAO) is used to estimate the quantized feedback signal, while a finite-time disturbance observer (FDO) estimates environmental disturbances. Additionally, a dynamic auxiliary system compensates for thruster saturation effects. A logarithmic barrier function-based constraint mechanism ensures positioning variables remain within predefined safety bounds. The adaptive quantized feedback controller is designed using the backstepping framework and dynamic surface control techniques, with an optimal compensation term based on adaptive dynamic programming (ADP) to minimize the cost function of the tracking error system. The proposed control strategy ensures that all closed-loop signals remain within specified limits. Simulation results confirm the effectiveness and advantages of this approach.

Frequency stability is vital for the reliable operation of critical equipment in power systems. However, increasing renewable integration and unpredictable actuator faults hinder traditional controllers from achieving frequency regulation within a fixed and predetermined time. To overcome such a challenge, a novel resilient decentralized fixed-time dynamic feedback controller is proposed for multi-area power systems subject to non-homogeneous Markovian jumps, actuator faults, and load fluctuations. In contrast to existing fixed-time control approaches dependent on state change rates, the proposed dynamic controller is uniquely governed by both control amplitude bounds and quadratic state terms. The designed controller rigorously guarantees both the stochastic fixed-time stability and the L∞ performance despite simultaneous actuator failures and non-homogeneous parameter jumps. By applying Lyapunov differential inequalities and set measure theory, a tractable design criterion is established to facilitate the solution of desired gain matrices, ensuring scalability and plug-and-play functionality. A three-area power system is finally utilized to evaluate the effectiveness of the developed control strategy regarding fixed time convergence and resilience against load fluctuations and actuator faults.

This article investigates the fully distributed adaptive fault-tolerant cluster consensus tracking problem of multiagent systems (MASs) under prescribed performance conditions. First, a novel multihop neighborhood partition approach is proposed, enabling fully distributed control without relying on global Laplacian information. Then, an improved prescribed performance method is developed, which integrates the spatial information of agents into the performance function through function mapping. Unlike traditional prescribed performance methods, the proposed performance function accounts for the relative position information of agents during operation, ensuring that the tracking error between the leader and followers remains consistently negative. Furthermore, the demands of transient and steady-state performance of the system increase the consumption of communication resources. Therefore, a dynamic preset boundary event-triggered mechanism (DPETM) is proposed, which dynamically adjusts the triggering conditions based on the tracking error and predefined performance boundaries. Additionally, an adaptive command filter error compensation system is constructed to eliminate the differential explosion caused by higher-order derivatives of virtual control signals. Finally, a simulation case is presented to demonstrate the validity of the proposed control approach.

Concerning the stability analysis for systems with time-varying delays, a negative-determination method for a class of generalized-convex (generalized-concave) matrix-valued polynomial function is proposed, which is an extension of real-valued quadratic function negative-determination lemma. Initially, the definition of generalized-convex matrix-valued function is presented, and its first-order and second-order determination conditions are established. Subsequently, negative definite sufficient conditions for generalized-convex (generalized-concave) matrix-valued polynomials with high degrees are developed. Further, stability criteria with less conservatism for linear time-delay systems are constructed by applying the proposed method. Finally, a numerical example and a load frequency control example are presented to verify the feasibility and the advantage of the designed method on achieving less conservative stability criterion.

This article investigates the multitarget dynamic encirclement control for multiple autonomous surface vehicles (ASVs). The ASVs are subjected to unavailable velocity state information, finite communication resources, saturated actuators, model uncertainties, and environmental disturbances. The control objective is to enable an ASV team to perform encirclement maneuvers while tracking the convex combination of the states of multiple targets. Specifically, an adaptive fuzzy state observer (AFSO) is developed to estimate the unavailable velocity state information. Next, the system is divided into two regions to avoid singularities. A time-varying terminal sliding mode controller and a time-varying auxiliary sliding mode controller are designed for each region. By applying finite-time control theory, the multitarget dynamic encirclement can be achieved within a finite time. Moreover, a dynamic event-triggered mechanism (DETM) is proposed to optimize the use of communication resources. Finally, the validity of the obtained theory results is demonstrated through the Lyapunov analysis and numerical simulation.

This study investigates reinforcement-learning-based fixed-time optimal formation control for multiple nonholonomic mobile robots with prescribed performance constraints. First, the constrained formation error dynamics is established using a leader-follower approach. Meanwhile, a barrier function is employed to transform the constrained formation error dynamics into an unconstrained form. Then, an adaptive control technique and a critic-only reinforcement learning strategy are utilized to design a fixed-time optimal control law for the unconstrained error dynamics. Rigorous theoretical derivations demonstrate that the proposed control law guarantees that the constrained formation error converges to near zero within a fixed time, optimizing the performance index while satisfying the prescribed performance requirement. Finally, the feasibility of the proposed method is verified through simulations and experiments.

This paper investigates the safety-certified cooperative path following problem for second-order nonlinear systems with multiple-input multiple-output strict-feedback form subject to safety, state, and input constraints. A safety-certified learning control approach is proposed to achieve collision-free cooperative path following based on command optimization, online learning, and self-triggered communication. Specifically, an extended-state-observer-aided learning neural predictor is developed to simultaneously identify nonlinear functions and unknown input gains without measuring state derivatives. Control barrier functions are then employed to ensure safety through forward invariant sets. Next, command optimization is utilized to generate the optimal virtual control signals that satisfy safety constraints, state constraints, and input constraints. A neurodynamic optimization technique is employed to solve the quadratic optimization problem in real time. Additionally, a self-triggered mechanism is introduced in path variable coordination to reduce the listening and triggering times. By using the proposed safety-certified cooperative path following approach, a safe formation is guaranteed for input-to-state safety. Simulation results are provided to illustrate the effectiveness of the proposed method.Note to Practitioners—This paper addresses the safety-certified cooperative path following problem of multi-agent systems, which has practical implications in various applications. These applications include formation patrol, cargo transportation, search and rescue missions, swarm robotics, and agricultural tasks. By coordinating the movements of multiple agents, cooperative path following enhances efficiency in these scenarios. The challenges posed by safety, state, and input constraints are commonly encountered in practical. To address these challenges, the command optimization approach is proposed in this paper. The optimization problem is efficiently solved in real-time using neurodynamic optimization technique. Furthermore, to enhance feasibility in a limited communication environment, a self-triggered mechanism is introduced to reduce the communication burden. Therefore, the aforementioned effective scheme is suitable for implementation in industrial applications.

This article proposes the dynamic event-triggered and self-triggered protocols to achieve the prescribed-time consensus of a class of nonlinear multi-agent systems with a trade-off performance. Firstly, a dynamic event-triggered control protocol and a time-varying parameter are jointly designed to accomplish the prescribed-time consensus. Secondly, a dynamic self-triggered control protocol is further designed to avoid continuous monitoring of the system states. The designed control protocols both can ensure the avoidance of the Zeno phenomenon and allow for dynamic adjustment between the specified time and the inter-event time. Specially, the minimum inter-event time can be designed as an arbitrarily large bounded constant when every agent is an integrator system. Finally, an example system is utilized to demonstrate the effectiveness of the designed algorithms.

The interval observer, recognized as a potent tool for fault detection (FD), diverges from the adoption of fixed thresholds to enhance the timeliness and precision of detection. This study endeavors to design an FD mechanism for fuzzy cyber-physical systems (CPSs) subjected to adversarial influences based on improved attack tolerant interval observer. Specifically, the proposed frequency-information-based unknown input interval observer (UIIO) not only enables a more precise estimation of the interval range under the influence of attack signals but also isolates unknown decoupled inputs. Furthermore, it facilitates specific performance design for signals within a particular frequency range. Following the stability analysis of the system, linear solvable conditions are presented to ensure the robustness, fault sensitivity of the augmented system, as well as non-negativity of the augment system matrix. Finally, the proposed attack-tolerant FD mechanism is validated through simulation examples, including the influence of the prior attack information to detection interval width and the detection performance for small-amplitude faults or faults with specific frequency characteristics.Note to Practitioners—CPSs constitute the core of the next-generation manufacturing industry, applicable in areas like unmanned autonomous systems, smart grids, and intelligent healthcare, capitalizing on wide-ranging cyber space utilization. Nonetheless, this breadth also exposes them to elevated risks, including the threat of actuator attacks. The novel fault detection scheme developed in this paper is capable of operating in real-time when the system is confronted with various threats such as disturbances, faults, and actuator attacks. This approach obviates the requirement for knowledge about the nature of actuator attack signals, while enabling the generation of a system state estimation interval with a tunable error range. Leveraging frequency-dependent techniques, the developed scheme, even in the presence of potential actuator attack and disturbance, is particularly well-suited for the detection of fault signals with specific frequency domain characteristics.

The cooperative control problem for cyber-physical systems under actuator faults and denial-of-service attacks is investigated, where each subsystem can be modeled by an agent and denial-of-service attacks are viewed as attacks against the inter-agent communication. Specifically, a scenario is considered in which the communication topology loses connectivity following a denial-of-service attack. To address the above problem, a fully distributed secure control strategy is proposed. This strategy combines a distributed observer with an observer-based fault-tolerant consensus control method. Therein, the assumptions that the leader’s communication links are unbreakable and the switched topology contains a spanning tree are further relaxed based on the idea of a jointly connected topology. In addition, this paper also has the following features: Firstly, the proposed scheme eliminates the need for all agents to have prior knowledge of the leader’s dynamics, making the scenario considered in this work more general and applicable. Secondly, the control method presented in this paper does not rely on any global topology information, allowing it to be implemented in a completely distributed manner. Building on the proposed scheme, even under denial-of-service attacks and actuator faults, the consensus problem for cyber-physical systems can be realized. Finally, simulations are provided to demonstrate the effectiveness of the proposed method.

This paper investigates the problem of practical prescribed-time fuzzy bipartite time-varying formation control for nonlinear multiagent systems. To achieve practical prescribed-time control, a bounded and continuous prescribed-time evolution function is constructed, and a new stability lemma is derived, proving that the proposed controller avoids singularities and ensures synchronization errors converge to a neighborhood independent of initial conditions within the prescribed time. Considering cooperative-competitive interactions among the agents, a fuzzy bipartite time-varying formation controller is designed to achieve formation control within a specified time while keeping all closed-loop signals bounded. Furthermore, to reduce communication costs, an adaptive self-triggered mechanism is introduced. This mechanism maps the prescribed time to a dynamic triggering function, enabling the controller to adjust triggering conditions based on the user-defined convergence time, thereby balancing speed and communication costs. Finally, the effectiveness of the proposed method is validated through a simulation case involving five unmanned ground vehicles.

This paper mainly focuses on secure fault-tolerant control for nonlinear cyber-physical systems under the influence of multiple threats, i.e., sensor and actuator saturations, actuator fault, and intermittent denial-of-service attacks. A novel secure fault-tolerant control method is proposed within the Takagi-Sugeno fuzzy modeling framework, considering both saturation constraints and intermittent denial-of-service attacks, to ensure the secure performance of cyber-physical systems under multiple threats. By leveraging the advantages of the proposed method, the influence of outdated information caused by denial-of-service attacks is mitigated, and more precise state/fault estimations are obtained. Furthermore, the method guarantees the stability of both the estimation error system and the closed-loop system, even in the presence of intermittent denial-of-service attacks and saturation constraints. Finally, the effectiveness of this method is validated through experiment.

This paper proposes a resilient output-feedback frequency regulation mechanism for multi-area power systems, ensuring secure and stable operation. Firstly, the uncertainties introduced by state-of-charge (SOC) variations are considered for the addressed system involved electric vehicle (EV). Subsequently, the behavior of hybrid replay-DoS attacks is taken into account, where a non-homogeneous Markov jump framework is employed to capture both stochastic transitions and temporal dependencies of attack behaviors, and a decentrilized resilient controller is designed to mitigate these adverse effects. To facilitate controller synthesis, a refined model of the lossless power network is further developed by equivalently transforming tie-line power deviations in specific areas, ensuring compliance with tie-line power constraints while preserving system controllability. Based on this model, a comprehensive analysis of system dynamics is performed, and a set of delay-dependent, computationally tractable criteria is derived for controller gain selection. Finally, extensive simulations on three-area power systems validate the effectiveness of the proposed strategy in maintaining frequency stability, mitigating SOC uncertainties, and counteracting hybrid replay-DoS attacks.

This article investigates the observer-based human-in-the-loop (HiTL) optimal output cluster synchronization control problem for nonlinear multiagent systems (MASs). First, the leader is designed to be nonautonomous, with the unknown time-varying input monitored by the human operator directly. To address the problem that leader's output is not available to each follower, an observer is designed. This observer features practical prescribed-time convergence, and independence of prior knowledge of leader's input. Then, an augmented system consisting of observer dynamics and follower dynamics is constructed and a cost function is formulated. Accordingly, the HiTL optimal output cluster synchronization control problem is transformed into a solution to the Hamilton-Jacobian-Bellman equation (HJBE). Subsequently, the off-policy reinforcement learning algorithm is utilized to learn the solution to HJBE without complete knowledge of the system dynamics. To alleviate computational burden, the single critic neural network (NN) is employed for the algorithm implementation, with the least square method applied for training the NN weights. Finally, the simulation results are presented to verify the validity of the designed control scheme.

This article investigates the distributed resilient energy management (EM) strategy for the seaport microgrid under stealthy attacks. First, based on an analysis of seaport microgrid characteristics, we construct an EM model that aims at minimizing both operating cost and security defense resource (SDR) cost. Second, we present a distributed, resilient strategy by defining node security levels and establishing dynamic security intervals. We prove that the gap between the obtained feasible solution and the optimal one is bounded. The designed strategy is capable of tolerating the effect of the unlimited number of stealthy attacked nodes on the seaport microgrid. In addition, given the limited SDRs for the resilient EM of the island seaport microgrid, a distributed mechanism for searching the minimum security connected dominating set (MSCDS) is proposed to minimize the size of trusted nodes. Finally, simulation results demonstrate the effectiveness of the proposed strategy. Note to Practitioners: This article addresses the vulnerability of the seaport microgrid, a critical issue that impacts EM and disrupts seaport operations. Current approaches to seaport EM do not account for potential attacks. Meanwhile, existing methods for attack resilience often overlook the costs of security resources. We propose a new approach for the distributed and resilient EM of the island seaport microgrid. Secure operation is achieved by protecting the fewest trusted nodes, thereby conserving SDRs. We then show how this algorithm (searching the MSCDS) can be efficiently designed. Preliminary simulations indicate its feasibility, though it has yet to be tested in a production environment. Future research will focus on designing trusted nodes within dynamic topologies.

In this article, a model-free reinforcement learning (RL) approach is proposed for solving the optimal consensus control issue of nonlinear discrete-time multiagent systems with input constraint. To address the challenge of solving the coupled discrete Hamilton-Jacobi-Bellman (HJB) equation, a RL approach based on actor-critic framework is proposed for optimal consensus control. A well-defined cost function is designed, and the actor and critic networks are updated through online learning to obtain the optimal controllers. Furthermore, the actuator's performance is often limited due to physical constraints. To address such actuator constraints, a gradual transition control (GTC) method is proposed, and update-free and update-weak policies are introduced to further optimize network performance. Additionally, in real-world distributed systems, the actor-critic networks deployed in each agent rely on data from neighboring agents, which necessitates addressing the issue of distributed synchronization. To address this challenge, the synchronization blocking method is designed, which designs additional control signals for each agent to handle these issues. Finally, two simulations under different scenarios are presented to verify the effectiveness of the proposed approach.

The prescribed-time output regulation problem is investigated for a class of uncertain nonlinear multiagent systems (MASs) subject to limited communication resources. To address this challenge, an event-based distributed neuro-adaptive prescribed-time control scheme comprising distributed prescribed-time observers, a dynamic event-triggered mechanism (DETM), and neuro-adaptive prescribed-time controllers is developed. Specifically, a distributed prescribed-time observer is constructed for each agent using event-based communications to estimate the states of the exosystem. The constructed observer operates without requiring prior knowledge of the exosystem dynamics or global information, ensuring that observation errors converge to a small neighborhood around zero within a user-determined time interval. Additionally, the incorporation of the DETM guarantees a positive lower bound on the interexecution intervals, thereby alleviating the communication demands. Building on this observer, neuro-adaptive prescribed-time controllers are derived for each agent, capable of maintaining the system states within a user-defined compact set without the need for prior knowledge of the initial system states. It is demonstrated that the regulated outputs converge to a region arbitrarily tuned by the user within a prescribed time, with all signals remaining bounded and Zeno behavior eliminated. Finally, two examples are exhibited to verify the effectiveness of the obtained results.

This article investigates the event-triggered$\mathcal {H}_{\infty }$filtering problem for interval type-2 fuzzy networked control systems with data loss. The goal of this article is to design an event-triggered fuzzy filter with adjustable gains to further improve the$\mathcal {H}_{\infty }$performance of the filtering error system (FES) while saving communication resources. First, an adaptive event-triggered mechanism (ETM) that utilizes the average filtering error to adaptively adjust the triggered threshold is proposed to mitigate network congestion. In contrast to the existing filtering achievements, the proposed adaptive ETM considers the impact of filtering error on data transmission which can achieve the effect of saving more communication resources. Then, an interval stability theory is applied to adjust the filter gains for achieving the purposes of changing the convergence speed of the FES and improving the$\mathcal {H}_{\infty }$performance. Moreover, under the conditions of imperfect communication links, sufficient criteria for designing the$\mathcal {H}_{\infty }$filter with adjustable gains are obtained based on the interval stability theory and linear matrix inequalities technique. Finally, the simulation results are utilized for demonstrating the feasibility of the proposed method.

This paper investigates the problem of secure control for input saturated nonlinear cyber-physical systems (CPSs) subject to faults and multi-channel non-periodic denial-of-service (DoS) attacks. First, the model is reconstructed where the nonlinear characteristics can be described by using the Takagi-Sugeno (T-S) fuzzy modeling technique. Then, with the help of the sampling mechanism and the anti-windup compensator, a sampled data-based anti-windup fault-tolerant control scheme with membership function mismatch is proposed. This scheme effectively mitigates the impact of faults while alleviating performance degradation caused by input saturation. Furthermore, to address the challenges posed by multi-channel non-periodic DoS attacks, a resilient observer-based anti-windup secure fault-tolerant control strategy is further proposed to guarantee system stability. Finally, the simulation results are also given to verify the validity of the proposed method.

This article presents an adaptive prescribed-time dynamic surface control approach for a differential-driven unmanned surface vehicle (USV) with fully unknown parameters, which is a cascaded control system consisting of kinematic, kinetic, and motor-driven propeller layers. First, the control design of the kinematic layer determines the virtual surge and yaw velocities, which are filtered as the pseudocommands for the kinetic layer. By imposing a performance function for the position error of the USV, it can be driven into the preset precision region within the prescribed time. Second, the virtual force control laws designed in the kinetic layer give the desired motor angular speed commands, which are filtered as the pseudocommands for the propeller layer. Then, in the control design of the propeller layer, the actual command duty cycles are proposed to make the actual motor angular speed follow these pseudospeed commands of motors to apply in practice directly. By employing the fuzzy logic system to approximate the nonlinearities, we can establish a simpler control structure than the existing disturbance-observer-based controller due to the avoidance of virtual control laws and the adaptive compensations of modeling uncertainties. Finally, experimental results show that the proposed method makes the tacking task successful with user-defined performance.

Accurate performance and low energy consumption are essential for trajectory tracking control of an underactuated surface vessel (USV) to satisfy marine operation requirements. This paper studies the user-specified optimal tracking control problem for the USV in the presence of modeling uncertainties. A fuzzy weights-based adaptive reinforcement learning (RL) control scheme is developed to achieve the tracking control with the predefined convergence time and steady-state accuracy while minimizing the performance index function. The constructed performance index function includes the surge velocity control law and the constrained position tracking error. Following that, an optimal backstepping design frame is proposed to derive the controllers with unknown gradient terms by constructing the error subsystems at the kinematic and kinetic levels of the USV. Then, a fuzzy reinforcement learning algorithm (FRLA) is presented to derive the optimal control solutions of corresponding error subsystems. Since the fuzzy basis function vectors derived from measured states of the USV system may be zero vectors, the persistence excitation condition may not always be satisfied. Thus, in the design of fuzzy RL updating laws of critics and actors, a positive parameter compensation technique is proposed to make the matrixes consist of fuzzy basis functions strictly positive definite. It releases the need for assumption of persistence excitation, and the convergence of the critic weight estimate and the boundedness of closed-loop systems can be guaranteed. Finally, the effectiveness of the presented method is demonstrated by numerical simulations and real-world experiments.

In the quadrotor-suspended-payload system, simultaneously achieving trajectory tracking and payload anti-swing control is essential but challenging. We reveal that the fast maneuver required by the anti-swing control may not be effectively executed by the classic quadrotor dual loop control framework, due to the inherent non-ideal inner loop actuation bandwidth in practice. To address this issue, we propose a novel hierarchical control framework incorporating the uncertainty and disturbance estimator (UDE)–based backstepping control. The proposed framework consists of three loops, where the outer loop tracking and anti-swing controller and the inner loop attitude controller are both designed in the conventional way, and more importantly, a newly proposed intermediate loop is induced between them. In the intermediate loop, the robust dynamic compensator is developed based on an experimentally verified actuator model to extend the quadrotor actuation bandwidth, such that the outer loop control bandwidth requirements are quantitatively satisfied. Furthermore, the system stability is proved by the Lyapunov method along with the system performance analysis based on the singular perturbation theorem. Finally, comparative simulation and experimental results are presented to demonstrate the feasibility and effectiveness of the proposed controller.

The prescribed-time (PT) human-in-the-loop (HiTL) optimal synchronization control problem for multiagent systems (MASs) under link-based denial-of-service (DoS) attacks is investigated. First, the HiTL framework enables the human operator to govern the MASs by transmitting commands to the leader. The link-based DoS attacks cause communication blockages between agents, resulting in topology switching. Under the switching communication topology, a fully distributed observer is proposed for each follower, which simultaneously integrates a prescribed finite-time function to estimate the leader's output within the PT. This observer is characterized by a bounded gain at the PT point and guarantees global practical PT convergence, while avoiding the use of global topology information. By combining the follower dynamics with the proposed observer, an augmented system is developed. Subsequently, the model-free Q-learning algorithm is used to learn the optimal synchronization policy directly from real system data. To reduce computational burden, the Q-learning algorithm is implemented using a single critic neural network (NN) structure, with the least-squares method applied to train the NN weights. The convergence of the Q-functions generated by the proposed Q-learning algorithm is proven. Finally, simulation results verify the effectiveness of the proposed control scheme.

In this article, the problem of adaptive event-triggered tracking control is investigated for a class of nonlinear multiagent systems (MASs) with asymmetric multi-information-related (MIR) constraints on all states. The fuzzy-logic systems (FLSs) are utilized to model the system unknown items by virtue of their universal approximation properties. The appropriate integral barrier Lyapunov functions (IBLFs) are selected to prevent the states from exceeding the asymmetric constraint boundaries associated with multi-information, which include historical states, time and neighbor outputs. The event-triggered mechanism (ETM) with varying threshold is employed to reduce the update frequency of the controller, thereby achieving the purpose of saving network resources, including communication bandwidth and computation abilities. Under the backstepping technique framework, the required control scheme is designed by integrating the adaptive controller with triggering mechanism. And it is proven that the controlled plant with state constraint conditions is stable, the consensus tracking errors can eventually remain near the origin, and the Zeno behavior does not exist. Finally, the simulation results corroborate the view that the designed control scheme is effective.

There exist general model uncertainty and shutoff deadzone in hydraulic-driving plant due to unknown model parameters of mechanical structure and physical feature of electrohydraulic actuator, which will degrade the motion performance and stability. In this study, a fixed-time sliding mode adaptive control is presented in 2-DOF hydraulic manipulator to address these issues. First, the dynamic manipulator with two degree-of-freedom joint driving by hydraulic servo valve is setup via Lagrangian method. Then, a sliding mode disturbance observer is designed to approximate uncertain nonlinearity to ensure the lumped uncertainties convergence in a practical finite time. Furthermore, a parametric adaptive estimation is used to estimate unknown shutoff deadzone parameters. According to backstepping technique, a fixed-time convergence control is adopted to ensure all the system state errors converge into a zero neighborhood in a constant time not related to any initial condition. Finally, the fixed-time sliding mode adaptive controller has been verified in an experimental bench of 2-DOF manipulator.

The leader-following consensus problem for input constrained multiagent systems is investigated in this article. First, the follower agents are divided into several clusters based on the connectivity of the communication topology. Then, a novel clustered time-varying dynamic event-triggered mechanism (ETM) with adjustable minimum interevent time (MIET) is designed for each cluster, such that the follower agents within the same cluster share a unique ETM and are not influenced by agents in other clusters. Next, an event-triggered-based bounded control protocol with a time-varying gain and a parameter scheduler is proposed for each follower. Finally, the leader-following consensus can be realized with a faster convergence rate and the frequency of controller updates can be significantly reduced. Obviously, with the proposed clustered time-varying dynamic ETM, the Zeno-free phenomenon is guaranteed and the MIET can be modified by adjusting only a design parameter. Simulation results validate the effectiveness of the designed control algorithm.

This article addresses the event-based adaptive optimal fault-tolerant consensus control problem for a class of nonlinear multiagent systems (MASs) under a directed graph. It can be solved that both control gains and actuator failure parameters are unknown in considered MASs, which enhances the practicability of optimal consensus control. First, a reinforcement learning-based distributed optimal control law is designed by constructing the identifier-critic-actor learning networks. Furthermore, by utilizing the distributed optimal control law as an auxiliary variable, an adaptive fault-tolerant controller is proposed to effectively compensate for actuator failures. Meanwhile, a co-design scheme is proposed for the construction of an event-triggered control input with the fault-tolerant property. It is proven that the designed control input ensures the boundedness of closed-loop systems through rigorous stability analysis. Finally, the effectiveness of the developed approach can be illustrated via simulations of numerical nonlinear MASs and a group of autonomous underwater vehicles.

This article mainly focuses on the problem of observer-based adaptive event-triggered security fault-tolerant control (FTC) for attacked cyber-physical systems (CPSs). First, for the nonlinear characteristics in CPSs, the model is reconstructed based on the Takagi-Sugeno (T-S) fuzzy modeling technique. Then, a T-S fuzzy resilient observer is proposed to estimate the state as well as the actuator fault information. The proposed T-S fuzzy resilient observer proactively detects sensor transmission channels affected by dynamically changing denial-of-service (DoS) attacks, and proactively isolates contaminated data so as to reduce the impact of DoS attacks on the estimation effect. Based on the above content, an observer-based adaptive event-triggered security FTC scheme is proposed, which can ensure the stability of the CPSs and save the limited network resources between the controller and the actuator. Finally, simulation results are given to verify the effectiveness of the proposed scheme.

In this article, a novel full errors prescribed-time tracking control issue is studied for strict-feedback nonlinear systems with compounded perturbations and input constraints. The conventional error constraint methods that require adjusting 2ndesign parameters to ensure all errors enter the specified accuracy within a preassigned settling time greatly increase the complexity of controller design. By blending the modified shifting function and the barrier Lyapunov function, the presented control scheme only needs to adjustnparameters to achieve full errors prescribed-time convergence. Besides, the dead-zone input is divided into a linear term and an interference-like term. A disturbance observer is devised to estimate and compensate the compounded perturbations, including external perturbations, fuzzy approximation errors and interference-like term, which significantly enhances the accuracy of the feedback compensation mechanism. It is proved that all signals of the closed-loop system are bounded. Finally, the availability of the proposed control strategy is testified via a simulation example of an RLC circuit.

In this paper, the collision avoidance process of underwater vehicles is considered, and an improved potential function is proposed to reduce the energy consumption of underwater vehicles when approaching the target position. Aiming at the negative gradient of the potential function in the controller, the potential function based on target position is proposed to prevent underwater vehicles from oscillating near the target position due to the excessive negative gradient of the potential function. Then, the Lyapunov function is used to prove that the potential function can achieve the purpose of underwater vehicles collision avoidance [1], and the improved potential function is compared with the general potential function during collision avoidance. Finally, two potential functions are compared by simulation, which shows the advantage of improving the potential energy function in reducing unnecessary energy consumption.

Accurate 6D pose estimation of objects during robot grasping is a common task in robotics. It becomes even more challenging when the texture-less objects are situated in a complex environment. We present a part-level 6D pose estimation approach, CPF6D. It takes RGB-D images as input and predicts a 6D pose for each object in the image. This method is capable of integrating the relationships between the object itself and its constituent parts. In CPF6D, a feature extracting module is specifically designed in the encoder layer to enhance the contextual features of the object parts. We use a dual-stream fusion module for each RGB-D image to fuse the RGB and depth data. Also, an optimized strategy specifically for conveyor belt robotic grasping is presented. Experiments on synthetic datasets and in a real-world scenario demonstrate that our method can obtain accurate 6D pose of each object observed and be competitive with other public methods.

With the rapid development of communication and Internet technologies, the Internet of Things (IoT) has become increasingly popular. Although IoT devices can bring more benefits to humans, there are a lot of concerns about the security and privacy of the IoT devices. Even though researchers propose some prevention and detection methods for security purposes, cyber-attacks often happen in IoT devices. Thus, we should have an accountability mechanism for the IoT devices. We should know what happens once the IoT devices have security and privacy issues. In this article, we propose an IoT system that can provide accountability. The proposed IoT system can guarantee that the communication behaviors of IoT devices and the local server can be observed and audited. IoT devices and the local server cannot record incorrect messages, deny that messages were sent out, and forge log entries without notice. We prove our proposed IoT system’s accountability property and evaluate its performance.

The model predictive control (MPC)-based asynchronous attack tolerant control scheme is investigated in this article for uncertain Markov jump cyber-physical systems (MJCPSs) under the Denial-of-Service (DoS) attack. To tackle the problem of the system running mode may not be observed in the control center, an asynchronous model predictive controller is proposed. Specifically, a dynamic controller, which can tune the performance online, is designed besides a traditional state feedback one. Even though such a combination may cause possible degradation of system performance, it can expand the initial feasible region and relieve the online computation burden efficiently. In addition, a decision variable is introduced to alleviate limitations on the feasible region generated by the constraints in the traditional MPC method. A series of solvable optimal problems are further constructed to achieve the desired performances. Finally, an application of the proposed method is given to demonstrate its effectiveness.

This article develops continuous and periodic event-triggered sliding-mode control (SMC) algorithms for path following of underactuated surface vehicles (USVs). Based on the SMC technology, a continuous path-following control law is designed. The upper bounds of quasi-sliding modes for path following of USVs are established for the first time. Subsequently, both continuous and periodic event-triggered mechanisms are considered and added into the proposed continuous SMC scheme. It is demonstrated that with appropriate selecting of control parameters, the use of hyperbolic tangent functions does not affect the boundary layer of quasi-sliding mode caused by event-triggered mechanisms. The proposed continuous and periodic event-triggered SMC strategies can make the sliding variables reach the quasi-sliding modes and stay in there. Moreover, energy consumption can be reduced. Stability analysis shows that the USV can follow a reference path by using the designed method. The simulation results show the effectiveness of the proposed control methods.

This article presents an event-triggered distributed secondary control with predictive compensation based on the model-free predictive control under Denial-of-Service (DoS) attacks in ac/dc-networked microgrids. First, models of ac/dc networked microgrids in both electric network and communication network are established. On this premise, event-triggered distributed secondary control is proposed to solve the problems of strong communication burden and low-power distribution accuracy. Besides, aiming at the impact of DoS attacks on distributed secondary control, a compensation algorithm based on model-free predictive control is designed to estimate the control variables when the DoS attack occurs, which can improve the control performance and maintain the stable operation without a specific system structure system. Then, the convergence of event-triggered distributed secondary control with the condition of whether DoS attacks happen are analyzed. Finally, the effectiveness of the proposed control is verified on the hardware-in-loop (HIL) simulation platform consisting of the RT-LAB simulator, MATLAB/Simulink simulation model, and DSP controller.

This article explores the output regulation (OR) problem for interval type-2 (IT2) switched fuzzy systems (IT2-SFSs) vulnerable to data integrity and derivative injection attacks (DIDIAs). The system under consideration allows for severe instability, where the OR of each subsystem is unsolvable and partial switching instants are destabilizing. We analyze the intricate asynchronous switching scenario under dual data derivative injection attacks (DDIAs) with duration limits. In particular, the IT2 fuzzy controller (IT2FC) acquires a false switching signal of subsystems manipulated by the data integrity attacks. Next, the event-triggered scheme with rolling detection (ETSRD) based on matching conditions of switching modalities and interval constraints of DDIAs is proposed to reduce asynchronous durations between switching subsystems and controllers while enhancing the stability of the closed-loop system. The switching rule associated with DIDIAs duration parameters is then developed to ensure exponential stability in the framework of the dwell time technique, which permits irregular destabilizing and stabilizing switching. The joint design of the IT2FC, ETSRDs, and switching rule yields a security control scheme for OR of IT2-SFSs in the presence of severe instability, DIDIAs, and packet delays. Finally, a robot model with a single-link arm is used to exemplify the proposed methodology.

This paper concerns the rate-based output regulation (OR) for switched interval type-2 (IT2) fuzzy systems (SIT2FSs) with severe instability. To expedite synchronization with the activated subsystem and more effectively mitigate of overshooting, the addressed systems need to convey the activated system modal and readily accessible state derivatives to the controller. The primary challenge in control synthesis arises from the potential impact of multiple data injection attacks (MDIAs) on networks connecting the subsystem and the controller. Such attacks can not only compromise system states and controller signals but also manipulate system modes and state derivatives, resulting in intricate asynchronous switching behavior and significant disruptions to system dynamics. To surmount this scenario, rate-based dynamic event-triggered schemes (RDETSs) are developed to shorten the asynchronous interval by introducing the dissipativity parameter and the lower bound of MDIAs' sleeping duration. Then, a switching rule is formulated to resist MDIAs and severe instability and break the binding of switching behavior between the subsystem and its controller. Based on the dissipativity theory framework, a rate-based OR control paradigm is delivered for SIT2FSs by co-designing the IT2 fuzzy derivative feedback controller, RDETSs, and switching law. The suggested methodology is validated by multi-ship formations that are represented as the addressed system adhering to a switching topology activated by restricted communication capabilities.

In this article, a novel secure fault-tolerant control (FTC) strategy is proposed to deal with the impact of multiple threats such as sparse sensor attacks, system faults, and unknown disturbances on T–S fuzzy cyber-physical systems (CPSs). First, under the assumption of 2s-detectability, a set of robust local unknown input observers is designed. Specifically, each observer can decouple partial disturbances and perform targeted suppression on undecoupling disturbances simultaneously. Next, the residual-based attack detection strategy and secure global estimation fusion mechanism are developed, leading to the estimation of the state and concerned fault with smaller estimation error. Ulteriorly, a secure fault tolerant controller is proposed to ensure that the system can recover satisfactory performance in time subjected to multiple threats. Finally, the proposed secure FTC method is applied to the control scenario of autonomous vehicles in the network environment, which proves the effectiveness of the developed technology.

This article studies the optimized backstepping consensus control problem for uncertain nonstrict-feedback nonlinear multiagent systems with intermittent actuator faults and input quantization. A fuzzy approximation-based optimal consensus control scheme is proposed via the reinforcement learning algorithm. Meanwhile, a time-varying constraint function is embedded into the optimized backstepping consensus design to guarantee the consensus errors converge to a preassigned residual set within the prescribed time. To overcome the difficulty of the virtual optimized controller design caused by unknown virtual control coefficients, a class of intermediate variables is designed. A modified variable separation technique is proposed to circumvent the problem of algebraic loop caused by the construction of virtual optimized controllers. Then, based on the reconstruction of unknown bounds, a fault-tolerant controller under the quantization action is developed to compensate for the impact of unknown actuator faults automatically, and achieve semiglobally ultimately uniformly bounded results. In the end, a practical example of a group of single-link manipulators is given to illustrate the validity of the presented method.

This paper proposes a novel dynamic eventtriggered scheme for nonlinear multi-agent systems under denialof-service (DoS) attacks via an adaptive fuzzy resilient control method. At the beginning, fuzzy logic systems are utilized to identify the unknown system dynamics. Then, a reliable attack detection mechanism forms the basis for establishing a dynamic event-triggered protocol, where dynamic parameters are introduced to adjust the threshold of event-triggered conditions. Compared with common attack detection methods relying on residuals between system and observer values, a novel and reliable mechanism for detecting DoS attacks is proposed, grounded in the logical relationship of voltage level signals derived from the outputs of detection components. Finally, a backstepping recursive design framework is utilized for constructing an eventtriggered adaptive fuzzy controller. Through the Lyapunov analysis, it is strictly demonstrated that, even under DoS attacks, the followers remain inside the leaders’ defined convex hull. The effectiveness of the presented control scheme is illustrated through the simulation results.

As the number of ships docking at ports and using onshore power grows, there is a pressing need for efficient economic dispatch within the port microgrid (PMG). The computing power network (CPN) leverages ubiquitous computing resources, presenting new opportunities for handling complex tasks, such as the economic dispatch problem (EDP) more efficiently. However, integrating CPNs into PMGs raises significant concerns about data security and privacy. To address these issues, this article proposes a distributed prescribed-time optimization (PPDPTO) algorithm specifically designed for the EDP in the integrated PMG and CPN. This algorithm ensures both high computation efficiency and enhanced privacy security. Specifically, this algorithm combines the distributed prescribed-time optimization theory and the privacy-preserving technique, which can protect the sensitive energy information of the berthing ships while ensuring fast convergence. Moreover, based on the proposed mask function protecting the power demand information of the berthing ships, the optimal solution of the EDP can be obtained with limited decoding resources. A smooth piecewise mask function is proposed to promote the nonvulnerability of the system and the bias in the optimal steady states of the proposed algorithm is reduced. Furthermore, the prescribed-time convergence of the PPDPTO algorithm is proven; meanwhile, the supply and demand balance constraints of the EDP can be guaranteed under the mask function. Finally, simulations undermine the effectiveness of the PPDPTO algorithm.

For shipboard boom cranes with the influence of ship roll motion, we develop a new nonlinear antiswing control scheme with a model predictive control (MPC) based auto-tuning mechanism. In this scheme, the superiority of MPC in handling constraints is retained in acquiring time-varying control gains. Compared with trial and error, the efficiency of searching for proper control gains can be improved dramatically while considering optimization. Specifically, by binding the ship rolling angle to the original state variables of shipboard cranes, a coordination transformation is first carried out to facilitate the controller design by excluding the specific expression of ship rollings. Then an energy-based nonlinear controller is proposed, and the system stability can be rigorously guaranteed based on the Lyapunov theory and LaSalle’s invariance principle. To improve the performance of the energy-based controller with gains derived through trial and error tuning, a novel MPC-based auto-tuning mechanism is further established so the optimal and time-varying control gains can be determined more effectively. The other merit of the proposed scheme lying in the state constraints and input saturation are taken into account to enhance the safe operation of shipboard cranes. Finally, numerical simulations are elaborately made, and comparison results are presented to demonstrate the superiority of the proposed scheme, especially in optimality, coordination control performance, and dealing with constraints.

Artificial intelligence and Automatic Identification Systems (AIS) play pivotal roles in intelligent maritime navigation for the modern maritime industry. Many artificial intelligence maritime applications based on AIS data have dramatically benefited traditional operations and managements in the field of maritime industry, and also provided state-of-the-art predictive analytics for vessel collisions and route optimization. However, the problem of modeling the interactions of vessels in complex waters still needs to be adequately addressed. In this paper, we focus on using spatiotemporal AIS data to model and forecast multiple vessel trajectories amid dynamic interaction patterns, and we propose a forecast model based on a novel neural network, namely a spatiotemporal multi-graph fusion network (STMGF-Net). The innovative STMGF-Net comprises three crucial modules. First, a Spatiotemporal graph construction module generates interaction graphs of various navigation modes, such as motions, risks, and attributes of vessels, Second, a multi-mode fusion module embeds and fuses the above interaction graphs into STMGF-Net. Finally, squeeze-and-excitation and temporal convolutional networks are introduced as Squeeze-and-excitation temporal convolutional modules to enhance the overall efficiency of the model significantly. Overall, the STMGF-Net can recognize complex spatiotemporal interaction patterns among neighboring vessels so as to capture and integrate these interaction features for achieving high-precision prediction performance in intelligent maritime navigation. In numerical experiments, three water areas of Zhoushan Islands, Yangshan Waters, and Yangtze River Waters are used as training and testing datasets. The results show that STMGF-Net improved prediction errors of average and final distance with increase of 49.637% and 50.622% than classic and state-of-art graph neural networks.

In this article, an adaptive reinforcement learning (RL) control problem is explored for a class of nonstrict-feedback discrete-time systems. First, different from the existing results, considering the noncausal problem which may exist in the backstepping design procedure, a universal system transformation method is first proposed for a class of nonstrict-feedback discrete-time systems. Second, by defining a compensation term to compensate the controller and utilizing the property of radial-basis-function neural network (RBFNN), an RL-based direct adaptive control strategy is developed via a backstepping method to achieve optimal control, and the multigradient recursive (MGR) algorithm is employed to estimate the weight vector. Finally, the stability of the control system is guaranteed and all signals in the closed-loop system are semiglobal uniformly ultimately bounded (SGUUB) on the basis of the Lyapunov theory. In addition, a universal system transformation is first proposed which breaks through the limitations on the controller design for the discrete-time nonstrict-feedback nonlinear system by using the traditional method. The validity of this strategy is verified by two simulation examples that include a course keeping system of the marine vessel.

To solve the tracking control problem, this article proposes a detect-switch-compensate mechanism based on fault-tolerant control strategy. Due to the existing challenge of nonlinear faults about occurrence time being unknown, a fault observer is constructed to recognize when faults occur, and the controller is activated to the fault compensate mode. Based on the above analysis, compared with the method of continuous compensation, the proposed detect-switch-compensate algorithm which uses the discontinuous compensation method having the advantage of high efficiency. Furthermore, the fault observer also solves the problem of unmeasured states. Meanwhile, both the normal tracking error and the prediction error caused by the predictor technology are considered to the feedback compensation, which prevents the system being unstable due to the large prediction error. On the basis of the Lyapunov stability, the stability of the closed-loop system is proven. A simulation example is supplied to elicit the availability of the presented methodology.

This article addresses the problem of prescribed-time optimal consensus control for a class of uncertain nonlinear multiagent systems (MASs) subject to denial-of-service (DoS) attacks over resilient event-triggered communication (RETC). First, a novel performance index function is devised to balance the convergence of consensus error and the cost of control energy, thereby achieving the optimal consensus with the preassigned steady-state precision and convergence time. Subsequently, a set of intermediate variables is established within the framework of reinforcement learning (RL)-based optimized backstepping to solve the problem of unknown control gains, which facilitates the implementation of optimal controllers. Furthermore, the designed actual optimal control law is transmitted through RETC, which provides two advanced advantages: 1) a resilient switching control protocol to avoid the impact of DoS attacks and 2) a flexible switching threshold event-triggered mechanism to conserve communication resources and balance system performance. Finally, the effectiveness of the presented approach is verified by a simulation example.

In order to estimate the unmeasurable velocities of dynamic positioning system of vessels quickly and consider reducing energy consumption, modelling errors and the ocean unknown disturbances, this paper proposes an optimal adaptive anti-disturbance constrains control scheme via finite-time velocity observer. Firstly, a fuzzy logic system is utilized to approximate modeling errors, and the finite-time velocity observer is established to obtain velocities quickly. Then, a disturbance observer is used to estimate the unknown disturbances. An auxiliary system is designed to compensate the effects of constrains. Based on the obtained velocities and disturbances information, the whole controller is composed of the adaptive anti-disturbance controller and the optimal compensation term designed by adaptive dynamic programming. It can be proved that all the signals in the closed-loop system are bounded. At Last, the simulation results verify the effectiveness of the proposed optimal control scheme.

This paper investigates the problem of attack tolerant state estimation for nonlinear Cyber-Physical Systems (CPSs). Firstly, a fuzzy system is employed to model the nonlinear CPS, and the system dynamics are re-described according to the working region, taking into account the effects of network attacks and multi-source disturbances. Inspired by previous interval observers and unknown input observers, an unknown input interval observer is proposed in this paper. The observer can isolate decoupled disturbances and the resulting interval error augment system is stable and robust to the bounded attack, with the caveat that the system matrix of the interval error augmentation system must also be non-negative. After some mathematical manipulation, these performances can be guaranteed by a set of linear matrix inequality (LMI) based solvable conditions. Finally, the effectiveness of the proposed method is verified through a set of experiments.

With the continuous development of artificial intelligence, the application of unmanned surface vessels (USVs) in the field of intelligent maritime vessels is becoming increasingly mature. Significant achievements have been made in several areas such as ocean environmental monitoring, maritime rescue, and maritime security. To further enhance the autonomous exploration capability, navigation safety, and operational efficiency of USV in route planning, this paper proposes an improved rapidly-exploring random tree* (RRT*) algorithm for USV path planning in known environments. Through simulation experiments, it has been verified that the proposed RRT* can achieve superior results in USV path planning. The proposed RRT* algorithm imposes restrictions on the sampling point range, building upon the original RRT* algorithm, resulting in more precise path planning. By comparing with the classical RRT algorithm and other traditional RRT algorithms, our approach significantly reduces the required distance for path planning while ensuring the safety of ship navigation. However, this improvement also comes with an increase in the time cost as it enhances the quality of the planned path.

Aiming at the impact of multi time scales characteristics of different energy sources in the CCHP cluster on the controller period, in this paper, a two-time-scale consensus control based on the multi-rate sampling mechanism is proposed. Firstly, a multi-rate sampling mechanism that includes a buffer and a zero order holder is established to achieve the function of controller updating as soon as the buffer receives sampled data. Due to improved data utilization, the dynamic performance of the system is better compared to the single rate sampling mechanism. Then, considering that the heating/cooling energy produced in CCHP cluster is mainly used for adjusting the indoor temperature, and electricity power mainly supplies for electrical loads. Hence, room temperature, power generation cost, and the angular frequency measuring the stability of electricity power system are taken as control objectives. The proposed two-time-scale consensus control method considering the multi time scales characteristics and coupling characteristics between different energy sources in CCHP cluster, can ensure stable and economic operation of the system. Finally, the simulation results of a CCHP cluster containing 4 CCHP devices demonstrate the effectiveness of the proposed method.

Abstract The dehiscence of the Xanthoceras sorbifolium (X. sorbifolium) may lead to seeds jump out and economic loss. The shape and the degree of the crack will provide the relevant elements for the study of the X. sorbifolium dehiscence and duly picked. Therefore, an improved level set method is proposed for X. sorbifolium crack segmentation. The problems of intensity inhomogeneity and so on, which pose challenges for accurate crack segmentation. The local Gaussian distribution fitting method has a good segmentation effect, but it is sensitive to the initial contour and does not use gradient information, which affects the accurate location of the edge. Aiming at the above problems and the scene of crack segmentation, this paper firstly adopts histogram threshold method to obtain the initial contour automatically. Secondly, the energy function is constructed by combining local and edge information. Finally, the double‐well potential function is used to reduce the oscillation and distortion of the method. In this paper, the experiment results show that the average boundary precision is 88.25% and average segmentation time of each image is 12.7s of the proposed method. After comprehensive analysis of IoU and boundary recall, the method in this paper achieves better results.

A Lyapunov-based model predictive control (MPC) scheme is firstly developed for shipboard boom cranes with ship rolling, while considering optimization and input saturation. By integrating the ship rolling into original state variables, the shipboard boom crane model is transformed to facilitate the successive controller design. A Lyapunov-based MPC framework is then constructed for the new dynamical model, while respecting actuator capability. To ensure the recursive feasibility and stability of the established framework, a contractive constraint with a bounded energy-based controller is further introduced, for which the bounds are intrinsical with the utilization of the arctan function. Stability analysis for the bounded controller is also discussed based on Lyapunov theory and LaSalle’s invariance principle. Compared with the bounded controller, optimization is taken into account by the Lyapunov-based MPC. Suboptimal solutions are also accepted to ease the computational burden. Moreover, the conservation existing in the bounded controller can be entirely circumvented by the Lyapunov-based MPC, so the capacity of actuators can be fully exploited while obeying the input saturation. Finally, numerical simulations are elaborately devised and implemented to show the effectiveness of the bounded controller and superiority of the Lyapunov-based MPC controller.Note to Practitioners—Shipboard crane is an important industrial equipment in ocean transportation and offshore engineering. Cranes are installed on ship deck, so the movements of ship, such as rolling, and pitching, may introduce difficulties, inefficiency, and unsafety to the manual operation. The conditions mentioned above motivate the study of automation control for shipboard cranes. A Lyapunov-based model predictive control (MPC) framework is constructed in this paper. Based on this framework, the payload can be conveyed to the desired location accurately and eliminate the swing quickly. Simultaneously, optimization is considered, thus, better control performance can be achieved by consuming less energy. Moreover, the physical limits of the actuators can be satisfied all the time. The present research only considers 3 degrees of freedom (DOF) shipboard boom cranes, for which the slew movement of boom and payload tangential swing are not involved. In the future research, the 5-DOF shipboard boom cranes will be studied to promote the application of the method proposed in this paper.

In this article, an adaptive fuzzy tracking control scheme based on a fuzzy state observer is proposed for a class of uncertain, nonstrict feedback, nonlinear systems with function constraints. In the first place, based on the approximation characteristic of fuzzy logic systems (FLSs), a fuzzy state observer is designed to estimate the immeasurable state variables in the controlled system. Next, under the framework of adaptive backstepping control technology, FLSs are selected not only to approximate unknown nonlinear functions but also to avoid the algebraic loop problem caused by nonstrict feedback structure. At the same time, asymmetric Barrier Lyapunov functions are selected to solve the problem that system states are subject to function constraints, which are related to states and time. Then, Lyapunov stability theory is utilized to prove the stability of the controlled system, the realizability of function constraints, and the convergence of output tracking errors. Finally, a simulation is given to check the effectiveness of the proposed control scheme.

This paper focuses on the problem of optimal fuzzy output-feedback tracking control for dynamic positioning of marine vessels simultaneously with model uncertainties, unknown disturbances, unavailable velocities and thruster saturations. The control scheme is built by integrating a fuzzy velocity observer, a finite-time disturbance observer, a dynamic auxiliary system, the optimal control strategy with the dynamic surface control. The fuzzy velocity observer is constructed to obtain the unavailable velocities without requiring the certain model dynamics. The finite-time disturbance observer is established to estimate the unknown disturbances. The dynamic auxiliary system is designed to compensate for thruster saturations effects. By incorporating these methods, a disturbance rejection adaptive controller is designed with dynamic surface control. The optimal compensation term is inserted to minimize the cost function of the tracking error system by adaptive dynamic programming. It is shown that the optimal control consisting of the adaptive controller and the optimal compensation term guarantees that all signals in the closed-loop dynamic positioning system are bounded. Finally, simulation results demonstrate the effectiveness of the proposed optimal control scheme.

In this article, an adaptive tracking control problem is addressed for nonstrict-feedback stochastic nonlinear systems subject to state constraints. Fuzzy logic systems (FLSs) are used to model unknown nonlinearities and avoid the algebraic loop arising from the system structure. Appropriate integral Barrier Lyapunov functions (IBLFs) are chosen so that time-varying full state constraints can be guaranteed directly rather than by transforming the constraint object. In the framework of backstepping technology, the relative threshold strategy is introduced to modify the adaptive control scheme so that the controller can be updated only after the trigger condition has been met, which reduces the update frequency of the controller and the loss of the actuator. Combined with Lyapunov stability theory, it is shown that all closed-loop signals are bounded in probability, in which the states remain within the specified constraints, and there is no Zeno behavior. A series of simulation results are given to reveal the effectiveness of the constructed control scheme.

This article investigates the fault detection problem of unmanned marine vehicles (UMVs) under the influence caused by replay attacks. First, the dynamics of UMV are modeled by a Takagi--Sugeno (T--S) fuzzy system with an unknown membership function, which includes the nonlinear coupling of the internal state of the system, the environmental multisource disturbance as well as the potential thruster failure on UMV. Then, the possible replay attack from the sensor to the shore-based center is considered, and a switching-type attack tolerant fault detection filter is designed. Sufficient conditions are given to ensure that the filtering augmented system is stable and with stochastic finite frequency$H_{\infty }$and$H_{-}$performances, which reflect the robustness to the disturbance and sensitivity to the fault. On this basis, through a series of mathematical processing, the linear solvable conditions for the design of fault detection filters are obtained. Finally, the effectiveness of the proposed algorithm is verified by simulations.

In this article, the tracking control problem of event-triggered multigradient recursive reinforcement learning is investigated for nonlinear multiagent systems (MASs). Attention is focused on the distributed reinforcement learning approach for MASs. The critic neural network (NN) is applied to estimate the long-term strategic utility function, and the actor NN is designed to approximate the uncertain dynamics in MASs. The multigradient recursive (MGR) strategy is tailored to learn the weight vector in NN, which eliminates the local optimal problem inherent in gradient descent method and decreases the dependence of initial value. Furthermore, reinforcement learning and event-triggered mechanism can improve the energy conservation of MASs by decreasing the amplitude of the controller signal and the controller update frequency, respectively. It is proved that all signals in MASs are semiglobal uniformly ultimately bounded (SGUUB) according to the Lyapunov theory. Simulation results are given to demonstrate the effectiveness of the proposed strategy.

The measurement delay of the feedback control system is a universal problem in industrial engineering, which will degrade output performance, especially causing undesirable chatter responses. In this study, a deep-Gaussian-process (DGP)-based method for operator's gait prediction is proposed to estimate the real-time motion intention and to compensate for the measurement delay of the inertial measurement unit (IMU). On the basis of these gait prediction uncertainties quantified by the DGP method, a variable admittance controller is designed to reduce real-time human-exoskeleton interaction torque. The reference trajectory is generated by the admittance controller, which is smoothed by the two-order Bessel interpolation. Meanwhile, the admittance parameters are self-regulated based on the defined uncertainty index of gait prediction. The extend-state observer (ESO) with backstepping iteration is adopted to compensate unmeasured system state, model uncertainties, and unmodeled dynamics of lower limb exoskeleton. The effectiveness of the proposed gait prediction and control scheme is verified by both the comparative simulations and experimental results of the human-exoskeleton cooperative motion.

This article investigates the model-free fault-tolerant containment control problem for multiagent systems (MASs) with time-varying actuator faults. Depending on the relative state information of neighbors, a distributed containment control method based on reinforcement learning (RL) is adopted to achieve containment control objective without prior knowledge on the system dynamics. First, based on the information of agent itself and its neighbors, a containment error system is established. Then, the optimal containment control problem is transformed into an optimal regulation problem for the containment error system. Furthermore, the RL-based policy iteration method is employed to deal with the corresponding optimal regulation problem, and the nominal controller is proposed for the original fault-free system. Based on the nominal controller, a fault-tolerant controller is further developed to compensate for the influence of actuator faults on MAS. Meanwhile, the uniform boundedness of the containment errors can be guaranteed by using the presented control scheme. Finally, numerical simulations are given to show the effectiveness and advantages of the proposed method.

In this article, an anti-attack event-triggered secure control scheme for a class of nonlinear multi-agent systems with input quantization is developed. With the help of neural networks approximating unknown nonlinear functions, unknown states are obtained by designing an adaptive neural state observer. Then, a relative threshold event-triggered control strategy is introduced to save communication resources including network bandwidth and computational capabilities. Furthermore, a quantizer is employed to provide sufficient accuracy under the requirement of a low transmission rate, which is represented by the so-called a hysteresis quantizer. Meanwhile, to resist attacks in the multi-agent network, a predictor is designed to record whether an edge is attacked or not. Through the Lyapunov analysis, the proposed secure control protocol can ensure that all the closed-loop signals remain bounded under attacks. Finally, the effectiveness of the designed scheme is verified by simulation results.

This article investigates the event-triggered multisource bumpless transfer (MSBT) control problem of networked switched systems (NSSs) with the almost output regulation (AOR) performance subject to switching deception attacks (SDAs). First, an improved dwell-time approach is developed to derive a proper switching law and ensure the feasibility of the AOR for NSSs allowing the AOR of each subsystem unsolvable and some switching instants destabilizing (i.e., with Lyapunov function increments), where destabilizing and stabilizing switching instants are subsystem independent. Second, a novel description of deception attacks called SDAs is established that contains multiple attack functions switching based on the currently activated subsystem, which considers the characteristics of NSSs and can cause the most severe damage to system control performance. Due to the networks and the event-triggered scheme, the switching of SDAs does not coincide with the switching of subsystems and controllers, which pose a challenge to system analysis. Third, MSBT controllers are designed to suppress the control bumps induced by multiple sources, including asynchronous controller switching, event-triggering mechanism, and SDAs. Finally, an application to a switched resistance–inductor–capacitance circuit is given to verify the effectiveness of the proposed method.

In this article, a data-driven control strategy with prescribed performance is proposed to address the distributed bipartite consensus problem for unknown nonlinear multiagent systems (MASs) undersigned directed graphs. The dynamics of the agents in each topology are completely unknown and the desired trajectory information is only communicated to a subset of agents. First, the unknown nonlinear dynamic of each agent is transformed into an equivalent time-varying linearized model by utilizing the dynamic linearization method. Then, by considering the prescribed performance, a strictly increasing function is given to transform the constrained tracking error condition of the nonlinear MASs into an equivalent unconstrained error condition. Meanwhile, a new control protocol that only uses the input and output data of the agents is designed in the case of fixed or switched topologies. The proposed method can not only guarantee the bipartite consensus but also ensure the bipartite tracking error always converges to the prescribed region. Moreover, the designed control parameters can be adjusted appropriately to obtain better tracking performance. Finally, the convergence of bipartite consensus tracking error is guaranteed through rigorous theoretical analysis. The control scheme is further verified by two examples.

In this article, an optimal control scheme against false data injection (FDI) attacks for multiagent systems with unavailable system states is presented. The output data that remains uncorrupted is extracted from a group of output measurements during FDI attacks using a secure preselector. Then, on the basis of the unattacked output data, a state observer is utilized to identify system states. By taking the communication overhead into account, an event-triggered mechanism is employed to alleviate communication burdens. The solution of the optimal control problem related to event-triggered Hamilton–Jacobi–Bellman (HJB) equations is obtained within a simplified adaptive dynamic programming (ADP) framework. The key point is that a single network is introduced to successfully avoid the problem of repeated approximations in traditional dual networks. Note that the weight vectors in the single critic network are adjusted through experience replay (ER), which helps to avoid the restrictive persistence of excitation (PE) conditions. It is strictly proven that all the signals in the closed-loop system are uniformly ultimately bounded, and the critic network weight can converge to optimal values. Simulation results illustrate the effectiveness of the control scheme.

This paper is concered with the nonlinear cyber physical system (CPS) with uncertain parameters under false data injection (FDI) attacks. The interval type-2 (IT2) fuzzy model is utilized to approximate the nonlinear system, then the nonlinear system can be represented as a convex combination of linear systems. To detect the FDI attacks, a novel robust fuzzy extended state observer with H ∞ preformance is proposed, where the fuzzy rules are utilized to the observer to estimate the FDI attacks. Utilizing the observation of the FDI attacks, a security control scheme is proposed in this paper, in which a compensator is designed to offset the FDI attacks. Simulation examples are given to illustrate the effecitveness of the proposed security scheme.

This article investigates the model-free containment control of multiple underactuated unmanned surface vessels (USVs) subject to unknown kinetic models. A novel cooperative control architecture is presented for achieving a containment formation under switching topologies. Specifically, a path-guided distributed containment motion generator (CMG) is first proposed for generating reference points according to the underlying switching topologies. Next, guiding-vector-field-based guidance laws are designed such that each USV can track its reference point, enabling smooth transitions during topology switching. Finally, data-driven neural predictors by utilizing real-time and historical data are developed for estimating total uncertainties and unknown input gains, simultaneously. Based on the learned knowledge from neural predictors, adaptive kinetic control laws are designed and no prior information on kinetic model parameters is required. By using the proposed method, the fleet is able to converge to the convex hull spanned by multiple virtual leaders under switching topologies regardless of fully unknown kinetic models. Through stability analyses, it is proven that the closed-loop control system is input-to-state stable and the tracking errors are uniformly ultimately bounded. Simulation results verify the effectiveness of the proposed cooperative control architecture for multiple underactuated USVs with fully unknown kinetic models.

This article is concerned with the quantized output-feedback control problem for unmanned marine vehicles (UMVs) with thruster faults and ocean environment disturbances via a sliding-mode technique. First, based on output information and compensator states, an augmented sliding surface is constructed and sliding-mode stability through linear matrix inequalities can be guaranteed. An improved quantization parameter dynamic adjustment scheme, with a larger quantization parameter adjustment range, is then given to compensate for quantization errors effectively. Combining the quantization parameter adjustment strategy and adaptive mechanism, a novel robust sliding-mode controller is designed to guarantee the asymptotic stability of a closed-loop UMV system. As a result, a smaller lower bound of the thruster fault factor than that of the existing result can be tolerated, which brings more practical applications. Finally, the comparison simulation results have illustrated the effectiveness of the proposed method.

This article studies an event-triggered asynchronous output regulation problem (EAORP) for networked switched systems (NSSs) with unstable switching dynamics (USDs) including all modes unstable and partial switching instants destabilization, which means that the Lyapunov function increases both on the activation intervals of all subsystems and at some switching instants. First, a memory-based mode-compared event-triggered mechanism for switched systems is proposed to effectively shorten asynchronous intervals, which employs historical sampled outputs and compares the mode of the current sampled instant and the adjacent sampled instant. Then, the maximum average dwell time for a novel switching signal is derived with a constraint on the ratio of total destabilizing switchings to total stabilizing switchings, which relaxes the requirement of the regular arrangement of destabilizing and stabilizing switchings. Moreover, with the help of different coordinate transformations in the EAORP, the discretized Lyapunov functions are no longer needed when synthesizing the NSSs with USDs, and the asynchronous switching situation is also discussed. Afterward, by designing a dynamic output feedback controller, sufficient conditions are given to solve the EAORP for NSSs with USDs subject to network-induced delays, packet disorders, and packet losses. Finally, the effectiveness of the proposed methods is verified via a switched RLC circuit.

In this article, the asynchronous fault detection (FD) strategy is investigated in frequency domain for nonlinear Markov jump systems under fading channels. In order to estimate the system dynamics and meet the fact that not all the running modes can be observed exactly, a set of asynchronous FD filters is proposed. By using statistical methods and the Lynapunov stability theory, the augmented system is shown to be stochastic stable with a prescribed$l_{2}$gain even under fading transmissions. Then, a novel lemma is developed to capture the finite frequency performance. Some solvable conditions with less conservatism are subsequently deduced by exploiting novel decoupling techniques and additional slack variables. Besides, the FD filter gains could be calculated with the aid of the derived conditions. Finally, the effectiveness of the proposed method is shown by an illustrative example.

Signed digraphs with both positive and negative weighted edges are widely applied to explain cooperative and competitive interactions arising from various social, biological, and physical systems. This article formulates and solves the asynchronous tracking control problem of multiagent systems with input uncertainties on switching signed digraphs. In the interaction setting, we assume that the leader moves at a time-varying acceleration that cannot be measured by the followers accurately, and further suppose that each agent receives its neighbors' states information at certain instants determined by its own clock, which is not necessary to be synchronized with those of other agents. Using dynamically changing spanning subdigraphs of signed digraphs to describe graphically asynchronous interactions, the asynchronous tracking problem is equivalently transformed into a convergence problem of products of general substochastic matrices (PGSSM), in which the matrix elements are not necessarily non-negative and the row sums are less than or equal to 1. With the help of the matrix analysis technique and the composition of binary relations, we propose a new and original method to deal with the convergence problem of PGSSM, and further establish a spanning tree condition for asynchronous tracking control. Finally, the validity of the theoretical findings is verified through several numerical examples.

This article proposes an adaptive neural-network command-filtered tracking control scheme of nonlinear systems with multiple actuator constraints. An equivalent transformation method is introduced to address the impediment from actuator nonlinearity. By utilizing the command filter method, the explosion of complexity problem is addressed. With the help of neural-network approximation, an adaptive neural-network tracking backstepping control strategy via the command filter technique and the backstepping design algorithm is proposed. Based on this scheme, the boundedness of all variables is guaranteed and the output tracking error fluctuates near the origin within a small bounded area. Simulations testify the availability of the designed control strategy.

In this article, a new design method based on the echo state network with time-varying reservoir parameter (TVRP-ESN) is proposed to optimize the proportional-integral-derivative (PID) controller parameters for a class of discrete-time systems with time delay. The TVRP-ESN can quickly obtain the PID controller parameters to meet the control performance of the system. According to the network learning and approximation ability of TVRP-ESN, the output weights and the reservoir parameters of TVRP-ESN can be synchronously updated, and then the TVRP-ESN can improve the convergence speed of determining the PID controller parameters. In order to update the output weights and the reservoir parameters of TVRP-ESN, the partial derivative of the system output error is used. Three simulation examples are used to show the effectiveness of the proposed method.

Multiagent systems (MASs) are distributed systems with two or more intelligent agents. Formation control is a significant control technique of MASs. To date, formation control on MASs is widely used in various fields, such as robots, spacecrafts, satellites, and unmanned aerial/surface/underwater vehicles. However, there is a relatively small body of literature that is concerned with security problems of formation control on MASs in past years. Our research represents the first step toward developing security attacks of formation control on MASs. Our study aims to investigate potential security problems of formation control on a multirobot system for the first time. We propose two kinds of control-level attacks and each kind of attack includes several specific attack forms. Then, we discuss specific features of formation control on a classical multirobot system and utilize theoretical analyses to illustrate how cyberattacks can influence the physical movements of robots. The experimental results of the proposed attacks show that attacks can easily interrupt formation movements of a multirobot system and several carefully designed attacks even can cause irreversible loss.

This article presentsan adaptive fuzzy asymptotic disturbance rejection scheme for multiagent mechanical systems with unknown model dynamics and unknown frequency disturbances. The mechanical motion equations are expressed as the parameterized equations and the disturbances are described by unknown parametric exogenous system. The unknown parametric exogenous system is transformed into the canonical model with unknown disturbances as the inputs. The disturbance filter provides estimations of the state vector in the canonical model such that disturbances are represented as parameterized forms with exponentially decaying errors. The disturbance rejection can be converted into the adaptive control. Based on the adaptive vectorial fuzzy backstepping, the disturbance rejection formation controller is designed with the fuzzy logic systems approximating the model unknown dynamics. The adaptive robust control term attenuates fuzzy approximation errors. In contrast to the existing results, the adaptive fuzzy disturbance rejection formation control realizes the estimation and rejection for unknown frequency disturbances without requiring thea prioriknowledge of model dynamics. Based on the Barbalat’s lemma, it is shown that the adaptive disturbance rejection controller achieves the asymptotic formation tracking. Application on the surface vessel formation confirms the availability.

This article investigates the containment control problem of nonlinear multiagent systems subject to actuator nonlinearity, unmeasured states, and unknown external disturbances. The predictor technique is employed for each subsystem, and the prediction error is utilized to update the adaptive law. The problem of actuator nonlinearity with dead zone and saturation input is solved by introducing an auxiliary control signal. Incorporating the state observer and disturbance ones, an extended state observer is proposed to compensate the effect of unmeasurable and unknown disturbances, simultaneously. Based on Lyapunov theory and graph theory, it is shown that the devised control scheme guarantees that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded. Moreover, the followers can enter the dynamic convex hull spanned by the dynamic leaders. A simulation example with comparison analysis is provided to illustrate the effectiveness of the theoretical results.

In this article, the adaptive neural backstepping control approaches are designed for uncertain stochastic nonlinear systems with full-state constraints. According to the symmetry of constraint boundary, two cases of controlled systems subject to symmetric and asymmetric constraints are studied, respectively. Then, corresponding adaptive neural controllers are developed by virtue of backstepping design procedure and the learning ability of radial basis function neural network (RBFNN). It is worth mentioning that the integral Barrier Lyapunov function (IBLF), as an effective tool, is first applied to solve the above constraint problems. As a result, the state constraints are avoided from being transformed into error constraints via the proposed schemes. In addition, based on Lyapunov stability analysis, it is demonstrated that the errors can converge to a small neighborhood of zero, the full states do not exceed the given constraint bounds, and all signals in the closed-loop systems are semiglobally uniformly ultimately bounded (SGUUB) in probability. Finally, the numerical simulation results are provided to exhibit the effectiveness of the proposed control approaches.

This article investigates optimal control problem for a class of discrete-time (DT) nonlinear systems with unknown dynamics. With the help of a broad learning system (BLS), a novel online adaptive dynamic programming (ADP) controller is presented. First, to approximate the unknown system dynamics, an approximator based on BLS is presented. The connection weights are calculated by the data of the system by using the ridge regression algorithm. Then, two BLSs are adopted to approximate the optimal cost function and optimal control law, respectively. The connection weights of these two BLSs are updated using the given weights tuning law at each sampling instant. The proposed optimal controller is proved to ensure that all the system states and estimation errors are uniform ultimate bounded. Finally, simulation examples are carried out to further demonstrate the effectiveness of the proposed BLS-based approximator and optimal controller.

Accurate prediction of vessel traffic flow plays a significant role in the field of modern intelligent transportation system. In order to enhance the prediction accuracy of vessel traffic flow, this paper combines genetic algorithm (GA) and Back Propagation neural network (BPNN) to build a prediction model. Based on the vessel traffic flow data of The Wuhan Yangtze River Bridge, the simulation experiments were carried out from 2013 to 2018. The average relative error of BPNN optimized by GA is 4.03%, which is better than the average relative error of direct BPNN prediction is 5.57%. The results show that accuracy of the prediction model using BPNN with GA optimization is higher than the traditional BPNN. The BPNN optimized by GA has achieved more ideal results in the forecast of vessel traffic flow. This paper provides the theoretical basis for the relevant decision-making of the water safety authorities so as to guarantee the water traffic safety.

In this paper, an adaptive neural network (NN) control scheme is developed for a class of stochastic nonlinear systems with time-varying full state constraints. In the controller design, RBF NNs are employed to approximate the unknown terms, and the backtracking technique is introduced to overcome the restriction of matching conditions. At the same time, tangent type time-varying barrier Lyapunov functions (tan-TVBLFs) are constructed to ensure the full state constraints are never violated, where tan-TVBLFs are beneficial to integrate constraint analysis into a common method. Furthermore, the Lyapunov stability theory is used to prove that all closed-loop signals are semiglobal uniformly ultimately bounded in probability and error signals remain in the compact set do not violate the time-varying constraints. A simulation example will be used to exhibit the effectiveness of the proposed control scheme.

In this article, the event-triggered output regulation problem is investigated for a networked flight control system with a switched system approach. By properly scheduling triggered times of periodic sampling associated with continuous event-triggering and switching instants of each subsystem, an alternate event-triggered mechanism based on the subsystem model is proposed to transmit the triggered information to the candidate controller. Since the subsystem may switch between two adjacent events while the corresponding controller does not switch, asynchronous switching may occur. Meanwhile, an event-triggered switching signal caused by asynchronous switching is designed with the mode-dependent average dwell time approach and event-triggered instants. By constructing multiple Lyapunov functions in the framework of the input delay approach and designing an error feedback controller, the sufficient condition for event-triggered asynchronous output regulation problem is solved. Finally, the proposed methods are proved to be effective by the F-18 aircraft model.

This paper investigates the distributed containment maneuvering problem for uncertain nonlinear multiagent systems in multiple-input multiple-output (MIMO) strict-feedback form. The follower agents are driven to achieve a collective motion guided by multiple parameterized paths, and a dynamic behavior can be independently prescribed for the group during maneuvering. A containment maneuvering controller is developed by utilizing a modular design method enabling decoupled estimation and control. Specifically, an estimator module is constructed by utilizing an echo state network to identify the unknown nonlinearities. Next, a controller module is constructed by employing a modified dynamic surface control method where a second-order nonlinear tracking differentiator is introduced to extract the derivative information of the virtual control law. Subsequently, a path update law is derived such that the virtual leaders are synchronized, and the desired speed profile for the group can be specified independently. By using a small-gain theorem and cascade stability theory, the entire closed-loop system is proved to be input-to-state stable, and the containment maneuvering errors are uniformly ultimately bounded. An application for the formation control of marine surface vehicles is provided to show the efficacy of the proposed controller for containment maneuvering of uncertain nonlinear MIMO strict-feedback systems.

A domain name system (DNS) is one of the most important infrastructures of the Internet communication. It is also a crucial point which is subjected to attacks. The largest distributed denial-of-service (DDoS) attack on October 21, 2016 has targeted a major DNS infrastructure named dynDNS. It was actually the Internet of Things (IoT) DNS flood attack that made more than half of websites in the United States unreachable for a significant amount of time. As we are using the Internet for everything in our life, especially health care and transportation, an attack of this type may cause a major disruption. Therefore, in this article, we are going to analyze the DNS flood attacks and propose two mitigation methods. We propose a load distributed mitigation process which will work as a quick escape route of the legitimate traffic from the attack field. Our solution mainly involves service level changes which can be implemented with collaboration among service providers. Also, our proposed solution is very cost effective as compared to the cost of downtime of the domain names caused by a DNS flood attack. Furthermore, we propose a benign-bot mitigation method and a business model for the method. In the benign-bot mitigation method, a bot program is installed in customers' DNS local servers to allow IP addresses of a list of paid businesses' websites to maintain in the caches so that the websites can be accessed even when the DNS servers are down.

This article investigates an adaptive reinforcement learning (RL) optimal control design problem for a class of nonstrict-feedback discrete-time systems. Based on the neural network (NN) approximating ability and RL control design technique, an adaptive backstepping RL optimal controller and a minimal learning parameter (MLP) adaptive RL optimal controller are developed by establishing a novel strategic utility function and introducing external function terms. It is proved that the proposed adaptive RL optimal controllers can guarantee that all signals in the closed-loop systems are semiglobal uniformly ultimately bounded (SGUUB). The main feature is that the proposed schemes can solve the optimal control problem that the previous literature cannot deal with. Furthermore, the proposed MPL adaptive optimal control scheme can reduce the number of adaptive laws, and thus the computational complexity is decreased. Finally, the simulation results illustrate the validity of the proposed optimal control schemes.

In this paper, an adaptive neural network (NN) control problem is investigated for discrete-time nonlinear systems with input saturation. Radial-basis-function (RBF) NNs, including critic NNs and action NNs, are employed to approximate the utility functions and system uncertainties, respectively. In the previous works, a gradient descent scheme is applied to update weight vectors, which may lead to local optimal problem. To circumvent this problem, a multigradient recursive (MGR) reinforcement learning scheme is proposed, which utilizes both the current gradient and the past gradients. As a consequence, the MGR scheme not only eliminates the local optimal problem but also guarantees faster convergence rate than the gradient descent scheme. Moreover, the constraint of actuator input saturation is considered. The closed-loop system stability is developed by using the Lyapunov stability theory, and it is proved that all the signals in the closed-loop system are semiglobal uniformly ultimately bounded (SGUUB). Finally, the effectiveness of the proposed approach is further validated via some simulation results.

This paper addresses the cooperative path following the problem of ring-networked under-actuated autonomous surface vehicles on a closed curve. A cooperative guidance law is proposed at the kinematic level such that a symmetric formation pattern is achieved. Specifically, individual guidance laws of surge speed and angular rate are developed by using a backstepping technique and a line-of-sight guidance method. Then, a coordination design is proposed to update the path variables under a ring-networked topology. The equilibrium point of the closed-loop system has been proven to be globally asymptotically stable. The result is extended to the cooperative path following the lack of sharing of a global reference velocity, and a distributed observer is designed to recover the reference velocity to each vehicle. Moreover, the cooperative path following the presence of an unknown sideslip is considered, and an extended state observer is developed to compensate for the effect of the unknown sideslip. Both simulation and experimental results are provided to illustrate the effectiveness of the proposed cooperative guidance law for the path following over a closed curve.

In this paper, a cooperative time-varying formation maneuvering problem with connectivity preservation and collision avoidance is investigated for a fleet of autonomous surface vehicles (ASVs) with position-heading measurements. Each vehicle is subject to unknown kinetics induced by internal model uncertainty and external disturbances. At first, a nonlinear state observer is used to recover the unmeasured linear velocity and yaw rate as well as unknown uncertainty and disturbances. Then, observer-based cooperative time-varying formation maneuvering control laws are designed based on artificial potential functions, nonlinear tracking differentiators, and a backstepping technique. The stability of closed-loop distributed formation control system is analyzed based on input-to-state stability and cascade stability. The salient features of the proposed method are as follows. First, cooperative time-varying formation maneuvering with the capability of connectivity preservation and collision avoidance can be achieved in the absence of velocity measurements. Second, the complexity of the cooperative time-varying formation maneuvering control laws is reduced without resorting to dynamic surface control. Third, the uncertainty and disturbance are actively rejected in the presence of position-heading measurements. Simulation results are given to substantiate the proposed output feedback control method for cooperative time-varying formation maneuvering of ASVs with connectivity preservation and collision avoidance.

This article focuses on the containment control problem for nonlinear multiagent systems (MASs) with unknown disturbance and prescribed performance in the presence of dead-zone output. The fuzzy-logic systems (FLSs) are used to approximate the unknown nonlinear function, and a nonlinear disturbance observer is used to estimate unknown external disturbances. Meanwhile, a new distributed containment control scheme is developed by utilizing the adaptive compensation technique without assumption of the boundary value of unknown disturbance. Furthermore, a Nussbaum function is utilized to cope with the unknown control coefficient, which is caused by the nonlinearity in the output mechanism. Moreover, a second-order tracking differentiator (TD) is introduced to avoid the repeated differentiation of the virtual controller. The outputs of the followers converge to the convex hull spanned by the multiple dynamic leaders. It is shown that all the signals are semiglobally uniformly ultimately bounded (SGUUB), and the local neighborhood containment errors can converge into the prescribed boundary. Finally, the effectiveness of the approach proposed in this article is illustrated by simulation results.

For a class of stochastic nonlinear systems in pure-feedback form with dead-zone input and multiple time-varying delays, a novel neural network (NN)-based adaptive control approach is presented in this paper through the use of backstepping approach and dynamic surface technique. By choosing proper Lyapunov-Krasovskii functionals, utilizing the characteristic of hyperbolic tangent functions and adopting the function separation technique, difficulties of controller design that introduced by the time-varying delays can be dealt with properly. Moreover, all unknown nonlinear functions are lumped together and approximated by the NN. Additionally, any information over the boundedness of dead-zone parameters is not needed in the process of controller design. The control scheme proposed in this paper ensures the boundedness in probability of all signals in the closed-loop system, besides, excellent performance of arbitrarily small tracking error will be achieved by selecting control parameters appropriately. At last, two numerical simulation examples are provided to verify the validity of the designed algorithm.

In this paper, a decentralized neural network (NN) output feedback fault tolerant control (FTC) problem is addressed for a class of multi-input multi-output systems with actuator fault. In order to avoid the noncausal problem, the original system is transformed into an input-output expression in accordance with the diffeomorphism theory. Then, in order to establish a quick response to the fault, the fault tolerant controller with minimum learning parameters has been designed such that the semiglobal uniform ultimate boundedness of all the variables in the resulting closed-loop systems can be guaranteed. Finally, the output feedback FTC approach is applied to the interconnected CSTRs, and the comparisons with existing methods are provided to show the effectiveness of the proposed strategy.

In this paper, we propose a new method for voice feature extraction by using a hierarchical clustering approach of inverse discrete cosine transform (IDCT) cepstrum coefficient. Since the IDCT cepstrum coefficient is transformed into the feature vector based on Euclidean metric, we call the proposed voice feature as “E-vector”. Comparing with other voice features, e.g. Mel frequency cepstrum coefficient (MFCC) and histogram of DCT cepstrum coefficients (HDCC), the E-vector can represent the dynamic characteristics and interactional relationships of the voice. In the numerical experiments, we use the speech files based on the voice of 630 people in TIMIT corpus, and employ Gaussian mixture model to compare the recognition accuracy of E-vector with that of MFCC and HDCC. The results show that E-vector outperformed other voice features on personal identification, and presented higher extensiveness on voice feature extraction.

This paper is concerned with the target tracking of underactuated autonomous surface vehicles with unknown dynamics and limited control torques. The velocity of the target is unknown, and only the measurements of line-of-sight range and angle are obtained. First, a kinematic control law is designed based on an extended state observer, which is utilized to estimate the uncertain target dynamics due to the unknown velocities. Next, an estimation model based on a single-hidden-layer neural network is developed to approximate the unknown follower dynamics induced by uncertain model parameters, unmodeled dynamics, and environmental disturbances. A bounded control law is designed based on the neural estimation model and a saturated function. The salient feature of the proposed controller is twofold. First, only the measured line-of-sight range and angle are used, and the velocity information of the target is not required. Second, the control torques are bounded with the bounds known as a priori. The input-to-state stability of the closed-loop system is analyzed via cascade theory. Simulations illustrate the effectiveness of the proposed bounded controller for tracking a moving target.

Customer relationship management (CRM) is one of the most important recommendation systems to manage customer groups and understand potential relationships. CRM systems use many communication approaches such as telephone, email and social media to collect mass of customer data, which can help the companies (managers) to increase sales growth and enhance customer retention. Since 2000s, the advancements of RFID technology brought a new perspective on customer in-store behavior. Based on such RFID data, this paper describes a novel analytic approach applying social network analysis (SNA) into customer relationship network (CRN) and presents three contributions. First, to be different from existing studies, we attempt to construct the CRN based the shopping path (visiting patterns), which is recognized as a data stream of digitally encoded coherent signals. The CRN can help us to understand the customer relationships from the network perspective. Second, we employ network clustering (also called community detection or node classification) method to extract customer groups by maximizing the modularity. The modularity provides us a structural and relational understanding of customer groups. Third, we use within-module agree and participant coefficient to measure how a customer well-connected to other customers in the CRN; and identify such customers as "hub" customers by a topological process.

A novel nonlinear sliding mode control approach dealing with the formation control of under-actuated ships is presented in this paper. To avoid the singularity problem, state space of the system is partitioned into two regions, with one region bounded for terminal sliding mode control and its complement singular for that. And a linear auxiliary sliding mode controller is designed for system trajectories starting from the complement region. With the application of nonlinear sliding mode control approach and finite-time stability theory, a distributed controller is designed for individual under-actuated ship to achieve the given formation pattern within a finite time. Finally, two simulation examples are provided to verify the effectiveness and performance of the proposed approach.

This paper is concerned with the event-triggered finite-time control problem for networked switched linear systems by using an asynchronous switching scheme. Not only the problem of finite-time boundedness, but also the problem of input-output finite-time stability is considered in this paper. Compared with the existing event-triggered results of the switched systems, a new type of event-triggered condition is proposed. Sufficient conditions are established to guarantee the event-based asynchronous closed-loop systems are both finite-time bounded and input-output finite-time stable. A set of event-triggered finite-time bounded and input-output finite-time stabilizing controllers are designed under the asynchronous control scheme. It is revealed that the triggered thresholds determine the number of sampling points transmitted to the controller, and the smaller triggered parameters indicate the less-sampled data needed to be transmitted to the controller under the event-triggered scheme. Finally, a boost converter circuit is applied to bring out the advantages of the proposed control scheme.

This paper is concerned with the cooperative path maneuvering of multiple underactuated autonomous surface vehicles (ASVs) moving along a parameterized path. Each vehicle is subject to uncertain kinematics and unknown kinetics induced by model uncertainty and ocean disturbances. A modular adaptive control method is presented to develop the cooperative controller such that a queue formation along the parameterized path can be achieved. First, two identification modules are developed such that the uncertain kinematics and unknown kinetics can be estimated by an adaptive term and echo state networks, respectively. Next, a cooperative path maneuvering controller module is designed based on a line-of-sight guidance scheme, tracking differentiators, and a path variable containment approach. The path variable containment approach is used to guarantee that the ASVs are evenly spaced between two virtual leaders moving along the path. Finally, the stability of the identification-controller pair is analyzed. Comparative studies are performed to validate the efficiency of the proposed method.

By the combination of the adaptive backstepping design with the dynamic surface control technique, an novel adaptive neural control approach is investigated for a class of pure-feedback stochastic nonlinear systems with multiple unknown time-varying delays. To overcome the design difficulty arising from the non-affine structure of pure-feedback stochastic systems, the mean value theorem is exploited. The design difficulties due to multiple unknown time-varying delay functions are overcome by using the function separation technique, the appropriate Lyapunov-Krasovskii functionals and the desirable property of hyperbolic tangent functions. The radial-basis-function (RBF) neural networks are utilized to approximate the unknown nonlinear functions. It is shown that the proposed control approach can guarantee that all signals of the closed-loop system are bounded in probability, and the tracking errors can be made arbitrarily small in probability by choosing suitable design parameters. Finally, simulation example is provided to demonstrate the effectiveness of the proposed control scheme.

In this paper, a hybrid fuzzy adaptive output feedback control design approach is proposed for a class of multiinput and multioutput strict-feedback nonlinear systems with unknown time-varying delays, unmeasured states, and input saturation. First, fuzzy logic systems are employed to approximate unknown nonlinear functions in the system. Next, a smooth function is used to approximate the input saturation and an adaptive fuzzy state observer is constructed to solve the problem of unmeasured states. Based on the designed adaptive fuzzy state observer, a serial-parallel estimation model is established. By applying adaptive fuzzy dynamic surface control technique and utilizing the prediction error between the system states observer model and the serial-parallel estimation model, a new fuzzy controller with the composite parameters adaptive laws is developed based on Lyapunov-Krasovskii functional. It is proved that all variables of the closed-loop system are bounded and the system outputs can follow the given bounded reference signals as close as possible. A simulation example is provided to further show the effectiveness of this novel control scheme.

In this paper, a leader-following consensus scheme is presented for networked uncertain nonlinear strict-feedback systems with unknown control directions under directed graphs, which can achieve predefined synchronization error bounds. Fuzzy logic systems are employed to approximate system uncertainties. A specific Nussbaum-type function is introduced to solve the problem of unknown control directions. Using a dynamic surface control technique, distributed consensus controllers are developed to guarantee that the outputs of all followers synchronize with that of the leader with prescribed performance. Based on Lyapunov stability theory, it is proved that all signals in closed-loop systems are uniformly ultimately bounded and the outputs of all followers ultimately synchronize with that of the leader with bounded tracking errors. Simulation results are provided to demonstrate the effectiveness of the proposed consensus scheme.

An adaptive neural network control approach is proposed for a class of stochastic nonlinear strict-feedback systems with unknown nonlinear function in this paper. Only one NN (neural network) approximator is used to tackle unknown nonlinear functions at the last step and only one actual control law and one adaptive law are contained in the designed controller. This approach simplifies the controller design and alleviates the computational burden. The Lyapunov Stability analysis given in this paper shows that the control law can guarantee the solution of the closed-loop system uniformly ultimate boundedness (UUB) in probability. The simulation example is given to illustrate the effectiveness of the proposed approach.

In this paper, an adaptive fuzzy decentralized output feedback control design is presented for a class of interconnected nonlinear pure-feedback systems. The considered nonlinear systems contain unknown nonlinear uncertainties and the states are not necessary to be measured directly. Fuzzy logic systems are employed to approximate the unknown nonlinear functions, and then a fuzzy state observer is designed and the estimations of the immeasurable state variables are obtained. Based on the adaptive backstepping dynamic surface control design technique, an adaptive fuzzy decentralized output feedback control scheme is developed. It is proved that all the variables of the resulting closed-loop system are semi-globally uniformly ultimately bounded, and also that the observer and tracking errors are guaranteed to converge to a small neighborhood of the origin. Some simulation results and comparisons with the existing results are provided to illustrate the effectiveness and merits of the proposed approach.

In this paper, a composite adaptive fuzzy output-feedback control approach is proposed for a class of single-input and single-output strict-feedback nonlinear systems with unmeasured states and input saturation. Fuzzy logic systems are utilized to approximate the unknown nonlinear functions, and a fuzzy state observer is designed to estimate the unmeasured states. By utilizing the designed fuzzy state observer, a serial-parallel estimation model is established. Based on adaptive backstepping dynamic surface control technique and utilizing the prediction error between the system states observer model and the serial-parallel estimation model, a new fuzzy controller with the composite parameters adaptive laws are developed. It is proved that all the signals of the closed-loop system are bounded and the system output can follow the given bounded reference signal. A numerical example and simulation comparisons with previous control methods are provided to show the effectiveness of the proposed approach.

In this paper, an adaptive fuzzy backstepping output-feedback tracking control approach is proposed for a class of multi-input and multi-output (MIMO) stochastic nonlinear systems. The MIMO stochastic nonlinear systems under study are assumed to possess unstructured uncertainties, unknown dead-zones, and unknown control directions. By using a linear state transformation, the unknown control coefficients and the unknown slopes characteristic of the dead-zones are lumped together, and the original system is transformed to a new system on which the control design becomes feasible. Fuzzy logic systems are used to approximate the unstructured uncertainties, and a fuzzy state observer is designed to estimate the unmeasured states. By introducing a special Nussbaum gain function into the backstepping control design, a stable adaptive fuzzy output-feedback tracking control scheme is developed. The main features of the proposed adaptive control approach are that it can guarantee the stability of the closed-loop system, and the tracking errors converge to a small neighborhood of zero. Moreover, it can solve the problems of unknown control direction, unknown dead-zone, and unmeasured states simultaneously. Two simulation examples are provided to show the effectiveness of the proposed approach.

This paper presents an adaptive output-feedback neural network (NN) control scheme for a class of stochastic nonlinear time-varying delay systems with unknown control directions. To make the controller design feasible, the unknown control coefficients are grouped together and the original system is transformed into a new system using a linear state transformation technique. Then, the Nussbaum function technique is incorporated into the backstepping recursive design technique to solve the problem of unknown control directions. Furthermore, under the assumption that the time-varying delays exist in the system output, only one NN is employed to compensate for all unknown nonlinear terms depending on the delayed output. Moreover, by estimating the maximum of NN parameters instead of the parameters themselves, the NN parameters to be estimated are greatly decreased and the online learning time is also dramatically decreased. It is shown that all the signals of the closed-loop system are bounded in probability. The effectiveness of the proposed scheme is demonstrated by the simulation results.

In this paper, the problem of fuzzy adaptive state-feedback control is investigated for a class of switched uncertain nonlinear large-scale systems. There exist switching jumps and uncertainties in systems models and switching signals, respectively. Fuzzy logic systems are utilized to approximate the unknown nonlinear functions, and combining the adaptive backstepping technique and the dwell-time property of the switching signal, an adaptive fuzzy state-feedback control approach is developed. It is proved that the proposed control approach can guarantee that all the signals in the closed-loop system are bounded, and the tracking errors converge to a small neighborhood of the origin.

In this paper, a backstepping based adaptive fuzzy control algorithem is presented for a class of uncertain nonlinear discrete-time systems in the strict-feedback form. By introducing the "minimal learning parameter (MLP)" technique, the proposed scheme is able to circumvent the problem of "curse of dimension" for high-dimensional systems. Meanwhile, all the virtual control laws and actual control law in the system are updated by a novel actual adaptive update law, thus the number of parameters updated online for whole system is only by one. Takagi-Sugeno (T-S) fuzzy systems are used to approximate the unknown system functions. It is shown via Lyapunov theory that all signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB). Finally, a simulation example is employed to illustrate the effectiveness and advantages of the proposed scheme.

In this paper, an adaptive backstepping controller based on nonlinear disturbance observer (DOB) is proposed for the nonlinear fin stabilizer system. DOB is responsible for disturbance rejection and uncertainty compensation, while the adaptive backstepping scheme is proposed for dealing with uncertain parameters of the fin stabilizer model. The designed controller guarantees uniform ultimate boundedness of all the signals in the closed-loop system and the tracking errors converge to a small neighborhood of the origin. The advantages of the proposed control scheme comprise that the DOB shorten the response time and observes the whole disturbances of the fin stabilizer system. Simulation example is presented to show the preciseness and robustness of the stabilization control realized by the proposed method.

This paper focuses on adaptive neural network control for a class of uncertain single-input single-out nonlinear strict-feedback systems. Neural network (NN) is directly used to approximate the unknown desired control signals and a novel direct adaptive neural network controller is proposed via backstepping and the minimal learning parameter (MLP) techniques. The main advantages of the proposed controller are that: (1) the problem of "explosion of complexity" inherent in the conventional backstepping method is avoided; (2) the problem of "dimensionality curse" is solved and only one adaptive parameter that needs to be updated online. These advantages result in a much simpler adaptive control algorithm, which is convenient to implement in applications. The proposed controller guarantees that all the close-loop signals are uniform ultimate boundedness (UUB) and that the tracking errors converge to a small neighborhood of the desired trajectory. Finally, simulation studies are given to show the effectiveness of the proposed approach.

In this paper, an adaptive fuzzy robust output feedback control problem is considered for a class of single-input and single-output nonlinear systems in a strict-feedback form. The considered systems possess the unstructured uncertainties, unknown dead zone, and the dynamics uncertainties, and they do not assume the states being available for the controller design. In the controller design, fuzzy logic systems are first used to approximate the unstructured uncertainties, and by utilizing the information of the bounds of the dead-zone slopes and treating the time-varying inputs coefficients as a system uncertainty, a fuzzy state observer is designed to estimate the unmeasured states. By combining a backstepping technique with a nonlinear small-gain approach, a new adaptive fuzzy robust output feedback control has been developed. It is proved that the proposed fuzzy adaptive control approach can guarantee the semiglobal uniform ultimate boundedness for all the solutions of the closed-loop systems. Simulation studies and comparisons with previous methods are included to illustrate the effectiveness of the proposed approach.

In this paper, a novel adaptive fuzzy output feedback control scheme is presented for a class of SISO uncertain nonlinear systems in the presence of input saturation. The control design is achieved by combining adaptive fuzzy K-filter observer technique and the dynamic surface control (DSC) technique along with the minimal-learning-parameters (MLP) algorithm. The proposed controller can guarantee that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges to a small neighborhood of the origin. An advantage of the proposed control scheme lies in that the number of fuzzy adaptive parameters is reduced to one, and three problems of “computational explosion”, “dimension curse”, “unmeasured states” are solved. A numerical simulation is presented to demonstrate the effectiveness and performance of the proposed scheme.

An adaptive output feedback control is studied for uncertain nonlinear single-input-single-output systems with partial unmeasured states. In the scheme, a reduced-order observer (ROO) is designed to estimate those unmeasured states. By employing radial basis function neural networks and incorporating the ROO into a new backstepping design, an adaptive output feedback controller is constructively developed. A prominent advantage is its ability to balance the control action between the state feedback and the output feedback. In addition, the scheme can be still implemented when all the states are not available. The stability of the closed-loop system is guaranteed in the sense that all the signals are semiglobal uniformly ultimately bounded and the system output tracks the reference signal to a bounded compact set. A simulation example is given to validate the effectiveness of the proposed scheme.

In this paper, an adaptive fuzzy backstepping dynamic surface control (DSC) approach is developed for a class of multiple-input-multiple-output nonlinear systems with immeasurable states. Using fuzzy-logic systems to approximate the unknown nonlinear functions, a fuzzy state observer is designed to estimate the immeasurable states. By combining adaptive-backstepping technique and DSC technique, an adaptive fuzzy output-feedback backstepping-control approach is developed. The proposed control method not only overcomes the problem of "explosion of complexity" inherent in the backstepping-design methods but also overcomes the problem of unavailable state measurements. It is proved that all the signals of the closed-loop adaptive-control system are semiglobally uniformly ultimately bounded, and the tracking errors converge to a small neighborhood of the origin. Simulation results are provided to show the effectiveness of the proposed approach.

Robust adaptive-fuzzy-tracking control of a class of uncertain multi-input/multi-output nonlinear systems with coupled interconnections is considered in this paper. Takagi–Sugeno (T–S) fuzzy systems are used to approximate the unknown system functions. A novel adaptive-control scheme is developed on the basis of the so-called “dynamic-surface control” and “minimal-learning parameters” techniques. The proposed scheme has following two key features. First, the number of parameters updated online for each subsystem is reduced to one, and both problems of “curse of dimension” for high-dimensional systems and “explosion of complexity” inherent in the conventional backstepping methods are circumvented. Second, the potential controller-singularity problem in some of the existing adaptive-control schemes with feedback-linearization techniques is overcome. It is shown via Lyapunov theory that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded. Finally, simulation results via two examples are presented to demonstrate the effectiveness and advantages of the proposed scheme.

A robust adaptive tracking control approach is presented for a class of strict-feedback single-input-single-output nonlinear systems. By employing radial-basis-function neural networks to account for system uncertainties, the proposed scheme is developed by combining "dynamic surface control" and "minimal learning parameter" techniques. The key features of the algorithm are that, first, the problem of "explosion of complexity" inherent in the conventional backstepping method is avoided, second, the number of parameters updated online for each subsystem is reduced to 2, and, third, the possible controller singularity problem in the approximation-based adaptive control schemes with feedback linearization technique is removed. These features result in a much simpler adaptive control algorithm, which is convenient to implement in applications. In addition, it is shown via input-to-state stability theory and small gain approach that all signals in the closed-loop system are semiglobal uniformly ultimately bounded. Finally, three simulation examples are used to demonstrate the effectiveness of the proposed scheme.

A simple adaptive fuzzy control (SAFC) is proposed for a class of strict-feedback uncertain nonlinear systems with both unknown system nonlinearities and unknown virtual control gain nonlinearities. Combining the dynamic surface control (DSC) technique with minimal-learning-parameters (MLP) algorithm, a systematic procedure for synthesis of SAFC is developed base on the universal approximation of Takagi-Sugeno (T-S) fuzzy system. An important feature of the proposed algorithm is that the number of parameters updated on line for each subsystem is reduced dramatically to one, both problems of ldquoexplosion of complexityrdquo and ldquocurse of dimensionrdquo are avoided, such that the computation load is reduced drastically. It is shown that all closed-loop signals are semi-global uniform ultimate bound (SGUUB) via Lyapunov stability theory and the tracking error can be made arbitrary small. Finally, simulation results are presented to demonstrate the effectiveness and performance of the proposed scheme.

A robust adaptive tracking control problem is discussed for a class of strict-feedback uncertain nonlinear systems. Takagi-Sugeno type fuzzy logic systems are used to approximate the uncertainties. A unified and systematic procedure is developed to derive a novel robust adaptive tracking controller by use of the input-to-state stability (ISS) and by combining the dynamic surface control(DSC)-based backstepping technique and generalized small gain approach. The key features of the algorithm are that, firstly, the problem of “explosion of complexity” inherent in the conventional backstepping method is circumvented, secondly, the number of parameters updated on line for each subsystem is reduced dramatically to 2. These features result in a much simpler algorithm, which is convenient to realize in application. In addition, it is shown that all closed-loop signals are semi-global uniformly ultimately bounded(SGUUB). Finally, simulation results via an application example of a pendulum system with motor is used to demonstrate the effectiveness and performance of the proposed scheme.