Maneuverable threat poses significant risks to the operational safety of spacecraft in orbit, thereby jeopardizing mission success in the contested space environment. To address the coordinated orbital game between multiple pursuers (spacecraft) and one evader (threat), this paper proposes a reward-centering multi-agent deep deterministic policy gradient (MADDPG) algorithm, which has two-folds: Firstly, a relative dynamical model consideringJ2perturbation is established to characterize orbital interactions among multiple players, laying a physics-based foundation for decision-making. Secondly, an improved multi-agent reinforcement learning framework is constructed with two components. For individual spacecraft of multiple pursuers, a state-driven exploration-exploitation tradeoff mechanism is incorporated to avoid suboptimal strategy convergence. For the spacecraft group (namely multiple pursuers), reward functions are designed to align individual behaviors with collective objectives. Furthermore, a reward-centering mechanism along with theoretical analysis is introduced to dynamically calibrate the reward baseline, which stabilizes policy update via reducing gradient variance. Simulation results show the proposed algorithm enables spacecraft to autonomously develop coordinated strategies, ultimately confining the threat within predefined interception threshold.

This paper investigates the secure consensus tracking control problem for nonlinear multi-agent systems under Byzantine attacks and unknown nonlinearities. Addressing structural limitations and vulnerabilities in sparse topologies, the multi-hop mean-subsequence-reduced algorithm is adopted to enhance system robustness and information availability by enabling message relay through healthy intermediate nodes. Unlike existing works primarily on linear dynamics and static average consensus, this study focuses on the multi-hop mean-subsequence-reduced method for nonlinear state feedback control frameworks. Radial basis function neural networks are integrated for secure approximation of unknown nonlinearities. Using the backstepping method, a novel secure control strategy is synthesized. The proposed scheme rigorously guarantees the convergence and boundedness of the closed-loop system, ensuring accurate tracking for nonlinear multi-agent systems.

This paper presents an optimal control strategy for Euler–Lagrange systems subject to full-state constraints, based on a dynamic event-triggered critic neural network. To handle the constraints, an error transformation technique is introduced to convert the constrained error dynamics into an unconstrained form. Leveraging the transformed system, a critic neural network is utilized to approximate the performance index and derive the corresponding optimal control law. The specific update mechanism is formulated for the weight matrix of the neural network. To alleviate computational load, a dynamic event-triggered condition is designed to limit the frequency of weight updates, thereby mitigating unnecessary computations while effectively eliminating Zeno behavior. Comprehensive stability analysis proves the proposed approach is feasible and ensures that the approximation error of the critic neural network remains uniformly ultimately bounded. Numerical simulations on a robotic manipulator demonstrate the effectiveness of the proposed method and further verify its disturbance rejection capability under parametric perturbations and external disturbances.

In this work, a dynamic event-triggered optimized backstepping control approach based on neural networks is proposed to address communication limitations in strict-feedback nonlinear systems. Neural networks are employed to approximate the unknown system uncertainties, with their parameters updated via a gradient descent algorithm. To enhance the communication efficiency of both control inputs and neural network parameters, a dynamic event-triggering scheme is devised. This mechanism adaptively modifies its threshold parameters in real time, depending on the system’s tracking performance. Additionally, a disturbance observer is incorporated to mitigate the influence of external perturbations. By formulating a barrier-type performance index for subsystem optimization and integrating observer–actor–critic structures, both virtual and actual optimal controllers are derived within an inversion-based control framework. Lyapunov theory is utilized to demonstrate that all signals in the closed-loop system are uniformly ultimately bounded. Finally, the effectiveness and practicality of the control strategy are verified by numerical simulations and an application case of an electromechanical system.

This paper aims to present a unified optimization for resource allocation and scheduling in resource allocation systems. In contrast to traditional two-stage methods, which optimize scheduling and resource allocation independently, this study combines the two to produce globally optimal solutions within practical limitations. In order to describe various resource categories and their disparate quantity needs, a variable timed Petri net is created, in which the arc weights and time delays are determined by resource types. The problem is formulated as a budget-constrained makespan minimization model, with constraints linearized if possible. To handle an inherent non-linear constraint, an iterative mixed integer linear programming approach is developed to obtain optimal resource allocation and scheduling strategies. Extensive computational experiments verify that the proposed framework effectively determines optimal resource type and quantity configurations along with scheduling strategies, jointly minimizing makespan under budget constraints and demonstrating practical efficiency and applicability.

Resource allocation systems are considered fundamental to many practical applications, where the challenge is often posed by the need to optimize resource usage under budget constraints to improve system performance. In this paper, a systematic approach to resource allocation in Petri net-based systems is presented. First, a method for system bottleneck identification is analyzed to reveal the structural limitations affecting throughput. Based on this, the minimum number of resources required to maximize system throughput without budget constraints is determined. Subsequently, a heuristic iterative resource allocation strategy is developed, in which mixed-integer linear programming is employed to guide the allocation process considering budget levels. The effectiveness of the proposed method is demonstrated through comparative experiments under different resource constraints.

The control of humanoid locomotion remains one of the most formidable challenges in robotics. Conventional model-based approaches not only rely extensively on manual design but also exhibit limited generalization across diverse tasks and environments. To overcome these limitations, a curriculum-enhanced reinforcement learning framework is proposed in this work for training robust locomotion. Given the strong temporal dependencies inherent in humanoid locomotion, Mamba-2 is employed as the backbone of the Actor-Critic network, enabling efficient temporal modeling of historical observations and content-based reasoning with linear spatiotemporal complexity. Furthermore, to eliminate the tendency of humanoid robots to favor low-speed motion in order to maintain gait stability and balance, which often leads to inaccurate velocity command tracking, a command-guided curriculum learning (CGCL) method is proposed to improve responsiveness to velocity commands. Experimental results demonstrate that the proposed Mamba-based framework achieves state-of-the-art (SOTA) performance, while CGCL significantly enhances the accuracy of velocity command tracking. Moreover, sim-to-real transfer experiments confirm both the robustness and the seamless deployability of the proposed framework on physical humanoid platforms.

In practical manufacturing scenarios, re-entrant manufacturing systems (RMSs) are vulnerable to uncertain workstation faults, which may cause sudden changes in the work-in-process (WIP) level and impair the overall production capacity. To ensure agile response to market demands and to maintain stable system behavior under fault scenarios, an active fault-tolerant control scheme via fault diagnosis for RMSs is developed in this paper. Firstly, a hybrid hyperbolic partial differential equation continuum model is developed to describe the evolution of WIP dynamics, where the workstation faults result in the discarding of defective products during processing. Subsequently, a fault diagnosis scheme is presented to detect and estimate the uncertain faults in real time, by integrating an observer-based fault detection method and an adaptive fault estimation algorithm. Utilizing the estimated fault information, a fault-tolerant control strategy is then proposed to compensate for fault-induced state jumps and achieve agile production control. Finally, a numerical simulation is conducted to demonstrate the effectiveness of the proposed fault-tolerant control approach. It is believed the proposed theory enriches the theory, analysis, design of control circuit systems and has potential of practical implementations to industrial manufacturing systems.

In this article, an input-constrained visual servoing formation controller is proposed for multiple quadrotor systems operating without intervehicle communication or relative position measurements. The aerial formation control is achieved by formulating image-based leader-follower dynamics using a virtual camera framework and sphere-based image moments. An adaptive velocity observer is developed for the follower quadrotor to estimate the relative velocity with respect to the leader quadrotor in communication-free environments. Input-constrained visual servoing and attitude controllers are proposed using an off-policy reinforcement learning (RL) algorithm to handle visibility and attitude constraints, without relying on accurate system model parameters. The stability of the closed-loop system is theoretically analyzed, and the effectiveness of the proposed controller is demonstrated through case studies.

Matrix placement machines improve production efficiency of printed circuit board assembly (PCBA), addressing critical needs for flexible and intelligent electronics manufacturing. However, their complex head structure renders solutions for traditional beam-head placement machines inefficient for matrix placement machines. This article proposes a phased hybrid algorithm with adaptive hyper-nondominated sorting genetic algorithm II (NSGA-II) for PCBA optimization. A bidirectional search mechanism is applied to derive feeder distributions and nozzle configurations, and iteratively tighten the solution space using priority-based search strategies. The softmax, max greatest common divisor, and max matching mechanisms are proposed for placement and pickup sequences, which facilitates construction of solution pools. Initial solutions are extracted from the pool and, subsequently, hyperheuristic mechanisms dynamically adjust genetic operators within NSGA-II to minimize placement, pickup, and recognition times with better convergence speed. Experimental validation with real-world production data demonstrates that the proposed algorithm achieves 6.05%–38.18% performance improvements compared to state-of-the-art solutions.

Operational efficiency of placement machines constrains the overall production capacity of printed circuit board (PCB) assembly lines. Existing state-of-the-art algorithms face challenges, such as conflicts between multiple objectives and coupling within different problems. This article proposes a multiobjective hybrid evolutionary multitasking algorithm (MOHEMTA) to address PCB assembly optimization in beam-head placement machines. The algorithm divides the problem into pickup and placement tasks, leveraging implicit parallelism to enhance solution efficiency. A nozzle block encoding method and heuristic decoding strategies with domain knowledge are introduced to reduce encoding complexity and accelerate algorithm convergence. MOHEMTA enhances offspring population diversity and quality through an elitist strategy, evolutionary operators, and knowledge transfer mechanisms, while incorporating safeguards against negative transfer. Experiments demonstrate that the multiobjective solution performance and practical results of MOHEMTA are better than those of other state-of-the-art algorithms.

In printed circuit board assembly (PCBA), placement route schedule (PRS) significantly affects assembly efficiency of the beam head placement machine. The PRS is typically solved by decomposing it into placement point assignment problem (PPAP) and beam heads sequencing problem (BHSP). This article first proposes a deep reinforcement learning framework to tackle PPAP, which is a key determinant of overall process quality. Then, to mitigate the impact of placement position and angle on assembly efficiency, a dynamic programming-based beam head sequencing algorithm is introduced to solve BHSP. Since component types and placement point assignment states vary across different pick-and-place cycles, a dynamic combinatorial mask encoding method is proposed to effectively extract feature information between placement points. Inspired by the beam head placement process, a decoder that combines gated recurrent units and an attention mechanism is finally introduced, which fully utilizes historical node information to predict the next node. Experimental results demonstrate that the proposed method reduces PCBA routing distance by an average of 4.62%, outperforming other State-of-the-Art approaches.

This article develops a fault-estimation (FE) method for a class of discrete-time nonlinear systems subject to unknown nonlinearities, external disturbances, and actuator faults. In this work, an adaptive fuzzy observer is first designed for discrete-time systems to eliminate the effects of nonlinearities on the FE performance. Subsequently, the rank constraints of disturbance coefficient matrices in traditional discrete-time FE results are relaxed by decomposing the disturbances into virtual perturbations and states. Furthermore, an adaptive mechanism is introduced to reconstruct the states and faults without prior knowledge of unknown perturbations. Finally, the effectiveness of the proposed adaptive fuzzy FE method is validated through a circuit system example.

This work is concerned with the asynchronous output feedback sliding mode control (SMC) of stochastic nonlinear Markov jump systems (MJSs) via Takagi–Sugeno fuzzy models. Due to some real-world environment limitations, the actual system modes that are not directly available for controller synthesis are known as hidden modes. Then the sliding surface/sliding mode controller modes are featured as observable modes, and the relationship between these two concepts is established by employing emission probabilities. As a two-layer stochastic process, the hidden Markov model (HMM) governs the jump parameters and characterizes the asynchronous mode switching phenomenon between the original plant and the sliding surface/sliding mode controller. By integrating the sliding surface with the dynamical features of fuzzy MJSs, the dynamics of the sliding motion are described by constructing a T–S fuzzy singular MJS. Under a unified convexification setup, novel dissipative performance and stochastic stability analysis results on the sliding motion are proposed. In view of the full MJS states also not measurable, a novel observed-mode-based asynchronous output feedback dynamic SMC synthesis approach is propounded to ensure the MJSs’ states are located in a vicinity of the sliding surface. Illustrative simulation examples are finally provided to validate the superiority and effectiveness of the developed scheme.

This article proposes an active fault-tolerant control (AFTC) scheme for attitude tracking of uncertain spacecraft with actuator faults and unavailable angular velocity. First, a second-order fully actuated system (FAS) model is derived from the dynamics of the spacecraft. The unwinding phenomenon is solved by controlling the scalar quaternion. Then, the state observer and the FAS-based controller are designed, and the assumption that all the system states and their derivatives are requested to be known is relaxed, which is a typical assumption in the existing FAS approach. The proposed method facilitates the detection and analysis of the fault information for a spacecraft, based on which an AFTC scheme is designed to address uncertain spacecraft attitude tracking under uncertain time-varying inertia parameters, faults, and disturbances. Moreover, angular velocity measurements are not needed. The uniformly bounded stability of the proposed control scheme is theoretically proved. Finally, the numerical simulation results are provided to illustrate the performance of the proposed control scheme.

The complex coupling relationship among attitude, orbit, and vibration challenges the control of flexible spacecraft. This article investigates the integrated attitude–orbit dynamical modeling and control of flexible spacecraft, which accurately describes the above complex coupling relationship to improve control performance. First, a twistor-based attitude–orbit integrated model of flexible spacecraft is established, avoiding the unitary constraint of the dual-quaternion-based model. Then, the attitude–orbit integrated control strategy based on the fully actuated system (FAS) approach is proposed for flexible spacecraft, which yields an arbitrary designable linear closed-loop system. Finally, the correctness of the dynamical model and the effectiveness and superiority of the proposed control strategy are demonstrated by two sets of simulations.

This article investigates the fault detection and isolation (FDI) problem for a class of re-entrant manufacturing systems (RMSs) subject to workstation faults, sensor faults, and measurement disturbances. The system dynamics are first characterized by a hybrid hyperbolic partial differential equation (HHPDE) continuum model. A two-stage observer-based FDI framework is then developed to enable timely and reliable fault diagnosis. In the first stage of this framework, a diagnostic observer equipped with residual evaluation logic is employed, which is capable of detecting the occurrence of faults yet incapable of differentiating between the sensor faults and the workstation faults. Once a fault is detected, the isolation procedure starts as the second stage to distinguish the fault types and localize the fault sources. To be specific, anH-/H∞observer-based isolation scheme is proposed to effectively decouple the sensor faults from the sensor disturbances, by exploiting the dual performance of disturbance attenuation and fault sensitivity; an adaptive observer-based isolation strategy is devised to identify the workstation faults by capturing the associated structural changes in the system dynamics. Finally, the effectiveness and robustness of the proposed methods are validated through comprehensive numerical simulations.

Time-optimal path generation is critical for maximizing throughput in high-speed PCB assembly, yet existing approaches predominantly focus on geometric distance minimization, overlooking the fundamental impact of acceleration dynamics and multi-axis coordination on temporal efficiency. This study addresses this gap by introducing a physics-based time estimator that explicitly models trapezoidal acceleration profiles for synchronized X/Y/Z/R-axis motions, enabling precise performance evaluation under realistic kinematic constraints. Experimental validation on production PCBs demonstrates that the proposed estimator achieves much higher estimation accuracy, outperforming conventional methods. When integrated as the objective function in multi-chromosome genetic algorithm optimization, time-optimal solutions effectively reduce actual movement times compared to distance-optimal baselines, despite requiring longer travel paths. These findings confirm the time-optimal estimator’s superiority over pure distance minimization, proving that peak efficiency is achieved by balancing travel distance with movement speed.

Significant differences in spatial structure characteristics make the coarse-grained few-shot scenarios easier to handle. However, fine-grained scenes remain often more challenging, and current related research falls far short of human-level recognition capabilities. To address this tough challenge, we get inspiration by the structures and functions of the human visual system, and then propose a human-centric visual cognition recognition framework, named VCRNet, including a shallow cognition network VCRNet-4 and the deep cognition neural network VCRNet-12. Specifically, in this framework, we cleverly design a visual perception module to simulate powerful visual pathway feature encoding of human visual system, an attentional regional sensing module and another pixel-level sensing module to simulate the structural and shape-detailed perception capabilities of the human occipital lobe, and a cognitive recognition module to emulate the integrative cognitive abilities of human frontal lobe. Ulteriorly, we have evaluated the performances of our proposed methods on three publicly available fine-grained benchmarks: CUB-200-2011, Aircraft-Fewshot, and Stanford-Cars. The compelling comparative experiments and thoroughly justified validation study demonstrate the structural excellence and superior performance of our VCRNet framework. To facilitate subsequent research, our codes have been available.

This paper proposes an adaptive super-twisting sliding mode resilient control method for uncertain nonlinear cyber-physical systems (CPSs) subject to actuator and sensor attacks. Two super-twisting algorithms with adaptive gains are designed based on the compromised system states to counteract the effect of cyber-attacks to ensure the system states converge to a small region near zero. Furthermore, the gain overestimation problem of the sliding mode resilient controller is also discussed. Finally, a formal analysis of the closed-loop system is derived using Lyapunov function techniques. Simulation results on a two-link manipulator system validate the effectiveness of the proposed resilient control strategy. Note to Practitioners—The motivation of this paper is to address the secure issues of the nonlinear cyber-physical systems, including applications in robotic manipulators, vehicle systems, and power systems. Since some devices of CPSs, such as sensor and controller components, communicate via networked channels in practice, the original system states and control signals of CPSs may be tampered by cyber-attacks, leading to unavailable system states and severe uncertainties with unknown bounds. Although the super-twisting sliding mode control (ST-SMC) method is an effective tool for dealing with uncertainties, the control gains may be overestimated due to the intermittent nature of cyber attacks, resulting in severe chattering caused by the discontinuous term of the ST-SMC method. Furthermore, it is extremely challenging when only compromised system states are available for the controller, potentially generating false commands and destroying the entire system. Therefore, based on Lyapunov theory, a novel multivariable adaptive sliding mode resilient control method with two adaptive super-twisting algorithms is proposed to mitigate the effects caused by cyber-attacks. The effectiveness of the proposed method is validated using a two-link manipulator. Note that the proposed method has the potential for broader engineering applications in real-world scenarios.

This paper addresses the challenge of attitude stabilization for a class of underactuated rigid and flexible spacecrafts by utilizing only two control inputs. The attitude stabilization problem of underactuated spacecraft poses substantial challenges to the High-Order Fully Actuated (HOFA) system approach, due to the fact that it heavily depends on the full actuation characteristics of the system. To circumvent the aforementioned limitation, we first derive a HOFA system for underactuated rigid spacecraft using homogeneity theory and averaged system approach. Subsequently, we design a controller based on the HOFA system approach, which yields a linear closed-loop system. This methodology is then extended to address the simplified attitude stabilization problem of underactuated flexible spacecraft. By utilizing the HOFA system approach in tandem with an observer for estimating modal variables, we propose a control strategy that not only stabilizes the attitude but also actively suppresses elastic vibration. The performance of the proposed control schemes are demonstrated through simulation studies.

The robust fuzzy control problem for a category of network-type re-entrant manufacturing systems (RMSs) is explored in this paper. A continuum model is first employed to represent the RMSs with external disturbances and time-varying communication delays in terms of a nonlinear hyperbolic partial differential equation (PDE) model. The nonlinear model is then reformulated in the framework of a T-S fuzzy model. A novel Lyapunov-Krasovskii-type stability theorem is proposed for the concerned PDEs with delays, extending the classical Lyapunov-Krasovskii framework to delayed PDE systems. Using the proposed stability theorem, LMI-based conditions are developed for stability analysis and control synthesis of the closed-loop RMSs under external disturbances and time-varying delays, which demonstrates effective alignment of production output with market demand despite environmental perturbations. At last the results of a simulation validate the developed control approach.

This paper investigates the fault-tolerant observer design problem for a class of re-entrant manufacturing systems (RMSs) in the presence of workstation faults during the production process. A hyperbolic hybrid partial differential equation (HHPDE) continuum model is constructed to describe the dynamics of RMSs suffering from unexpected workstation faults, by considering that machinery failures of workstations lead to discarding of defective products. In the case that the faults are known, a fault-tolerant impulsive observer is designed for state estimation of the RMSs. In the case that the fault information is uncertain, a diagnostic observer based residual evaluation logic is developed for fault detection first. Upon detecting the faults, an adaptive impulsive observer is then proposed to simultaneously estimate both the system states and the faults. In addition, by using a piecewise Lyapunov function candidate, sufficient stability conditions that guarantee the exponential input-to-state stability (EISS) of the estimation error are formulated in terms of linear matrix inequalities (LMIs). Finally, the feasibility and effectiveness of the proposed strategy are validated through numerical simulations.

In the field of smart surface mount technology (SMT) production, integrating machines through a cyber-physical system (CPS) architecture holds significant potential for improving assembly quality and efficiency. However, fully unifying inspection and production systems to effectively address assembly-related quality issues remains a challenge. This study seeks to close these gaps by introducing collaborative optimization methods to ensure seamless operations. The research is driven by the need for precise control of key assembly parameters, such as placement height, x-offset, y-offset, rotation angle deviations, and blowing durations, all of which are major contributors to defects. To address these challenges, we propose a self-adaptive collaborative optimization (SACO) framework that prioritizes enhancements based on their impact on both quality and efficiency. The SACO framework combines customized Bayesian optimization and particle swarm optimization techniques, allowing for dynamic adjustments to process parameters, guided by real-time data from automatic optical inspection (AOI) systems. The primary goal of this study is to reduce defects and improve efficiency in the SMT assembly process through these targeted improvements. Experimental results validate the effectiveness of the proposed methods, demonstrating significant advancements in placement accuracy and overall assembly efficiency. Our findings confirm that the SACO framework provides a robust solution to persistent challenges in SMT production, addressing critical gaps in quality control and process optimization.

Printed circuit board assembly line scheduling (PCBALS) is a difficult task in the electronic industry for assembly lines using surface mounters, which is critical for production efficiency. This is a special type of line optimization problem that uses different allocation techniques, resulting in wide differences in assembly times between machines. This article proposes a hyper-heuristic optimizer embedded with a multifeature fusion ensemble estimator (HHO-MFEE) for PCBALS using linear-aligned-heads surface mounters. The objective and constraints of the problem are discussed, and a min-max integer model for small-scale problems is built. At the hyper-heuristic low level, seven data- and target-driven heuristics are presented for allocating components to different machines. Strategies for duplicated conditions with component types and placement points allocation are proposed to improve the applicability of the algorithm and the quality of the solution. An ensemble assembly time estimator that incorporates the coding of multifeatures, including estimated subobjectives, is proposed for evaluating the quality of the solution. Experimental results show that: 1) the gaps between the solution from HHO-MFEE and the optimal solution of the model are 3.44%~7.28% for small-scale data; 2) the proposed time estimator has higher accuracy than regression and heuristic-based ones, with mean absolute error of 2.01% and 3.43% for training and testing data, respectively; and 3) HHO-MFEE is better than other state-of-the-art algorithms, with average improvement of 7.21%~9.47%.

The optimization of fuzzy grey cognitive map (FGCM) can enhance the decision quality of the system in managing uncertainties and incomplete information. Addressing this issue requires a method that effectively balances the competing demands of the speed and precision in the optimization process. Therefore, a tradeoff genetic algorithm (TOGA) is proposed to refine the FGCM optimization process in this article. First, a modified fitness function with a penalty term is designed to improve the convergence rate, which can drive the FGCM population to obtain the optimal solution during the evolutionary process. Second, an adaptive genetic mechanism based on horizontal comparison and longitudinal assessment, is designed to strike a balance between accelerating convergence and avoiding the dilemma of falling into local optima. Finally, in the simulation section, the effectiveness of the proposed method is validated by optimizing FGCM using synthetic datasets and applying it toa spacecraft debris threat avoidance scenario.

Route scheduling for automatic optical inspection (AOI) of printed circuit boards (PCBs) impacts the productivity of surface mount production lines. Current state-of-the-art mathematical models in the area are not rigorous enough and neglect significant practical constraints, such as component geometric constraints. This article proposes a hierarchical mixed integer programming model to describe the route scheduling problem for AOI of PCBs. The model allows theoretical optimal solutions to be obtained for small-scale problems. In addition, a two-stage heuristic framework, consisting of clustering and path planning stages, is proposed to improve efficiency in solving large-scale problems, achieving near-optimal solutions. Taking into account that component distribution affects clustering results, the clustering stage is developed with a hierarchical heuristic algorithm based on block density with an aggregation strategy. The Lin–Kernighan algorithm is first used to quickly generate the scheduling sequence in the path planning stage. Image acquisition centers are initially adjusted with a customized heuristic. After that, a hybrid evolutionary multitask algorithm is proposed to further reduce path distance by dividing the image acquisition center adjustment task into several subtasks using heuristic rules. The algorithm obtains better quality results and is faster than traditional evolutionary algorithms. Experiments on an actual industrial AOI platform demonstrate that the proposed two-stage heuristic route scheduling algorithm outperforms state-of-the-art research in the area.

Dual-gantry surface mount optimization effectively improves the productivity of printed circuit board assembly (PCBA), but also brings new challenges. Optimizing workload allocation to balance the front and rear gantry placement completion time is a significant challenge for improving PCBA productivity. This study proposes a finite potential game heuristic algorithm (FPGHA) to solve the workload allocation problem. The algorithm generates game agents by analyzing the feeding characteristics of the dual-gantry placement machine and using an improved bisection K-means clustering method. Agent utility is calculated based on metrics affecting productivity of the pick-and-place process, including the number of simultaneous pickups, nozzle changes, cycles, and mounting points. Nash equilibrium of FPGHA is obtained by a best-response dynamics and heuristic algorithm. Then, the effectiveness of FPGHA in solving the workload allocation problem is first demonstrated in simulated experiments with different nozzle and feeder configurations. Finally, FPGHA is compared with the hierarchical restricted balance algorithm, adaptive clustering algorithm, and the popular industrial optimizer software in actual placement experiments using real-world industrial printed circuit boards. The effectiveness and accuracy of FPGHA are verified by analyzing the correlation between three variables: The FPGHA estimated value, the actual assembly value, and the PCB assembly time.

Meta-metric learning has demonstrated strong performance in coarse-grained few-shot situations. However, despite their simplicity and availability, these metametrics are limited in effectively handling fine-grained few-shot scenarios. Fine-Grained Few-Shot Classification (FGFSC) presents significant challenges to the network's ability to extract subtle features. Equipped with the symmetrical binocular perception system and complex neural networks in the brain, humans inherently possess exceptional and resilient meta-learning abilities, facilitating superior management of fine-grained few-shot scenarios. In this paper, inspired by the human binocular visual system, we pioneer the first human-like meta-metric paradigm: Binocular Singular Hellinger Metametric (BinoHeM). Functionally, BinoHeM incorporates advanced symmetric binocular feature encoding and recognition mechanisms. Structurally, it integrates two binocular sensing feature encoders, a singular Hellinger metametric, and two collaborative identification mechanisms. Building on this foundation, we introduce two innovative metametric variants: BinoHeM-KDL and BinoHeM-MTL. These are grounded in two advanced training mechanisms: knowledge distillation learning (KDL) and meta-transfer learning (MTL), respectively. Furthermore, we showcase the high accuracy and robust generalization capabilities of our approaches on four representative FGFSC benchmarks. Extensive comparative and ablation experiments have validated the efficiency and superiority of our paradigm over other state-of-the-art algorithms. Our code is publicly available at: https://github.com/ChaofeiQI/BinoHeM.

The issue of fuzzy-observer-based (FOB) fault-tolerant (FT) steering control for autonomous driving with persistent disturbances via model predictive control approach is studied in this article, where velocity variation, potential faults, nonlinearity, and unmeasurable states are considered simultaneously. Due to the variable longitudinal velocity during cruising on diverse road conditions and various vehicle maneuvers, the Takagi–Sugeno fuzzy method is employed to handle parameter variations in the steering control system. For the sake of obtaining information on fault behavior and unmeasurable system states, this article constructs a fuzzy observer to estimate the fault signal and faulty system states. Improved conditions for designing the observer gains are proposed, such that the estimation error converges to a minimal robust positively invariant set. Subsequently, this article introduces a FOB FT model predictive controller, which is designed through the resolution of a Min-Max optimization issue. In the end, the benefits of the approach developed in this article are validated by utilizing the Carsim/Matlab joint simulation.

This article investigates a sliding-mode resilient control problem for uncertain strict-feedback nonlinear cyber-physical systems (CPSs) subject to deception attacks. The main characteristic of the proposed sliding-mode control method is its ability to mitigate undesirable system behaviors caused by deception attacks in a fixed-time interval. In particular, a novel fixed-time integral nonsingular terminal sliding-mode (INTSM) surface is designed recursively by adding a power integrator (API) technique, wherein the fractional power can be chosen to ensure the fixed-time stability of the closed-loop system and to estimate the upper bound of settling time. In addition, an adaptive super-twisting reaching law is proposed to counteract the deception attacks and ensure the continuous control action of CPSs, effectively reducing the effects of the chattering problem. Simulation results are given to demonstrate the effectiveness of the proposed sliding-mode resilient control scheme.

This article studies the asynchronous dissipative-based sliding mode control (SMC) for nonlinear Markov jump system (MJS) by Takagi–Sugeno (T–S) fuzzy models, which suffer from mismatched parameter uncertainties and exogenous disturbances. The actual system modes are usually not accessible directly for controller design due to some practical environment constraints, and the modes of the sliding surface and controller are characterized as observed modes. The hidden Markov model (HMM) is first utilized to detect the system mode information, which also describes the asynchronous phenomenon of the jump mode between the sliding surface/controller and the original plant. Based on the observed modes, a suitable sliding surface is designed via consisting of both the system states and the control input. By sufficiently exploiting the dynamical features of the fuzzy MJSs and associating with the sliding surface, new sliding surface existence conditions are proposed, which also ensure the stochastic stability (SS) and desired dissipative performance for the sliding motion. Then, a novel asynchronous dynamic SMC law is proposed to drive the fuzzy MJS states into a neighborhood of the designed sliding surface. Finally, simulation studies are conducted to show the validity of the proposed approach.

This article addresses the challenge of state observer design for sliding mode security control in Markov jump cyber-physical systems subjected to stochastic injection attacks. To enhance network efficiency, a dynamic event-triggered algorithm is introduced in the communication channel. First, the design begins with a Luenberger state observer featuring an adaptive compensator. This configuration aims to effectively counteract malicious attacks. Second, an integral sliding hyperplane is formulated within the estimation space, which serves as the foundation for deriving the sliding mode dynamics, ensuring robustness against disturbances. Recognizing the diversity of transition rates (TRs), an elastic sliding mode controller is designed to accommodate three distinct types of TRs, which is also strategically designed to guarantee reachability and maintain sliding motion. Third, stochastic stability with an$H_{\infty }$attenuation level is conducted separately for each type of TR. Correspondingly, the development of an algorithm for determining threshold parameters in triggered conditions is presented. Simultaneously, a proof of the nonexistence of Zeno behavior is provided, ensuring the stability and efficiency of the proposed system. Finally, a simulation study using a practical model is included to empirically demonstrate the validity of the proposed method in a real-world context.

In this article, the twistors modeling method and high-order fully actuated (HOFA) system approach are utilized to investigate the attitude and orbit integrated control problem of spacecraft. Initially, a first-order state-space model is formulated to represent the attitude and orbit dynamics of spacecraft. This model is subsequently transformed into a HOFA system model. Based on this transformed model, a control strategy is meticulously designed. With the developed control strategy, a linear closed-loop system is obtained, whose poles can be arbitrarily configured. The effectiveness of the proposed control strategy is ultimately verified through detailed simulation results.

This study addresses the problem of guaranteed cost fault-tolerant fuzzy control for multiline re-entrant manufacturing systems (RMSs) against stochastic disturbances and workstation faults. Initially, a nonlinear hyperbolic impulsive partial differential equation model is employed to describe the complex and hybrid dynamics of RMSs suffering from unexpected faults within the working stations, and then the corresponding approximation T-S fuzzy model is constructed. In what follows, with the aid of the parallel distributed compensation fuzzy control scheme, the main results of stability analysis and controller synthesis for the closed-loop re-entrant manufacturing control system are derived using a timer-dependent Lyapunov functional with spatio-temporal auxiliary variables. It is found that by means of the proposed fault-tolerant control approach, the RMS can be effectively and robustly driven to a desired production mode with steady feeding and production rates while the upper bound of a quadratic cost function is minimized. Finally, the effectiveness of the proposed control approach is validated through numerical simulations.

It is nontrivial to achieve practical predefined-time (PPT) control for uncertain nonlinear systems (UNSs) in strict-feedback form. The main technical difficulty is that there is no relevant lemma to prove the predefined-time stability of the UNS with unknown control directions. This article proposes an adaptive neural predefined-time control scheme, capable of achieving PPT control for UNS with unknown control directions. The presented scheme is derived from a PPT function, associating with backstepping technique, generating a predefined-time control solution. Meanwhile, a dynamic event-triggered schedule is developed to update the control law, therefore reducing data transmission. Finally, an illustrative example is given to illustrate the effectiveness of the presented scheme.

For stochastic nonlower triangular nonlinear systems subject to dead-zone input, a neuroadaptive tracking control frame is constructed by applying the dynamic surface technique with a state observer in this work. Its primary contribution lies in extending the stability criteria to encompass stochastic nonlinear systems characterized by nonlower triangular structures and unmeasured states. The control strategy is delineated as follows. First, the state observer is designed to address the issue of unmeasured states, thereby facilitating the generation of an error dynamics system for subsequent analysis. Second, within the backstepping design framework, a neural network-based tracking controller is devised using dynamic surface control technique and variable separation approaches, ensuring system performance despite the presence of unmeasured states. Finally, stability analysis is conducted to guarantee that all the system signals remain bounded. Simulation examples are presented to illustrate the validity and practicality of the framework.

This article deals with the leader-following consensus problem of multiple uncertain Euler-Lagrange systems with unknown nonlinear dynamics. By introducing a dynamic compensator for each agent, a fully distributed control strategy is developed based on the fuzzy approximation approach, which is independent of any priori global information associated with the communication topology. Meanwhile, a distributed event-triggering mechanism (ETM) is designed such that each agent broadcasts its states only when an event occurs. It is shown that with the proposed ETM, the leader-following consensus is achieved with aperiodic intermittent communication and Zeno behavior is excluded by contradiction. Moreover, the consensus tracking errors converge to small sets around the origin. Finally, an example is provided to illustrate the effectiveness of the obtained theoretical results.

This article investigates the attitude containment control problem for multiple spacecrafts based on optimized backstepping design strategy. First, the spacecrafts' attitude dynamics are modeled by modified Rodrigues parameters and are rewritten in the strict-feedback form. Then, we incorporate the idea of optimal control into each step of backstepping design procedure. At each step, the optimal virtual/actual control law is obtained via actor–critic reinforcement learning algorithm and approximated by fuzzy logic systems (FLSs). The updating laws of the parameter matrices are designed in a more concise form and the persistent excitation condition is relaxed. According to the Lyapunov stability analysis, the containment error and FLS parameter errors are semiglobally uniformly ultimately bounded. Finally, a simulation example is given to illustrate the effectiveness of the proposed method.

In this work, we consider the detection of velocity sensor faults for autonomous underwater vehicles in a bounded error context. The nonlinearity of autonomous underwater vehicles is handled via a Takagi–Sugeno fuzzy technique. The fault detection is achieved by solving a zonotopic enclosure problem based on zonotopic reachability analysis. To improve fault detection performance, a novel observer structure and H$_{-}$performance index are introduced. The performance of the presented approach is analyzed utilizing a hidden fault set. Experimental examples are given to validate the effectiveness of the presented approach.

The optimization of printed circuit board assembly (PCBA) for a beam head placement machine is a multivariable and multiconstraint combinatorial problem. Current techniques falter in solving a variety of PCBA problems since heuristic algorithms lack theoretical guarantees of optimality, and mathematical modeling methods have high computational complexity for the whole problem. This article proposes a novel two-phase optimization for PCBA, integrating the advantages of mathematical modeling with heuristic algorithms. We divide the problem into the head task assignment and the placement route schedule. For the former, an effective integer linear programming model with component partition is proposed, encompassing key efficiency-influencing factors. A recursive heuristic-based initial solution speeds up the solving convergence, while the reduction strategies enhance model solvability. For the placement route schedule, a tailored greedy algorithm yields high-quality solutions, leveraging the results of the model, and an aggregated route relink heuristic does further optimization. In addition, we propose a selection criterion for the solution pool of the model to pre-evaluate the placement movement, which builds the connection between the two phases. Finally, we validate the performance of the two-phase optimization, which provides an average efficiency improvement of 8.66%–21.83% compared to other mainstream research.

Surface mount technology is essential to the development of the electronic manufacturing industry. This article studies optimizing the surface mount process for the beam-head placement machine. A mixed-integer programming (MIP) model is proposed for this problem, which is decomposed into three interconnected hierarchical parts: feeder allocation; component assignment; and pick-and-place (PAP) sequence problems. This article proposes an efficient hierarchical framework with three elaborately designed heuristics to solve the above problem. The design of the scan-based algorithms optimizes the subobjectives of feeder allocation and component assignment. First, the allocation heuristic arranges the feeders into slots as a prerequisite for other problems. Then, the component assignment heuristic determines the component type for each head with a variety of criteria and long short-term objectives. Finally, the PAP sequence problem is solved using a modified beam search algorithm. The proposed algorithm offers advantages in terms of effectiveness, efficiency, and extension, which can satisfy various customization demands. Experiments are conducted on our self-designed placement machine using industrial and randomly generated data. Computational experiments show that the scan-based heuristic algorithm obtains near-optimal solutions with a gap of 9.93% averagely compared with the proposed MIP model and provides efficiency improvement over the mainstream studies.

Subspace predictive control (SPC) is a widely utilized data-driven control technique in various industrial applications. However, its static nature restricts its ability to effectively track nonlinear dynamic systems, resulting in diminished performance. To address this problem, an adaptive subspace predictive control approach is proposed, incorporating an adaptive mechanism to continuously update the subspace predictor. The designed adaptive mechanism mitigates the negative impact of historical data by sliding the data window. It simultaneously employs the addition and deletion of data vectors in the data matrix through recursive matrix transformation, simplifying computational complexity while maintaining accuracy. In addition, the developed subspace predictor enables online learning and effectively handles the dynamic nature of industrial processes, requiring little prior knowledge. The theoretical analysis of the proposed control approach includes recursive feasibility and stability, along with a discussion on determining relevant parameters. The effectiveness of the proposed control approach is demonstrated through its application to a continuous stirred tank heater benchmark. The results exhibit significant improvements in tracking control performance, leading to enhanced efficiency and cost reduction. Overall, this research presents a promising solution for addressing the challenges of predictive control in industrial processes.

This article investigates the optimal consensus problem for general linear multiagent systems (MASs) via a dynamic event-triggered approach. First, a modified interaction-related cost function is proposed. Second, a dynamic event-triggered approach is developed by constructing a new distributed dynamic triggering function and a new distributed event-triggered consensus protocol. Consequently, the modified interaction-related cost function can be minimized by applying the distributed control laws, which overcomes the difficulty in the optimal consensus problem that seeking the interaction-related cost function needs all agents' information. Then, some sufficient conditions are obtained to guarantee optimality. It is shown that the developed optimal consensus gain matrices are only related to the designed triggering parameters and the desirable modified interaction-related cost function, relaxing the constraint that the controller design requires the knowledge of system dynamics, initial states, and network scale. Meanwhile, the tradeoff between optimal consensus performance and event-triggered behavior is also considered. Finally, a simulation example is provided to verify the validity of the designed distributed event-triggered optimal controller.

This article studies the asynchronous observer-based output feedback control for the continuous-time networked nonlinear systems via the Takagi–Sugeno (T–S) fuzzy affine models with quantized measurements. To tackle the premise variables’ asynchronous phenomena between the original plant and the observer/controller, an asynchronous piecewise fuzzy affine observer design approach is proposed to estimate the immeasurable system state vectors via making full utilization of some advanced matrix inequalities, a novel existence criterion for the asynchronous observer-based piecewise output feedback controller is derived in an unified convex optimization setup, which successfully relax the constraint that the control input matrices are required to be common and uncertainty-free under each fuzzy rules. Simulation studies are provided to justify the efficacy of the proposed method.

In this article, a velocity-free adaptive controller is proposed for the tracking control of servo mechanisms with friction compensation. A continuously differentiable friction model is employed to compensate for the dominant friction nonlinearity of servo mechanisms. Besides, a projection-type adaptive law is applied to handle parameter uncertainties in the system model. Since only the output position signal is directly measurable, an adaptive extended state observer (AESO) is constructed to estimate the indeterminate velocity state, which can also provide an estimation of unmodeled dynamics. Moreover, the dynamic gain switching of AESO can effectively suppress the peaking phenomenon at the motion beginning. Specifically, the parameter adaptation law utilizes the desired velocity state instead of the estimated value, avoiding the coupling problem between parameter and state estimation. The proposed control strategy theoretically demonstrates the transient performance and boundedness of the error in output tracking. Asymptotic stabilization of the system can also be implemented when only parameter uncertainty exists. Comparative experiments are conducted on a linear motor platform to demonstrate the effectiveness of the proposed control scheme.

This work investigates the problem of piecewise affine (PWA) filtering design for two-dimensional (2-D) Roesser nonlinear systems with finite frequency performance based on Takagi–Sugeno (T–S) fuzzy affine models. The goal is to synthesize a 2-D PWA filter such that the resulting filtering error system is asymptotically stable and simultaneously satisfies a finite frequency$\mathscr{H}_{\infty}$performance$\gamma$. With the utilization of the state space partition knowledge on 2-D fuzzy models, a novel PWA filter is obtained. By exploiting the 2-D Fourier transform technique to convert 2-D disturbances into their frequency domain counterparts, finite frequency$\mathscr{H}_{\infty}$performance analysis conditions are established, and then by applying projection lemma, an admissible frequency information-based filter design approach is proposed for 2-D Roesser nonlinear systems. Finally, the effectiveness of the PWA finite frequency filtering synthesis approach is validated through simulation studies on two examples.

Observer-based methods are widely used to estimate the disturbances of different dynamic systems. However, a drawback of the conventional disturbance observers is that they all assume persistent excitation (PE) of the systems. As a result, they may lead to poor estimation precision when PE is not ensured, for instance, when the disturbance gain of the system is close to the singularity. In this paper, we propose a novel disturbance observer based on concurrent learning (CL) with time-variant history stacks, which ensures high estimation precision even in PE-free cases. The disturbance observer is designed in both continuous and discrete time. The estimation errors of the proposed method are proved to converge to a bounded set using the Lyapunov method. A history-sample-selection procedure is proposed to reduce the estimation error caused by the accumulation of old history samples. A simulation study on epidemic control shows that the proposed method produces higher estimation precision than the conventional disturbance observer when PE is not satisfied. This justifies the correctness of the proposed CL-based disturbance observer and verifies its applicability to solving practical problems.

This article investigates the fault estimation (FE) problem for a class of nonlinear systems via an adaptive fuzzy approach. Considering the limited communication capacity of networks, the quantized measurement signals are used to construct adaptive laws instead of the real measurements in the designed fuzzy observer. By injecting the quantizer parameter into the observer inputs, the quantization effects on the convergence of estimation errors can be compensated. It is also shown that nondifferentiable actuator faults can be reconstructed by the developed FE approach. Finally, two simulation examples are provided to illustrate the validity of the presented scheme.

Compared with traditional rigid objects' dynamic throwing and catching by the robot, the in-flight trajectory of nonrigid objects (incredibly variable centroid objects) throwing is more challenging to predict and track. This article proposes a variable centroid trajectory tracking network (VCTTN) with the fusion of vision and force information by introducing force data of throw processing to the vision neural network. The VCTTN-based model-free robot control system is developed to perform highly precise prediction and tracking with a part of the in-flight vision. The flight trajectories dataset of variable centroid objects generated by the robot arm is collected to train VCTTN. The experimental results show that trajectory prediction and tracking with the vision-force VCTTN is superior to the ones with the traditional vision perception and has an excellent tracking performance.

This work investigates the issue of output-feedback sliding-mode control (SMC) for nonlinear 2-D systems by Takagi-Sugeno fuzzy-affine models. Via combining with the sliding surface, the sliding-mode dynamical properties are depicted by a singular piecewise-affine system. Through piecewise quadratic Lyapunov functions, new stability and robust performance analysis of the sliding motion are carried out. An output-feedback dynamic SMC design approach is developed to guarantee that the system states can converge to a neighborhood of the sliding surface. Simulation studies are given to verify the validity of the proposed scheme.

This article tackles the problem of filtering design for continuous-time Roesser-type 2-D nonlinear systems via Takagi–Sugeno (T-S) fuzzy affine models. The aim is to design an admissible piecewise affine (PWA) filter such that the filtering error system is asymptotically stable with a prescribed disturbance attenuation level. First, 2-D Roesser nonlinear systems are approximated by a kind of 2-D fuzzy affine models with norm-bounded uncertainties. Then, the premise variable space of the 2-D fuzzy affine systems is partitioned into two classes of subspaces, that is: 1) crisp regions and 2) fuzzy regions. For each region, boundary continuity matrices and characterizing matrices are constructed by utilizing the space partition information and 2-D structure. After that, novel piecewise Lyapunov functions are constructed, based on which together with$S$-procedure, the asymptotic stability with$\mathcal H_{\infty }$performance is guaranteed for the filtering error system. By the projection lemma and some elegant convexification techniques, the PWA$\mathcal H_{\infty }$filtering design conditions are obtained. Finally, the less conservativeness and effectiveness of the proposed approach over a common Lyapunov function-based one are illustrated by simulation studies.

This article addresses the adaptive secure control problem for a class of uncertain nonlinear large-scale cyber-physical systems (CPSs) subjected to deception attacks. Specifically, a novel adaptive fuzzy control scheme for each subsystem of CPSs is designed to mitigate the effects of cyberattacks that intentionally tamper with control signals from controllers to actuators and the state signals from sensors to controllers, and a Nussbaum function is proposed to tackle unknown time-varying control directions. Moreover, a finite-time converging secure control scheme is designed for compromised CPSs to guarantee all the states converge to a predetermined small set in finite time. It is shown that with the proposed control scheme, all the signals of the closed-loop system are proved to be semiglobally bounded. Two simulation examples are provided to illustrate the effectiveness of the proposed secure control method.

This article investigates the nonsynchronous-observer -based output-feedback control of nonlinear systems by Takagi–Sugeno fuzzy affine models. In order to estimate the unmeasurable system states and handle the nonsynchronization issue of premise variables between the observer/controller and the original system, a new nonsynchronous piecewise fuzzy affine observer is first synthesized to estimate the unmeasurable system state variables. Via utilizing some elegant matrix inequalities with convexification techniques, new observer-based piecewise output-feedback controller design results are proposed within the framework of piecewise quadratic Lyapunov functions. Simulation studies are given to illustrate the validity of the developed approach.

The fuzzy observer-based output feedback control of continuous-time nonlinear two-dimensional (2-D) systems is studied by Takagi–Sugeno (T–S) fuzzy models in this work. The plant information of the 2-D systems evolves along two independent directions dynamically. The nonlinear 2-D systems are firstly expressed by T–S fuzzy models with parameter uncertainties. By a Lyapunov method together with some convexification techniques, two methods are developed for fuzzy observer-based output feedback controller synthesis of the underlying fuzzy 2-D systems, and novel output feedback controller synthesis results are proposed within a convex optimization setup. Simulation studies are provided to illustrate the validity of the proposed methods.

The observer-based output feedback control problem for nonlinear systems is studied through Takagi–Sugeno fuzzy affine models. A novel piecewise fuzzy affine observer is designed to estimate the unmeasurable state variables. To remove the assumptions in the existing works that the system input matrices and/or output matrices should be common, full rank, and uncertainties-free, a new approach is developed for the observer-based piecewise output feedback controller synthesis by making sufficient use of Young's inequality with its variance. Through fuzzy piecewise quadratic Lyapunov functions, improved and less conservative results on the output feedback controller design are obtained in terms of linear matrix inequalities, and the restrictions imposed in the existing results can be relaxed. Simulation studies are provided to confirm the effectiveness and conservatism reduction of the developed method.

We consider the tracking control problem for a class of stochastic nonlinear systems in strict-feedback structure via output feedback signal in this article. The controlled plant is assumed subject to unknown dead-zone input. By utilizing the fact that the unknown dead-zone input function can be modeled as a time-varying nonlinear function and a bounded disturbance, and selecting appropriate design parameters, we show that the effect of unknown dead zone can be compensated. We design a fuzzy state observer via fuzzy logic modeling technique to estimate the unknown system states. Then, we prove, via Lyapunov stability analysis, that the controlled plant is bounded in probability. In addition, all the signals in the closed-loop system are guaranteed to be globally bounded in probability. The tracking errors are also ensured to converge to a small neighborhood of the origin. Finally, to show the effectiveness of the proposed control strategy, a simulation example of one-link manipulator is presented in the simulation section.

This article proposes a hierarchical multiobjective heuristic (HMOH) to optimize printed-circuit board assembly (PCBA) in a single beam-head surface mounter. The beam-head surface mounter is the core facility in a high-mix and low-volume PCBA line. However, as a large-scale, complex, and multiobjective combinatorial optimization problem, the PCBA optimization of the beam-head surface mounter is still a challenge. This article provides a framework for optimizing all the interrelated objectives, which has not been achieved in the existing studies. A novel decomposition strategy is applied. This helps to closely model the real-world problem as the head task assignment problem (HTAP) and the pickup-and-place sequencing problem (PAPSP). These two models consider all the factors affecting the assembly time, including the number of pickup-and-place (PAP) cycles, nozzle changes, simultaneous pickups, and the PAP distances. Specifically, HTAP consists of the nozzle assignment and component allocation, while PAPSP comprises place allocation, feeder set assignment, and place sequencing problems. Adhering strictly to the lexicographic method, the HMOH solves these subproblems in a descending order of importance of their involved objectives. Exploiting the expert knowledge, each subproblem is solved by an elaborately designed heuristic. Finally, the proposed HMOH realizes the complete and optimal PCBA decision making in real time. Using industrial PCB datasets, the superiority of HMOH is elucidated through comparison with the built-in optimizer of the widely used Samsung SM482.

This article focuses on the sampled-data output-feedback control problem for nonlinear systems represented by Takagi-Sugeno fuzzy affine models. An input delay approach is adopted to describe the sample-and-hold behavior of the measurement output. Via augmenting the system states with the control input, the resulting closed-loop system is converted into a singular system first. Based on the piecewise quadratic Lyapunov-Krasovskii functionals, some novel results on the sampled-data piecewise affine output-feedback controller design are attained by employing some convexification techniques. The simulation studies are presented to illustrate the effectiveness of the proposed scheme.

The reachable set estimation problem for a class of Markovian jump neutral-type neural networks (MJNTNNs) with bounded disturbances and time-varying delays is tackled in this article. With the aid of the delay partitioning method, a novel stochastic Lyapunov-Krasovskii functional containing triple integral terms is constructed in mode-dependent augmented form. To begin with, transition probabilities of the concerned Markovian jump neural networks (NNs) are considered to be completely known. By employing the integral inequality approach and reciprocally convex combination method, it is proved that all state trajectories which start from the origin by bounded inputs can be constrained by an ellipsoid-like set if a group of linear matrix inequalities (LMIs) is feasible. Then, the free-connection weighting matrix technique is utilized to handle the case of partially known transition probabilities. As byproducts, some sufficient conditions are also obtained to guarantee the stochastic stability of the concerned NNs. The validity of the theoretical analysis is confirmed by numerical simulations.

The issue of fuzzy adaptive switching control for stochastic systems with arbitrary switching signal and finite-time prescribed performance is investigated in this article. A piecewise function is adopted to characterize finite-time prescribed performance, and the error signal is converted to a new state variable via the tangent function. Unknown functions are approximated via fuzzy-logic systems (FLSs). Based on the stochastic stability theory and common Lyapunov function, a fuzzy adaptive switching control scheme is presented. The control law is proposed for the stochastic switched closed-loop system so that not only all the signals are ensured to be semiglobally uniformly ultimately bounded (SGUUB) in probability but also a residual error related to the finite-time prescribed performance bound is guaranteed. Eventually, simulation studies for a practical system are given to show the effectiveness of the presented fuzzy adaptive switching control scheme.

This article addresses the event-triggered adaptive fuzzy output-feedback control problem for a class of nonstrict-feedback nonlinear systems with asymmetric and time-varying output constraints, as well as unknown nonlinear functions. By designing a linear observer to estimate the unmeasurable states, a novel event-triggered adaptive fuzzy output-feedback control scheme is proposed. The barrier Lyapunov function (BLF) and the error transformation technique are used to handle the output constraint under a completely unknown initial tracking condition. It is shown that with the proposed control scheme, all the solutions of the closed-loop system are semiglobally bounded, and the tracking error converges to a small set near zero, while the output constraint is satisfied within a predetermined finite time, even when the constraint condition is violated initially. Moreover, with the proposed event-triggering mechanism (ETM), the Zeno behavior can be strictly ruled out. An example is finally provided to demonstrate the effectiveness of the proposed control method.

This article studies the fault estimation problem for a class of continuous-time nonlinear Markovian jump systems with unmeasured states, unknown bounded sensor faults, and unknown nonlinearities simultaneously. In this article, a new adaptive fuzzy observer design scheme is developed, where the completely unknown nonlinear terms are approximated by adaptive fuzzy logic systems. By means of a novel online adaptive mechanism, the asymptotic stability of the error dynamic system is guaranteed despite of sensor faults and unknown nonlinear terms. Moreover, the sliding surface switching problem in the traditional sliding mode observer techniques can be avoided for Markovian jump systems. Finally, two practical examples are given to demonstrate the effectiveness of the proposed approach.

A new stability analysis framework, step-function method, is developed to study the stability of more general impulsive systems, which greatly expands the existing Lyapunov-like stability approaches. The proposed method does not require the monotonicity and continuity of Lyapunov-like functions in each impulsive interval and is expected to address the stability problem for a wider class of hybrid systems, including the state-dependent and event-triggered impulsive systems where the minimal dwell time may vanish. Moreover, the proposed method can also be applied to the stability problem of Zeno point. We propose the construction of one-span step functions and multiple-spans step functions, and present several less-conservative stability conditions for T–S fuzzy impulsive systems. Two examples demonstrate the efficiency of the proposed methods.

This article studies the disturbance observer-based adaptive fuzzy finite-time control issue of strict-feedback nonlinear systems. Specifically, to meet practical application requirement, the finite-time prescribed performance is considered, which can guarantee the tracking error enters into the prescribed bounded set in a known time. A disturbance observer is proposed to estimate the external disturbance. It is proved that the closed-loop system is semi-globally practically finite-time stable. Finally, simulation studies for a one-link manipulator are shown to verify the effectiveness of the proposed approach.

Optimizing all the objectives of the printed circuit board assembly (PCBA) optimization in a multifunctional placer remains a formidable challenge till now. This article converts the original PCBA optimization problem to a newly defined component allocation problem, which decides the component-type handled by each head per pickup-and-place (PAP) cycle. The component allocation problem is a quadratic 3-D assignment problem (Q3AP) and effectively combines the optimization of all the main objectives. It is possible that one head stays idle, so the assigning 2-D locations are uncertain. We propose the cell division genetic algorithm (CDGA) to solve such a complex Q3AP. The CDGA allocates a component cell as the basic unit. Each of the first-generation component cells contains the mounting points of the same type. A cell chromosome decoding heuristic is designed to determine the next assigning head. By doing so, the problem dimension is reduced, so the conventional GA can be used for searching the optimal component allocation formed by the current-generation cells. When a better allocation can no longer be found by allocating the current cells, the cell division operation is performed to divide each cell into two new cells. The new cells are used in the next round of GA searching, which further optimizes the allocation from two perspectives: better balancing the minimization of nozzle changes and PAP cycles, more flexibly maximizing the simultaneous pickups with the uncertain locations. The CDGA works continuously until the current cells cannot bring any improvement. In simulations and experiments using the industrial samples, the proposed algorithm significantly reduces the PCBA time compared to two recent studies and the built-in optimizer of the widely used multifunctional placer, Hanwha SM482 PLUS, which demonstrates its effectiveness and superiority.

This article investigates the command filtering-based event-triggered adaptive fuzzy control problem for a class of stochastic nonlinear systems with stochastic faults and input saturation. The unknown nonlinear functions and system dynamic changes that caused by stochastic faults are approximated by fuzzy logic systems (FLSs). In order to reduce the computational burden, the command filtering design technique is incorporated into the adaptive fuzzy event-triggered control strategy. Finally, the effectiveness of the proposed method is verified by simulation studies, in which the uniform ultimate boundedness of the system is guaranteed and all the signals in the closed-loop system are bounded.

This article presents an automated strategy to touch the injection site on zebrafish larva skin with the injection pipette tip accurately in the presence of water-depth variation, which is a crucial problem to automate zebrafish larva microinjection. The presented method consists of two parts: adaptive coordinate transformation and curve evolution for edge detection. In the first part, the impact of refraction is taken into consideration. An adaptive calibration method is developed, which enables the coordinate transformation matrix to adapt to the changing water depth. In the second part, the abovementioned calibration result is used to keep the injection pipette tip descending along the desired route. A curve-evolution-based edge detection algorithm is introduced to detect the deformation of larva skin caused by contact with the injection pipette tip. Experimental results demonstrate that high accuracy and success rates are achieved. The effect of uncertainties caused by water-depth variation and the skill requirement in manual manipulation are eliminated. The proposed contact detection strategy can be extended to microinjection for other organisms.Note to Practitioners—As a typical multicellular model organism, the zebrafish has been increasingly used in biological research. For studying drug toxicity and disease models, exogenous substances need to be injected into zebrafish larvae. However, for both manual and automated injection, a fatal problem is that the camera on the microscope only provides 2-D positional information. It is laborious to align the pipette tip with the injection site along the$z$-axis. Moreover, due to the characteristic of stereomicroscopes, the impact of refraction at the water surface cannot be ignored. In order to address these issues, in this article, we present an adaptive calibration method and an edge detection algorithm for zebrafish larva heart injection to avoid contact failure in practical implementations.

Piecewise affine (PWA) systems are attractive models that can represent various hybrid systems with local affine subsystems and polyhedral regions due to their universal approximation properties. The identification problem of PWA systems amounts to estimating the number of subsystems, parameters of each subsystem, and the corresponding polyhedral partitions via state-input vectors. In this paper, we propose a novel approach to address the online identification problem of continuous-time PWA systems in state-space form. Specifically, an online active mode recognition algorithm and a generalized integral concurrent learning identifier are presented to acquire the number of subsystems, the switching sequence, and the parameter of each subsystem. In addition, we develop the optimization problem for the polyhedral partition estimation, which is solved by using the estimated switching sequence and subsystem parameters. The effectiveness of the proposed identification approach is demonstrated via simulation results.

The problem of fuzzy-based adaptive event-triggered tracking control is investigated for a class of non-strict-feedback nonlinear systems within fixed-time interval in this paper. Fuzzy logic system (FLS) is employed to approximate the packaged unknown nonlinearities. Event-triggered mechanism is employed to schedule the data transmission dependent upon errors exceeding certain threshold. And combining backstepping design algorithm with Lyapunov stability theorem, a fuzzy-based adaptive event-triggered controller is developed. The presented control scheme eliminates the singularity problem that may occur in the design process and guarantees the boundedness of all the closed-loop signals. And the tracking error converges into a small neighborhood of zero in fixed-time. Meanwhile, the Zeno behavior is also avoided. At last, the effectiveness of the proposed approach is demonstrated by simulation results.

This article studies the asynchronous sampled-data filtering design problem for Itô stochastic nonlinear systems via Takagi-Sugeno fuzzy-affine models. The sample-and-hold behavior of the measurement output is described by an input delay method. Based on a novel piecewise quadratic Lyapunov-Krasovskii functional, some new results on the asynchronous sampled-data filtering design are proposed through a linearization procedure by using some convexification techniques. Simulation studies are given to illustrate the effectiveness of the proposed method.

This article investigates the adaptive fuzzy fault-tolerant control problem for a class of strict-feedback stochastic nonlinear systems with quantized input signal. A hysteretic quantizer is utilized to avoid chattering caused by quantized input signals. The fuzzy-logic systems are utilized to approximate the unknown nonlinear functions and also to construct the fuzzy state observer, which is used to estimate the immeasurable state vector. The actuator faults considered in this article are loss of effectiveness and lock-in-place faults. By using the Lyapunov stability theory, the closed-loop stochastic nonlinear system is guaranteed to be stable in probability, and all the signals of the closed-loop system are bounded in probability in the presence of quantized input and actuator faults. Finally, a simulation example is given to verify the validity of the proposed control strategy.

This article studies the problem of finite frequency fault detection filtering design for uncertain nonlinear systems based on interval type-2 Takagi-Sugeno fuzzy models. It is assumed that the frequencies of disturbances and faults are in finite frequency sets, respectively. The objective is to design an admissible filter such that the fault detection system is asymptotically stable with prescribed finite frequency \mathscr H∞and \mathscr H-performances. Based on Fourier transform and Projection lemma, finite frequency filtering synthesis results are obtained. Then, a novel membership-function-dependent finite frequency fault detection filtering design approach is proposed by using the information of the lower and upper membership functions together with the footprint of uncertainties. Two algorithms with linear matrix inequality constraints are developed to optimize the finite frequency \mathscr H∞performance and the finite frequency \mathscr H-performance, respectively. Finally, simulation studies are provided to show the effectiveness of the proposed method.

This article studies the problem of finite-frequency static output feedback (SOF) \mathscr H∞controller design for discrete-time Roesser-type two-dimensional (2-D) nonlinear systems based on Takagi-Sugeno (T-S) fuzzy models. The 2-D Roesser nonlinear systems are described by T-S fuzzy models with parameter uncertainties. The objective is to design a SOF controller guaranteeing the asymptotic stability of the resulting closed-loop system with finite frequency \mathscr H∞performance. Via a system state-input augmentation technique, the closed-loop system is formulated in a descriptor form. Then, based on fuzzy Lyapunov functions and some elegant convexification procedures, the SOF controller design approach is proposed. It is shown that the controller gains can be obtained by solving a set of linear matrix inequalities. Finally, simulation studies are given to demonstrate the effectiveness of the proposed method.

This article focuses on the integral sliding mode control (SMC) issue for nonlinear singularly perturbed systems via T-S fuzzy models. A new sufficient criterion for the sliding surface design is attained by adopting convexification techniques, which also guarantees the asymptotic stability of the sliding motion. Then, a new dynamic SMC synthesis scheme is developed to ensure the finite-time reachability of the sliding surface. A simulation example is shown to illustrate the validity of the developed scheme.

This article investigates the adaptive fuzzy control problem for a class of stochastic nonlinear systems with the risk-sensitive performance index. The desired cost level of the risk-sensitive index, which could be arbitrarily small, is guaranteed by the solution of a specified Hamilton–Jacobi–Bellman (HJB) equation. By considering the unknown uncertainties of the stochastic nonlinear systems, a novel adaptive fuzzy risk-sensitive control method is proposed, which guarantees the input-to-state stability of the system. In addition, the proposed control strategy also reduces the conservatism of the existing adaptive robust control method. Finally, the effectiveness of the proposed approach is verified by a simulation example of one-link manipulator.

This article investigates the problem of adaptive fuzzy decentralized tracking control design for a class of large-scale interconnected nonlinear systems with network-induced input time delay. First, fuzzy logic systems are used to approximate the unknown nonlinear functions. Then, a newly defined filtered error is introduced to compensate the network-induced delay, and a novel adaptive fuzzy decentralized control strategy is proposed to guarantee the uniform ultimate boundedness of the closed-loop nonlinear system. Finally, a simulation example is given to further demonstrate the effectiveness of the proposed method.

The system information of 2-D systems is usually in propagation along two independent directions. This article focuses on the issue of output feedback sliding mode control (SMC) for 2-D nonlinear systems through T-S fuzzy affine models. Associated with the sliding surface function, a singular system is established to describe the sliding mode dynamics. Based on piecewise quadratic Lyapunov functions, some new stability analysis results on the sliding mode dynamical system are attained, and the analysis conservatism can be further reduced. A robust output feedback dynamic SMC synthesis scheme is developed to guarantee the finite-time reachability of the sliding surface. It is also worthy of mentioning that the proposed dynamic SMC synthesis method can handle the mismatched disturbances, and the control input channels are permitted to have parameter uncertainties. Simulation studies are given to illustrate the validity of the developed scheme.

This article addresses the sampled-data piecewise affine (PWA) filter design problem for Itô stochastic nonlinear systems represented by Takagi–Sugeno fuzzy affine models. An input delay method is used to describe the sample-and-hold behavior of the measurement output. Based on a novel piecewise quadratic Lyapunov–Krasovskii functional, some new results on the robust sampled-data PWA filtering design are proposed through a linearization procedure by using some convexification techniques. Simulation studies on a tunnel diode circuit system, and an inverted pendulum system are given to illustrate the effectiveness of the proposed method.

This article devotes to investigating the issue of fuzzy adaptive control for a class of strict-feedback nonlinear systems with nonaffine nonlinear faults. The computational complexity is reduced by adopting the dynamic surface control technique. Under the framework of finite-time stability, a novel fault-tolerant control strategy is designed so that the closed-loop system is semiglobally practically finite-time stable, and the tracking error converges to a small residual set in a finite time. Finally, simulation studies for an electromechanical system are shown to verify the feasibility of the presented approach.

In this article, an event-triggered robust fuzzy adaptive prescribed performance finite-time control strategy is presented for a class of strict-feedback nonlinear systems with external disturbances. The relative-threshold-based event-triggered signal is introduced to reduce communication burden, and the dynamic surface control technique is applied to address the computational complexity problem. A disturbance observer is designed to estimate the compounded disturbances, which are composed of external disturbances and fuzzy approximation errors. The proposed control strategy can guarantee that the closed-loop system is semiglobally practically finite-time stable, and the tracking error converges to a small residual set by incorporating the prescribed performance bound in finite-time. Finally, simulation results are provided to verify the effectiveness of the proposed robust fuzzy control strategy.

This article deals with the zonotopic fault detection for fuzzy systems with event-triggered mechanism. An$\ell _1/h_{\infty }$fault detector design method is deduced with considering the asynchronous premise variables caused by event-triggered transmission, such that the residual signal is sensitive to system failure while robust to amplitude-bounded disturbance, and noise. Then, a novel zonotopic residual evaluation process is constructed by converting the asynchronization of premise variables into system uncertainties. Different from most published works on event-triggered fault detection where constant thresholds are utilized for residual evaluation, zonotopic thresholds for residual signal are designed with taking the influences of disturbance, noise, asynchronous premise variables, and event-based transmission into consideration. Finally, a numerical example is utilized to verify the effective performance of the developed strategy.

This article focuses on the problem of fault detection filtering design for discrete-time interval type-2 Takagi-Sugeno (T-S) fuzzy systems in finite frequency domain. Considering the fact that external disturbances and faults are usually reside in finite frequency ranges, the finite frequency H∞and H-performances are introduced to reflect the disturbance robustness and fault sensitiveness in finite frequency domain, respectively. Based on discrete-time Fourier transform and its properties, finite frequency performance analysis results are first obtained. Then, by exploiting the information on upper and lower membership functions, the membership-function-dependent filtering design conditions in the form of linear matrix inequalities are established for discrete-time interval type-2 T-S fuzzy systems in finite frequency domain. With the obtained filter, a fault detection scheme is then proposed and it is shown that the resulting fault detection system is asymptotically stable with prescribed finite frequency H∞and H-performances. Finally, the effectiveness of the proposed method is validated by simulation studies.

This article investigates the event-triggered adaptive tracking control for a class of pure-feedback stochastic nonlinear systems with full state constraints and input saturation. The saturated input is expressed as a smooth nonlinear function with bounded disturbance. The pure-feedback structure is transformed into strict-feedback case via mean value theorem, and a novel event-triggered adaptive fuzzy tracking control scheme with relative threshold is then proposed. The barrier Lyapunov function is introduced to analyze the system stability, and the state constraints are, thus, guaranteed. It is proved that the closed-loop stochastic nonlinear system is semiglobally uniformly ultimately bounded in probability, and the output tracking error converges to a small neighborhood of zero. Finally, the effectiveness of the proposed method is verified via simulation studies.

In this article, the problem of steering control based on Takagi-Sugeno (T-S) fuzzy vehicle lateral dynamics is investigated for autonomous driving with nonlinearities, system uncertainties, and actuator constraints. During normal vehicle cruising, the vehicle velocity always changes due to the different road conditions and/or steering wheel maneuvers, and moreover, the vehicle dynamics is also significantly influenced by the tire/road forces under different road surface conditions, which brings many difficulties in steering controller design. By adopting fuzzy modeling techniques and varying look-ahead control strategy, an approach to the T-S fuzzy antiwindup output feedback controller design is proposed for the steering control in path tracking within T-S fuzzy-model-based analysis framework, where the actuator amplitude saturation and rate limit are simultaneously taken into consideration. Finally, valuation results with Carsim/MATLAB joint simulation are shown to demonstrate the effectiveness of the developed methods, and some comparison results in path tracking performance with fixed look-ahead distance control rule and the driver model controller embedded in Carsim are provided, which illustrate the advantages of the developed controller design method.

The visual inspection algorithms are the core of automatic optical inspection system on surface mounting machines. This article is concerned with the development of precise positioning algorithms for circular mark points and transistor (TR) components on surface mounting devices. To handle nonuniform illumination or occlusion of other components, a polar coordinate transform and smoothness selection based circular mark point location method is proposed. The TR components are fundamental chips in electronic products and have various package types. The illumination changes, background disturbance, and the diversity of package types have imposed great challenges on the development of the uniform algorithm for detection and location of TR components. To deal with these issues, the 1-D integral image based TR component detection and location algorithm is proposed and the coordinates and orientation of the component are calculated simultaneously. The efficiency of the proposed methods is tested on real images and compared with classical Hough transform method, commercial algorithms on SMT482 device, and two methods of Halcon software.

This article investigates the adaptive learning control problem for a class of nonlinear autonomous underwater vehicles (AUVs) with unknown uncertainties. The unknown nonlinear functions in the AUVs are approximated by radial basis function neural networks (RBFNNs), in which the weight updating laws are designed via gradient descent algorithm. The proposed gradient descent-based control scheme guarantees the semiglobal uniform ultimate boundedness (SUUB) of the system and the fast convergence of the weight updating laws. In order to reduce the computational burden during the backstepping control design process, the command-filter-based design technique is incorporated into the adaptive learning control strategy. Finally, simulation studies are given to demonstrate the effectiveness of the proposed method.

To reduce the labor effort in hazardous environments like spray painting, an automatic car part spray painting machine has been set up. This article presents an intelligent location procedure for this machine. The location procedure assumes that the car part is placed with a tiny arbitrary pose, furthermore some kinds of parts under inspection undergoes through the deformation and thus it contains a nonrigid model. To tackle this problem, a workpiece positioning system works though multicamera is established first. Subsequently, a novel modified iterative closest point (ICP) algorithm is proposed which registers the nonrigid shape to the undeformed source shape in the training library. The modified ICP combines the ideas from traditional ICP and deformation estimation from bounded biharmonic weights. It solves a nonlinear cost function by using Levenberg–Marquardt algorithm. As a result, it estimates the transformation of the target point cloud with regards to the source point cloud. Additionally, it improves the accuracy of traditional ICP and increases its scope to nonrigid shapes. By employing these results in our location procedure, it can estimate the 6-DOF pose of the car part to be painted in addition to that it also estimates the deformation compared to the source cloud in the training library. This information is subsequently used to modify the guidance trajectory of the spray gun. In this article, a test case of front bumper is given, as it is commonly made of soft plastic materials and undergoes deformation under the influence of the external force. All the stated results support the efficacy of our algorithm.

This paper investigates the problem of output feedback sliding mode control (SMC) for a class of uncertain nonlinear systems through Takagi-Sugeno fuzzy affine models. By adopting a state-input augmentation method, a descriptor system is first constructed to characterize the dynamical properties of the sliding motion. Based on a common quadratic Lyapunov function and piecewise quadratic Lyapunov functions, sufficient conditions for asymptotic stability analysis of the sliding motion are obtained with some convexification techniques. An output feedback dynamic SMC design scheme is proposed to force the states of the resulting closed-loop system onto the sliding surface locally in finite time. Two simulation examples are finally shown to illustrate the effectiveness of the proposed approaches.

This article investigates the neural network-based finite-time control issue for a class of nonstrict feedback nonlinear systems, which contain unknown smooth functions, input saturation, and error constraint. Radial basis function neural networks and an auxiliary control signal are adopted to identify unknown smooth functions and deal with input saturation, respectively. The issue of error constraint is solved by combining the performance function and error transformation. Based on the backstepping recursive technique, a neural network-based finite-time control scheme is developed. The developed control scheme can ensure that the closed-loop system is semi-globally practically finite-time stable. Finally, the validity of theoretical results is verified via simulation studies.

This article studies the issue of adaptive neural network (NN) control for strict-feedback multi-input and multioutput (MIMO) nonlinear systems with full-state constraints and actuator hysteresis. Radial basis function NNs (RBFNNs) are introduced to approximate unknown nonlinear functions. The command filter is adopted to solve the issue of "explosion of complexity." By applying a one-to-one nonlinear mapping, the strict-feedback system with full-state constraints is converted into a new pure-feedback system without state constraints, and a novel NN control method is proposed. The stability of the closed-loop system is proved via the Lyapunov stability theory, and the tracking errors converge to small residual sets. The simulation results are given to confirm the validity of the proposed method.

This paper tackles the problem of piecewise affine memory filtering design for the discrete-time norm-bounded uncertain Takagi-Sugeno fuzzy affine systems. The objective is to design an admissible filter using past output measurements of the system, guaranteeing the asymptotic stability of the filtering error system with a given [Formula: see text] performance index. Based on the piecewise fuzzy Lyapunov functions and the projection lemma, a new sufficient condition for [Formula: see text] filtering performance analysis is first derived, and then the filter synthesis is carried out. It is shown that the filter gains can be obtained by solving a set of linear matrix inequalities. In addition, it is also shown that the filtering performance can be improved with the increasing number of past output measurements used in the filtering design. Finally, two examples are presented to show the advantages and effectiveness of the proposed approach.

This article investigates the state and fault estimation problem for switched systems with external disturbances, unknown nonlinear dynamics, and actuator and sensor faults. In this article, a new adaptive fuzzy-based observer is designed, where the prior knowledge of the nonlinearities, disturbances, and faults is not required. In contrast to the sliding mode observer (SMO), the proposed observer can guarantee the convergence of the error trajectory through the online adaptive mechanism, avoiding the sliding surface switching problem in SMO design. The accurate state asymptotic estimations can also be provided by the developed design approach subject to the simultaneous additive and multiplicative faults. Finally, a simulation example is presented to show the effectiveness of the proposed method.

This paper addresses the output feedback sliding-mode control (SMC) problem for discrete-time uncertain nonlinear systems through Takagi-Sugeno fuzzy dynamic models. Combining with the sliding surface, a descriptor system is constructed to characterize the sliding motion dynamics. Sufficient conditions for asymptotic stability analysis of the sliding motion are attained by the piecewise quadratic Lyapunov functions within a convex optimization setup. Two SMC design approaches are proposed to ensure the finite-time convergence of the sliding surface. Two simulation examples are presented to show the effectiveness of the proposed approaches.

This article addresses the finite-time adaptive fuzzy control problem for a class of nonstrict-feedback uncertain nonlinear systems via an event-triggered strategy. A novel design scheme, consisting of finite-time adaptive fuzzy controller and event-triggering mechanism (ETM), is proposed to decrease the number of data transmission and the number of control actuation updates. With the proposed event-triggered adaptive fuzzy control scheme, all the solutions of the resulting closed-loop system are guaranteed to be semi-globally bounded within finite time. Moreover, the feasibility of the proposed ETM is verified by excluding Zeno behavior. In contrast to existing results on similar problems, the restrictions on nonlinearities are relaxed and the more general uncertain nonlinear systems are considered. Finally, an example is provided to illustrate our theoretical results.

This article investigates region stabilization issue of switched neural networks (SNNs) with multiple modes (MMs) and multiple equilibria (ME) via a pole assignment method. In such an SNN, every neuron is observed with more than one mode and unstable equilibrium point. First, SNNs with MMs and ME are modeled in terms of switched systems with unstable subsystems and ME. Second, a necessary and sufficient condition and a sufficient condition are, respectively, proposed for arbitrary switching paths pole assignment and arbitrary periodic/quasi-periodic switching paths (PSPs/QSPs) asymptotically region stabilizing pole assignment of switched linear time-invariant (LTI) systems with ME. It is shown that to stabilize a switched LTI system, some/all poles of all/some linear subsystems can be assigned to suitable locations of the right-half side of the complex plane. Third, based on the obtained pole assignment results, an asymptotical-region-stabilizing-control law observed as distributed state feedback controllers of MMs, asymptotical-region-stabilizing PSPs/QSPs, and a corresponding algorithm are all designed for asymptotical region stabilization of switched linear/nonlinear neural networks with MMs and ME. Finally, a numeral example is given to illustrate the effectiveness and practicality of the new results.

The problem of H∞filtering to estimate the unmeasurable states is investigated in this paper and the most general multirate measurements condition is taken into account. The main result of this paper is to give a method to design a filter, which can guarantee the stability of the resultant filtering error system with H∞performance. For a given system with multirate measurements, this paper first provides a method to convert this multirate design problem into an equivalent single-rate design problem. Then, it can be proved that the design approach of a required filter based on a linear matrix inequality is a sufficient and necessary condition. Lastly, two examples are utilized to demonstrate that this design approach is effective and applicable to estimate the unmeasurable states for the given system with multirate measurements.

In this paper, we study the adaptive fuzzy output feedback control problem for a class of nonstrict-feedback nonlinear systems with output constraint. The case of asymmetric and time-varying output constraint together with unmeasurable states is considered. By integrating the barrier Lyapunov function (BLF) approach and the fuzzy approximation technique, we show that the output constrained adaptive fuzzy output feedback control problem for a class of uncertain nonlinear systems can be solved. Finally, the effectiveness of the main result is demonstrated by simulation studies of a one-link manipulator system.

This paper studies the problem of fuzzy adaptive event-triggered control for a class of pure-feedback nonlinear systems, which contain unknown smooth functions and unmeasured states. Fuzzy logic systems are adopted to approximate unknown smooth functions and a fuzzy state observer is designed to estimate unmeasured states. Via the event-triggered control technique, the control signal of the fixed threshold strategy is obtained. By converting the tracking error into a new virtual error variable, an observer-based fuzzy adaptive event-triggered prescribed performance control strategy is designed. The key advantage is that the proposed method does not require a priori knowledge of partial derivatives of system functions, i.e., it relaxes the restrictive condition that the partial derivatives of system functions need to be known for pure-feedback nonlinear systems. Simulation results confirm the efficiency of the proposed method.

The problem of adaptive fuzzy control is investigated for a class of nontriangular structural stochastic switched nonlinear systems with full state constraints in this paper. A remarkable feature of the nontriangular structural nonlinear system is the so-called algebraic loop problem in the existing backstepping-based analysis and design. Properties of fuzzy basis functions are utilized to circumvent this algebraic loop problem. Based on the Barrier Lyapunov function, an adaptive fuzzy stochastic switched control scheme is designed. It is proven that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded with full state constraints. The effectiveness of the proposed control scheme is verified via simulation studies.

This paper considers a robust adaptive fuzzy control problem for a class of uncertain nonlinear systems via an event-triggered control strategy. Fuzzy logic systems are used to approximate the unknown nonlinear functions in the nonlinear system. A novel robust adaptive control scheme together with a novel event-triggering mechanism (ETM) is proposed to reduce communication burden. It should be noted that both the control signal and the adaptive parameters are updated only at the triggering time instants in the proposed scheme, which further saves the system energy and resources. It is shown that with the proposed event-triggered robust adaptive control scheme, all the signals in the closed-loop system are guaranteed to be semiglobally bounded, and the output of the system converges to a small neighborhood of the origin. Moreover, with the proposed ETM, Zeno behavior can be strictly excluded. Finally, a one-link manipulator system is used to demonstrate the effectiveness of the proposed control scheme.

In this paper, the problem of steering control is investigated for vehicle path tracking in the presence of parametric uncertainties and nonlinearities. In practice, the vehicle mass varies due to the number of passengers or amount of payload, while the vehicle velocity also changes during normal cruising, which significantly influences vehicle dynamics. Moreover, the vehicle dynamics are strongly nonlinear caused by the tire/road forces under different road surface conditions. With fuzzy modeling method, the original nonlinear path tracking system with parameter variations is first formulated as a T-S fuzzy model with additive norm-bounded uncertainties, and then an approach to the fuzzy observer-based output feedback steering control for vehicle dynamics is proposed under a fuzzy Lyapunov function framework. By employing matrix inequality convexifying techniques, a sufficient condition is developed in the form of linear matrix inequalities such that the closed-loop path tracking error system is asymptotically stable with a guaranteed H∞level. Finally, the effectiveness of the proposed fuzzy observer-based output feedback controller is demonstrated in Carsim/Matlab joint simulation environment, via which the advantage of a T-S fuzzy observer-based output controller over the closed-loop driver model embedded in Carsim is also shown with parametric uncertainties and nonlinearities.

This paper examines the problem of membership-function-dependent controller design for a class of discrete-time T-S fuzzy systems. Based on the partition method of premise variable space, the original T-S fuzzy model is equivalently converted into a piecewise-fuzzy system. Then, by employing some staircase functions, the continuous membership functions are approximated by a series of discrete values via which the information of membership functions is brought into the stability analysis to reduce the design conservatism. With piecewise-Lyapunov functions, the approaches to the piecewise-fuzzy state feedback and observer-based output feedback controller design are proposed, respectively, in terms of linear matrix inequalities such that the closed-loop system is asymptotically stable with a prescribed H∞performance level. It is shown that the membership functions of the fuzzy model and fuzzy controllers are not necessarily the same, which allows more design flexibility. Finally, two illustrative examples are provided to show the effectiveness of the developed methods.

In this work, the fuzzy observer-based control problem is investigated for a class of nonlinear coupled systems, which consists of a hyperbolic partial differential equation (PDE) containing nonlinearities and a nonlinear ordinary differential equation (ODE). The nonlinear coupled system is represented as a Takagi-Sugeno (T-S) fuzzy coupled hyperbolic PDE-ODE model. Based on the T-S fuzzy model, a novel Lyapunov functional approach is proposed to design a fuzzy observer based control strategy. More specifically, a fuzzy observer is presented to estimate the state variables of the fuzzy coupled PDE-ODE system with the measurements of the PDE, and the exponential convergence of the observer error is proved. Then, a fuzzy controller is given utilizing the estimated states as feedback variables, and it is proved that the evolution profiles of the PDE and the trajectory of the ODE in the closed-loop fuzzy system converge exponentially to the desired values, respectively. The sufficient existence conditions of the fuzzy observer based controller are formulated in terms of a set of space differential linear matrix inequalities (SDLMIs). A recursive algorithm based on the finite-difference approximation and the linear matrix inequality techniques are provided to solve the SDLMIs. Finally, the results are applied to case study of a predator-prey system, and the simulations are performed to illustrate the effectiveness of the proposed observer based control law.

The autonomous operation of industrial robots with minimal human supervision has always been in high demand. To prepare the autonomous operation of a car part spray painting robot, novel object detection, and pose estimation algorithms have been developed in this paper. The object detection part used principal components analysis (PCA) to reduce the dimension of three-dimensional (3-D) point cloud to 2-D binary image. Distance measure between the auto and cross correlation of the binary features was established to find out the similarity between them. Resultantly, the type of auto part was successfully obtained. Furthermore, iterative closest point (ICP) algorithm was used to estimate the pose difference of the auto part with respect to the camera reference frame, which was mounted on the robot. An issue with ICP's lack of robustness to local minimum was solved by the combination of ICP and genetic algorithm (GA). This allowed the optimization of pose error and addressed the problem of local minimum entrapment in ICP. For experimental validation: the proposed object recognition pipeline was implemented in both serial and parallel programming paradigms. The results were obtained for the acquired point clouds of side body car parts and compared with the major 3-D object detection systems in terms of computational cost. Pose estimation error was calculated with both ICP and the modified point set registration schemes, and it was shown to be decreasing in the case of later. All shown results supported the research claims.

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

This paper deals with a real-time-weighted observer-based fault-detection (FD) scheme for Takagi-Sugeno (T-S) fuzzy systems. The essential idea is to develop a weighted diagnostic observer-based FD system to optimize the worst case robustness and fault sensitivity simultaneously by using the information provided by each local system. To achieve an early detection of potential fault, the robustness issue is investigated in the L∞/L2observer-based FD context. Meanwhile, the L-fault sensitivity condition is addressed to optimize the fault detectability. Using fuzzy Lyapunov functions, sufficient conditions on the FD system design are studied. Two examples are given in the end to show the efficiency of the proposed results.

This paper investigates the robust control problem of nonlinear systems with unknown time-varying actuator faults over digital communication networks. In the study, a new adaptive sliding mode control (SMC) scheme is developed for the investigated nonlinear systems, where the unknown nonlinearity is approximated via the adaptive fuzzy mechanism in the presence of signal quantization. The proposed control law can compensate the time-varying faults and the quantization errors completely by injecting the quantizer parameter into the controller gain. Moreover, in this design, the adaptive fuzzy law is constructed via the quantized state information instead of using the exact original information. Under the proposed quantized SMC scheme, the closed-loop control systems are guaranteed to be asymptotically stable, and the reachability of the sliding surface can be ensured strictly. Finally, two examples are presented to show the effectiveness of the method.

In this paper, an adaptive fault estimation approach is proposed for a class of switched nonlinear systems. The considered system is assumed to possess unknown nonlinearities, unmeasured states, and simultaneous sensor and actuator faults. Fuzzy logic systems are applied to approximate the unknown nonlinear terms. Two new adaptive fuzzy observers are designed where the sensor and actuator faults can be estimated separately. On the basis of average dwell time approach and Lyapunov stability theory, the resulting error system is proven to be bounded stable with designed parameters. Finally, a simulation example is presented to illustrate the effectiveness of the designed observers.

This paper is devoted to the data-driven fault detection of nonlinear systems. For our purpose, the definition of Takagi-Sugeno fuzzy data-driven forms of kernel representations for nonlinear systems is introduced first, which builds the basis of our work. The major contributions consist of two parts. In the first part, a data-driven method for fuzzy process modeling is proposed, and associated with it, some modeling issues are addressed with the aid of the so-called randomized algorithm technique in the probabilistic framework. It is followed by a data-driven realization of fuzzy kernel representation and its implementation in the fault detection system design. To link the data-driven methods to the well-established observer-based fault detection approaches, the recursive form of the fuzzy kernel representation is proposed. In the second part, the fuzzy-observer-based fault detection design scheme is investigated based on the recursive fuzzy kernel representation. The main results of our study are illustrated by an experimental study on the laboratory setup of a three-tank system.

The state-feedback decentralized stabilization problem is considered for interconnected nonlinear systems in the presence of unmodeled dynamics. The functional relationship in affine form between the strong interconnected functions and error signals is established, which makes backstepping-based fuzzy control successfully generalized to strong interconnected nonlinear systems. By combining adaptive control with both backstepping design and the approximation property of fuzzy systems, an adaptive decentralized control algorithm is developed. It is demonstrated by both theoretical analysis and simulation study that the proposed control strategy ensures semiglobally uniformly ultimately bounded of all signals within the closed-loop systems.

In this paper, we will investigate the problem of finite frequency memory fixed-order output feedback controller design for Takagi–Sugeno (T–S) fuzzy affine systems. It is assumed that the disturbances reside in a finite frequency range, i.e., the low, middle, or high frequency range. The objective is to design a memory piecewise affine (PWA) controller by using past output measurements to guarantee the asymptotic stability of the resulting closed-loop system with a prescribed finite frequency $\mathscr H_{\infty }$ performance. Via the system state-input augmentation, a novel descriptor system approach is proposed to facilitate the controller design. All the design conditions are formulated in the form of linear matrix inequalities. It is also proven that the $\mathscr H_{\infty }$ performance can be improved with the memory control strategy. Finally, simulation studies are presented to show the effectiveness of the proposed design method.

An intelligent data-driven predictive control strategy is proposed in this paper. The predictive controller is designed by combining predictive control and local weighted projection regression. The presented control strategy needs less prior knowledge and has fewer parameters that are hard to determine compared to other data-driven predictive controller, e.g., the one in dynamic partial least square (PLS) framework. Furthermore, the proposed predictive controller performs better in the control of nonlinear processes and is able to update its parameters based on the online data. The predictive model validity and intelligence of the control strategy are guaranteed by the online updating strategy to a certain degree. The control performance of the proposed predictive controller against the model predictive control (MPC) in dynamic PLS framework is illustrated through the simulation of a typical numerical example and the benchmark of a continuous stirred tank heater system. It can be observed from the simulation that the proposed MPC strategy has higher prediction precision and stronger ability in coping with nonlinear dynamic processes which are quite common in practical applications, for instance, the industrial process.

This paper addresses the problem of delay-dependent robust and reliable, H∞static output feedback (SOF) control for uncertain discrete-time piecewise-affine (PWA) systems with time-delay and actuator failure in a singular system setup. The Markov chain is applied to describe the actuator faults behaviors. In particular, by utilizing a system augmentation approach, the conventional closed-loop system is converted into a singular PWA system. By constructing a mode-dependent piecewise Lyapunov-Krasovskii functional, a new H∞performance analysis criterion is then presented, where a novel summation inequality and S-procedure are succeedingly employed. Subsequently, thanks to the special structure of the singular system formulation, the PWA SOF controller design is proposed via a convex program. Illustrative examples are finally given to show the efficacy and less conservatism of the presented approach.

A data-driven model predictive control (MPC) that combines modified partial least squares (PLSs) and MPC is proposed in this paper. A theoretical comparison among traditional MPC, MPC in PLS framework and in modified PLS framework is presented, which demonstrates that the proposed MPC approach has high prediction precision and the ability in coping with dynamics in the process compared to MPC in traditional PLS framework. Furthermore, the proposed MPC requires no prior knowledge, and the simplicity in computation makes it possible to update the prediction model online. The model validity and intelligence of the control strategy are guaranteed by the model updating strategy to a certain degree. Steady-state performance and dynamic response of the proposed MPC is testified through a tracking control simulation of the benchmark of a continuous stirred tank heater system, which illustrates that the advantages of the proposed MPC.

This paper investigates the problem of delay-dependent H∞memory filtering for continuous-time semi-Markovian jump linear systems (MJLSs) with time-varying delay in an input-output framework. Differing from the constant transition rates (TRs) in the conventional MJLSs, the TRs of the semi-MJLSs depend on the random sojourn-time and are thus with time-varying characteristics. By utilizing a two-term approximation for the terms with time-varying delay, it is first shown that the filtering error system (FES) can be reformulated into a feedback interconnection form and the stability and performance analysis problem of the FES can be recast as the scaled small gain (SSG) problem of an interconnected system. Then, based on a semi-Markovian Lyapunov-Krasovskii formulation of SSG condition combined with projection lemma, the H∞filter synthesis for the underlying semi-MJLSs is formulated in terms of linear matrix inequalities. Finally, simulation studies are provided to evaluate the effectiveness and superiority of the proposed design method.

Three-dimensional (3D) morphological information of Zebrafish larvae is important for investigating the development of the vertebrate model. Some existing automated handling systems have already been developed to reconstruct 3D models of micro-objects, but many commercial devices are generally costly and complicated to assemble, which limits their wide usage. In this paper, we present an automated visual servo platform to carve 3D model of Zebrafish larva in a simple and controllable manner. The proposed 3D carving strategy only involves a 4-DOF manipulator, a glass capillary and a micropump. The Zebrafish larva is first captured by the capillary mounted at the end of the manipulator. Then, the manipulator rotates larva body to desired orientations in order to obtain 2D images from different views. A structure-from-motion algorithm finally carves the 3D model of the larva body. Experimental results verify the validity of proposed methods, and a guideline of selecting the number of views is also given. As a high-cost-performance system, it has a considerable reference for reconstructing other microobjects.

The purpose of this paper is to design an adaptive reliable H∞controller for multirate sampled-data systems. Sampled-data systems are widely adopted in the industrial process and multirate sampling is abundant in such systems. Sensor failure is one of the faults exist in the control systems, which can result in the instability. A reliable controller is proposed in this article to guarantee the stability and H∞performance of the multirate sampled-data systems when sensor failures happen. The lifting technique is used to convert a multirate system to an equivalent discrete system and the idea of the substitution is utilized to address the controller design problem based on adaptive mechanism. With the use of the mathematical model of the practical F-404 engine, an example is illustrated to prove the applicability of the proposed method.

Zebrafish has been demonstrated to be an important model organism in the study of genetics, diseases and drugs. For investigating drug toxicity, we need to inject foreign substances into specific organs within zebrafish larvae. Traditionally, zebrafish larva microinjection is conducted manually and requires operators to control larva's orientation with flexible ends, which is time-consuming, labor-intensive, and inaccurate. In this paper, we present an integrated microfluidic system to facilitate zebrafish larva organs microinjection, which is capable of adjusting larvae to appropriate orientation conveniently. The head's direction of zebrafish is adjusted through a microfluidic chip and a series of pumps so that the larva moves tail-first at the exit of the microfluidic channel, where it is rotated around its body axis to a desired orientation. Finally, the binary image of the larva is analysed to locate the organs and injection is executed. Experimental results are presented to verify the efficiency of the proposed method.

In this study, the problem of filtering for sampled‐data systems under unreliable communication links is investigated. The phenomena of data packet dropouts and signal transmission delays are addressed in a unified framework. The objective is to design an admissible filter to guarantee the asymptotic stability of the filtering error system and minimise the disturbance attenuation level. Thanks to the proposed novel Lyapunov–Krasovskii functional together with the improved Wirtinger's inequality and the reciprocally convex approach, novel sufficient linear‐matrix‐inequality‐based conditions are obtained for the existence and design of admissible filters. Finally, two simulation examples are provided to illustrate the efficiency and less conservativeness of the proposed filter design methods.

This paper is concerned with a real-time observer-based fault detection (FD) approach for a general type of nonlinear systems in the presence of external disturbances. To this end, in the first part of this paper, we deal with the definition and the design condition for an £∞/£2type of nonlinear observer-based FD systems. This analytical framework is fundamental for the development of real-time nonlinear FD systems with the aid of some well-established techniques. In the second part, we address the integrated design of the £∞/£2observer-based FD systems by applying Takagi-Sugeno (T-S) fuzzy dynamic modeling technique as the solution tool. This fuzzy observer-based FD approach is developed via piecewise Lyapunov functions, and can be applied to the case that the premise variables of the FD system is nonsynchronous with the premise variables of the fuzzy model of the plant. In the end, a case study on the laboratory setup of three-tank system is given to show the efficiency of the proposed results.

This paper deals with the problem of robust unknown input fault detection observers (UIFDOs) design for interval type-2 Takagi-Sugeno (T-S) fuzzy systems with immeasurable premise variables. It has been shown that choosing the system states, which may be immeasurable, as premise variables, will able us to model a larger class of nonlinear systems. Accordingly, the premise variables of underlying system are considered to be immeasurable. However, the design procedure of a stable observer for such systems are more challenging. Furthermore, the system is supposed be affected by time-varying delays and unknown inputs. The UIFDO is exploited so as to generate a residual signal with the most possible sensitivity to fault and the least sensitivity to exogenous signals. In this paper, this issue is investigated thoroughly, in this respect the design procedure consists of two sections: 1) measurable and 2) immeasurable premise variables. Sufficient design conditions are provided in terms of linear matrix inequalities for both cases. The effectiveness of the proposed UIFDO in detection of two different kinds of faults is illustrated during the simulation of a numerical example. Moreover, a fair comparison has been drawn between the proposed UIFDO and an existent reference to indicate that interval type-2 T-S fuzzy model is more superior than type-1. Finally, the faulty behavior of a one-link manipulator is investigated to express the applicability of the proposed method.

Data-driven fault detection plays an important role in industrial systems due to its applicability in case of unknown physical models. In fault detection, disturbances must be taken into account as an inherent characteristic of processes. Nevertheless, fault detection for nonlinear processes with deterministic disturbances still receive little attention, especially in data-driven field. To solve this problem, a just-in-time learning-based data-driven (JITL-DD) fault detection method for nonlinear processes with deterministic disturbances is proposed in this paper. JITL-DD employs JITL scheme for process description with local model structures to cope with processes dynamics and nonlinearity. The proposed method provides a data-driven fault detection solution for nonlinear processes with deterministic disturbances, and owns inherent online adaptation and high accuracy of fault detection. Two nonlinear systems, i.e., a numerical example and a sewage treatment process benchmark, are employed to show the effectiveness of the proposed method.

Due to the unavailability of full state variables in many control systems, this paper is concerned with the design of reliable observer-based output feedback controller for a class of network-based Takagi-Sugeno fuzzy systems with actuator failures. In order to better allocate network resources under the case that the sensor nodes are physically distributed, the decentralized event triggering communication scheme is adopted such that each sensor node is capable to determine the transmission of its local measurement information independently. Considering that the implementation of the controller may not be synchronized with the plant trajectories due to asynchronous premise variables with such communication mechanism, a novel piecewise fuzzy observer-based output feedback controller is developed. By applying a piecewise Lyapunov function and some techniques on matrix convexification, an approach to the design of observer and controller gain is derived for the augmented closed-loop system to be asymptotically stable with a guaranteed H∞performance and reduced transmission frequency. Finally, two examples are given to show the effectiveness of the developed method.

This paper proposes a novel system-augmentation approach to the delay-dependent reliable piecewise-affine ℋ∞static output feedback control for nonlinear systems with time-varying delay and sensor faults in the piecewise-Markovian-Lyapunovfunctional-based framework. The nonlinear plant is described by a continuous-time Takagi-Sugeno fuzzy-affine model with parametric uncertainties, and the sensor faults are characterized by a Markov process. Specifically, by applying a state-input augmentation technique, the original closed-loop system is first reformulated into a descriptor fuzzy-affine system. Based on a new piecewise-Markovian Lyapunov-Krasovskii functional, combined with a Wirtinger-based integral inequality, improved reciprocally convex inequality, and S-procedure, a novel bounded real lemma is then derived for the underlying closed-loop system. Furthermore, by taking advantage of the redundancy of descriptor system formulation, together with a linearization procedure, the piecewiseaffine controller synthesis is carried out. It is shown that the desired piecewise-affine controller gains can be attained by solving a linear matrix inequality based optimization problem. Finally, simulation examples are performed to confirm the effectiveness and less conservatism of the presented approach.

This paper deals with the problem of reliable and robust H∞static output feedback (SOF) controller synthesis for continuous-time nonlinear stochastic systems with actuator faults. The nonlinear stochastic plant is expressed by an Itô-type Takagi-Sugeno fuzzy-affine model with parametric uncertainties, and a Markov process is employed to model the occurrence of actuator fault. The purpose is to design an admissible piecewise SOF controller, such that the resulting closed-loop system is stochastically stable with a prescribed disturbance attenuation level in an sense. Specifically, based on a Markovian Lyapunov function combined with Itô differential formula, S-procedure, and some matrix inequality convexification procedures, two new approaches to the reliable SOF controller analysis and synthesis are proposed for the underlying stochastic fuzzy-affine systems. It is shown that the existence of desired reliable controllers is fully characterized in terms of strict linear matrix inequalities. Finally, simulation examples are presented to illustrate the effectiveness and advantages of the developed methods.

This paper presents an adaptive neural network (NN)-based fault-tolerant control approach for the compensation of actuator failures in nonlinear systems with time-varying delay. The novelty of this paper lies in the fact that both the lock in place and loss of effectiveness faults, unmodeled dynamics, and dynamic disturbances are catered for simultaneously. Furthermore, this is achieved by the adaptation of only one parameter, which simplifies the computation of the control effort, and therefore extends its applicability. In the approach, the Razumikhin lemma and a dynamic signal are employed. It is shown that the output of the system converges to a neighborhood of the reference signal and the semiglobal boundedness of all signals is guaranteed. A simulation example is used to illustrate the validity and efficacy of the approach.

This paper is concerned with the analysis and integrated design of a type of observer-based fault detection (FD) system for general nonlinear processes. To this end, the existence and design condition for this type of nonlinear observer-based FD systems is first introduced. In this context, the integrated design scheme is investigated by dealing with the design condition with the aid of Takagi-Sugeno (T-S) fuzzy dynamic modeling technique. To be specific, the universal T-S fuzzy observer-based residual generator is developed via fuzzy Lyapunov functions. Subsequently, an integrated observer-based FD scheme is proposed with an embedded dynamic threshold, which is generated to meet the real-time FD requirements from industrial processes. In the end, a numerical example and case simulation study on a continuous stirred tank heater process are performed to show the application of the proposed method.

For a class of stochastic nonlinear time-delay systems with multiple constraints-predefined tracking constraint, input saturation, and output dead zone-the output tracking control problem is addressed in this paper. By expressing the saturated actuator as a smooth nonlinear function and employing the Nussbaum function technique, the input and output constraints problems are solved. The tracking performance is achieved under the predefined tracking constraint by utilizing the backstepping recursive design technique and the approximation property of neural networks. Then, based on the utilization of the Lyapunov-Krasovskii functional, the stochastic stability of the closed-loop system is achieved. Finally, the proposed control method is verified through a simulation example.

In this paper, the output feedback control problem is investigated for general nonlinear industrial processes. At the device layer, the nonlinear industrial processes with disturbances are modeled by utilizing Takagi-Sugeno modeling approach, and the corresponding local controllers are then designed to guarantee that the outputs for the local subsystems can track the decomposed setpoints. At the operation layer, considering the effect of radial basis function performance index and packet dropout phenomenon, a setpoint compensator is constructed to dynamically regulate the setpoints and track the given operation index. Finally, a network-based continuous stirred tank reactor system is considered to verify the validity of the proposed strategy in the simulation part.

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

This paper focuses mainly on the approximation-based fuzzy adaptive fault-tolerant control problem for nonlinear systems with unmodeled dynamics and unknown control directions. With the Nussbaum gain technique and a dynamic signal introduced, the difficulties from the unknown control directions and unmodeled dynamics are successfully overcome. Then, by taking advantage of the adaptive fuzzy control method and backstepping technology, we develop a fuzzy adaptive failure compensation control strategy and guarantee the semi-global boundedness for all signals. A simulation example is carried out to demonstrate the validity of the theoretical findings.

This paper is concerned with the design of H∞event-triggered filter for a class of Takagi-Sugeno fuzzy systems. Based on the proposed communication strategy, only the measured outputs of the physical plant that violate a predefined triggering condition will win the right for transmission in the shared communication channel. Considering that the implementation of the filter may not be synchronized with the plant trajectories due to the asynchronous premise variables in network environment, a novel observer-based piecewise fuzzy filter is proposed. By adopting the idea of input delay method, the filtering error dynamics is reformulated as a new event-triggered piecewise fuzzy system. By applying a piecewise Lyapunov-Krasovskii functional and some techniques on matrix convexification, a method of event-triggered H∞piecewise filter design is developed for the filtering error system concerned to be asymptotically stable with a given disturbance attenuation level and reduced transmission rate. Moreover, a co-design algorithm to derive the filter gains and the event triggering parameters is proposed. Illustrative examples are finally given to show the effectiveness of the developed method.

This paper deals with the robust ℋ∞non-fragile controller design for a class of uncertain T-S fuzzy-affine systems. More specifically, a non-fragile piecewise affine static output feedback (SOF) controller with additive gain variations is constructed to ensure the stability and ℋ∞performance of the closed-loop fuzzy-affine dynamic system. Through applying a common/piecewise quadratic Lyapunov functions, different sufficient conditions are derived to solve the robust and non-fragile piecewise affine SOF control problem. Finally, simulation results verify the effectiveness of the proposed approaches.

This paper investigates the problem of exponential stabilization for sampled-data Takagi-Sugeno (T-S) fuzzy control systems with packet dropouts. An input delay approach is adopted to model the sample-and-hold behavior with a time-varying delayed control input, and a switched system approach is proposed to model the data-missing phenomenon. On this basis, the sampled-data T-S fuzzy control system with packet dropouts is modeled as a switched T-S fuzzy system with time-varying delay. The objective is to design a sampled-data fuzzy controller to guarantee the exponential stability of the resulting closed-loop system. Based on a new piecewise time-dependent Lyapunov functional, a novel sufficient condition is derived for the existence of exponentially stabilizing sampled-data fuzzy controllers. All the solutions to the problem are formulated in the form of linear matrix inequalities. Finally, two simulation examples are provided to illustrate the effectiveness of the proposed methods.

In this paper, the performance-based control design problem for double-layer networked industrial processes is investigated. At the device layer, the prescribed performance functions are first given to describe the output tracking performance, and then by using backstepping technique, new adaptive fuzzy controllers are designed to guarantee the tracking performance under the effects of input dead-zone and the constraint of prescribed tracking performance functions. At operation layer, by considering the stochastic disturbance, actual index value, target index value, and index prediction simultaneously, an adaptive inverse optimal controller in discrete-time form is designed to optimize the overall performance and stabilize the overall nonlinear system. Finally, a simulation example of continuous stirred tank reactor system is presented to show the effectiveness of the proposed control method.

The main focus of this paper is on the analysis and integrated design of $\mathcal {L}_2$ observer-based fault detection (FD) systems for discrete-time nonlinear industrial processes. To gain a deeper insight into this FD framework, the existence condition is introduced first. Then, an integrated design of $\mathcal {L}_2$ observer-based FD approach is realized by solving the proposed existence condition with the aid of Takagi-Sugeno fuzzy dynamic modeling technique and piecewise-fuzzy Lyapunov functions. Most importantly, a weighted piecewise-fuzzy observer-based residual generator is proposed, aiming at achieving an optimal integration of residual evaluation and threshold computation into FD systems. The core of this approach is to make use of the knowledge provided by fuzzy models of each local region and then to weight the local residual signal by means of different weighting factors. In comparison with the standard norm-based fuzzy observer-based FD methods, the proposed scheme may lead to a significant improvement of the FD performance. In the end, the effectiveness of the proposed method is verified by a numerical example and a case study on the laboratory setup of continuous stirred tank heater plant.

This paper investigates the problem of output feedback robust ℋ∞control for a class of nonlinear spatially distributed systems described by first-order hyperbolic partial differential equations (PDEs) with Markovian jumping actuator faults. The nonlinear hyperbolic PDE systems are first expressed by Takagi-Sugeno fuzzy models with parameter uncertainties, and then, the objective is to design a reliable distributed fuzzy static output feedback controller guaranteeing the stochastic exponential stability of the resulting closed-loop system with certain ℋ∞disturbance attenuation performance. Based on a Markovian Lyapunov functional combined with some matrix inequality convexification techniques, two approaches are developed for reliable fuzzy static output feedback controller design of the underlying fuzzy PDE systems. It is shown that the controller gains can be obtained by solving a set of finite linear matrix inequalities based on the finite-difference method in space. Finally, two examples are presented to demonstrate the effectiveness of the proposed methods.

This paper investigates the multirate networked industrial process control problem in double-layer architecture. First, the output tracking problem for sampled-data nonlinear plant at device layer with sampling period T(d) is investigated using adaptive neural network (NN) control, and it is shown that the outputs of subsystems at device layer can track the decomposed setpoints. Then, the outputs and inputs of the device layer subsystems are sampled with sampling period T(u) at operation layer to form the index prediction, which is used to predict the overall performance index at lower frequency. Radial basis function NN is utilized as the prediction function due to its approximation ability. Then, considering the dynamics of the overall closed-loop system, nonlinear model predictive control method is proposed to guarantee the system stability and compensate the network-induced delays and packet dropouts. Finally, a continuous stirred tank reactor system is given in the simulation part to demonstrate the effectiveness of the proposed method.

The problem of decentralized piecewise ℋ∞control for discrete-time large-scale nonlinear systems with time-varying delay is studied. Each nonlinear subsystem in the large-scale system is represented by a Takagi-Sugeno (T-S) model, and the time-varying state delay of each subsystem is assumed to be of an interval-like type. We propose a two-term approximation method to transform the closed-loop fuzzy control system into an interconnected formulation, which is subject to two constant time delays in the forward path and norm-bounded uncertainties in the feedback one. By introducing a piecewise Lyapunov-Krasovskii functional (PLKF) and utilizing the scaled small gain (SSG) theorem, the LMI conditions to the decentralized piecewise memory ℋ∞state-feedback controller design for the large-scale fuzzy systems are derived. An example is provided to verify the efficacy of the proposed method.

This paper investigates the adaptive fuzzy backstepping control and H∞performance analysis for a class of nonlinear systems with sampled and delayed measurements. In the control scheme, a fuzzy-estimator (FE) model is used to estimate the states of the controlled plant, while the fuzzy logic systems are used to approximate the unknown nonlinear functions in the nonlinear system. The controller is obtained based on the FE model by combining the backstepping technique with the classic adaptive fuzzy control method. In the stability analysis, all the signals in the closed-loop system are guaranteed to be semiglobally uniformly ultimately bounded (SUUB) and the outputs of the system are proven to converge to a small neighborhood of origin. Furthermore, the H∞performance is investigated and the outputs of the closed-loop system are bounded in the H∞sense. Two examples are given to illustrate the effectiveness of the proposed control scheme.

With the development of hyperspectral remote sensing technology, the spectral resolution of the hyperspectral image data becomes denser, which results in large number of bands, high correlation between neighboring bands, and high data redundancy. It is necessary to reduce these bands before further analysis, such as land cover classification and target detection. Aiming at the classification task, this paper proposes an effective band selection method from the novel perspective of spectral shape similarity analysis with key points extraction and thus retains physical information of hyperspectral remote sensing images. The proposed approach takes all the bands of hyperspectral remote sensing images as time series. Firstly, spectral clustering is utilized to cluster all the training samples, which produces the prototypical spectral curves of each cluster. Then a set of initial candidate bands are obtained based on the extraction of key points from the processed hyperspectral curves, which preserve discriminative information and narrow down the candidate band subset for the following search procedure. Finally, filtering contiguous bands according to conditional mutual information and branch and bound search are further performed sequentially to gain the optimal band combination. To verify the effectiveness of the integrated band selection method put forward in this paper, classification employing the Support Vector Machine (SVM) classifier is performed on the selected spectral bands. The experimental results on two publicly available benchmark data sets demonstrate that the presented approach can select those bands with discriminative information, usually about 10 out of 200 original bands. Compared with previous studies, the newly proposed method is competitive with far fewer bands selected and a lower computational complexity, while the classification accuracy remains comparable.

This paper investigates the problem of robust model predictive control (RMPC) with saturations and packet dropouts. In this model, polytopic uncertainties are adopted to describe the inconsistency arising from the discretization process of sampling, while the occurrence probabilities of packet dropouts are time-varying and saturations are taken into account to describe input and output signals. The problem of exponential RMPC with saturations and packet dropouts is solved and characterized by a convex optimization problem. The developed results of RMPC are then applied to networked flotation processes, which are made up of three layers: direct control layer, set-point control layer, and optimization layer. The RMPC is used for compensating the output information from the optimization layer to the direct control layer such that the desired economic objective can be achieved. Simulations are presented to show the effectiveness of the proposed method.

In this paper, a novel dynamic sliding mode control scheme is proposed for a class of uncertain stochastic nonlinear time-delay systems represented by Takagi-Sugeno fuzzy models. The key advantage of the proposed scheme is that two very restrictive assumptions in most existing sliding mode control approaches for stochastic fuzzy systems have been removed. It is shown that the closed-loop control system trajectories can be driven onto the sliding surface in finite time almost certainly. It is also shown that the stochastic stability of the resulting sliding motion can be guaranteed in terms of linear matrix inequalities; moreover, the sliding-mode controller can be obtained simultaneously. Simulation results illustrating the advantages and effectiveness of the proposed approaches are also provided.

In this paper, a novel dynamic integral sliding-mode control (ISMC) scheme is proposed for a class of uncertain stochastic nonlinear time-delay systems represented by Takagi-Sugeno fuzzy models. The key advantage of the proposed scheme is that two very restrictive assumptions in most existing ISMC approaches for stochastic fuzzy systems have been removed. It is shown that the closed-loop control system trajectories are kept on the integral sliding surface almost surely since the initial time. It is also shown that the stochastic stability of the resulting sliding motion can be guaranteed in terms of linear matrix inequalities, and moreover, the sliding-mode controller can be obtained simultaneously. Simulation results from an inverted pendulum example illustrating the advantages and effectiveness of the proposed approaches are provided in the end.

This paper addresses the sliding-mode control (SMC) design problem for a class of uncertain nonlinear systems that can be represented by Takagi-Sugeno (T-S) fuzzy models. We propose a novel dynamic sliding-mode control scheme for T-S fuzzy models, aiming to eliminate the restrictive assumption that all subsystems share a common input matrix, which is required in most existing fuzzy SMC approaches. Sufficient conditions for the reachability of the sliding surface and asymptotic stability of the sliding motion are formulated in the form of linear matrix inequalities. Finally, simulation results that illustrate the advantages and effectiveness of the proposed approaches are provided.

This paper addresses the universal fuzzy integral sliding-mode controllers' problem for continuous-time multi-input multi-output nonlinear systems based on Takagi-Sugeno (T-S) fuzzy models. By using the approximation capability of T-S fuzzy models, the nonlinear systems are expressed by uncertain T-S fuzzy models with norm-bounded approximation errors. A novel fuzzy dynamic integral sliding-mode control (DISMC) scheme is then developed for the nonlinear systems based on their T-S fuzzy approximation models. One of the key features of the new DISMC scheme is that the restrictive assumption that all local linear systems share a common input matrix, which is required in most existing fuzzy integral sliding-mode control (ISMC) approaches, is removed. Furthermore, the results of universal fuzzy ISMCs for two classes of nonlinear systems, along with constructive procedures to obtain the universal fuzzy ISMCs, are provided, respectively. Finally, the advantages and effectiveness of the proposed approaches are illustrated via a numerical example.

This paper investigates the problem of robust H(∞) state estimation for a class of continuous-time nonlinear systems via Takagi-Sugeno (T-S) fuzzy affine dynamic models. Attention is focused on the analysis and design of an admissible full-order filter such that the resulting filtering error system is asymptotically stable with a guaranteed H(∞) disturbance attenuation level. It is assumed that the plant premise variables, which are often the state variables or their functions, are not measurable so that the filter implementation with state-space partition may not be synchronous with the state trajectories of the plant. Based on piecewise quadratic Lyapunov functions combined with S-procedure and some matrix inequality linearization techniques, some new results are presented for the filtering design of the underlying continuous-time T-S fuzzy affine systems. Illustrative examples are given to validate the effectiveness and application of the proposed design approaches.

This paper investigates the universal fuzzy model and universal fuzzy controller problems for stochastic nonaffine nonlinear systems. The underlying mechanism of stochastic fuzzy logic is first discussed, and a stochastic generalized fuzzy model with new stochastic fuzzy rule base is then given. Based on their function approximation capability, these kinds of stochastic generalized fuzzy models are shown to be universal fuzzy models for stochastic nonaffine nonlinear systems under some sufficient conditions. An approach to stabilization controller design for stochastic nonaffine nonlinear systems is then developed through their stochastic generalized Takagi-Sugeno (T-S) fuzzy approximation models. Then, the results of universal fuzzy controllers for two classes of stochastic nonlinear systems, along with constructive procedures to obtain the universal fuzzy controllers, are also provided, respectively. Finally, a numerical example is presented to illustrate the effectiveness of the proposed approach.

This paper investigates the problem of robust H∞output feedback control for a class of continuous-time Takagi-Sugeno (T-S) fuzzy affine dynamic systems with parametric uncertainties and input constraints. The objective is to design a suitable constrained piecewise affine static output feedback controller, guaranteeing the asymptotic stability of the resulting closed-loop fuzzy control system with a prescribed H∞disturbance attenuation level. Based on a smooth piecewise quadratic Lyapunov function combined with S-procedure and some matrix inequality convexification techniques, some new results are developed for static output feedback controller synthesis of the underlying continuous-time T-S fuzzy affine systems. It is shown that the controller gains can be obtained by solving a set of linear matrix inequalities (LMIs). Finally, three examples are provided to illustrate the effectiveness of the proposed methods.

Complex industrial processes are controlled by the local regulation controllers at the field level, and the setpoints for the regulation are usually made by manual decomposition of the overall economic objective according to the operators' experience. If a precise static process model can be built, real-time optimization (RTO) can be used to generate the setpoints. Nevertheless, since the aforementioned control structure is actually open-loop, the desired economic objective of the whole processes may not be tracked when disturbances exist. Aiming at solving this problem, a novel network based model predictive control method (MPC) for setpoints compensation is proposed in this paper. Firstly, a multivariable proportional integral (PI) controller is designed to perform the local regulation control. Secondly, a stochastic packet dropout model is adopted to characterize the measurement and human-in-the-loop delay effect. Then, a model predictive controller considering the random dropout effect is developed to compensate the setpoints dynamically according to the changing conditions of the processes, such that the prescribed performance objective can be obtained. Finally, a flotation process model is employed to demonstrate the effectiveness of the proposed method.

This paper studies the problem of robust H∞output feedback control for a class of continuous-time Takagi-Sugeno (T-S) fuzzy affine dynamic systems with parametric uncertainties and input constraints. Based on a smooth piecewise quadratic Lyapunov function combined with S-procedure and some matrix inequality convexification techniques, some new results are developed for static output feedback H∞controller synthesis of the underlying continuous-time T-S fuzzy affine systems. It is shown that the controller gains can be obtained by solving a set of linear matrix inequalities. Finally, a simulation example is provided to illustrate the application of the proposed methods.

This paper is concerned with designing a fuzzy robust H∞filter for a class of continuous-time nonlinear systems via Takagi-Sugeno (T-S) fuzzy affine dynamic models based on piecewise Lyapunov functions. Attention is focused on the analysis and design of an admissible full-order filter such that the filtering error dynamics is stochastically stable and a prescribed H∞attenuation level is guaranteed. It is assumed that the plant premise variables are not measurable so that the filter implementation with state space partition may not be synchronous with the state trajectories of the plant. Based on piecewise quadratic Lyapunov functions (PQLFs) combined with S-procedure and some matrix inequality linearization techniques, some new results are established for filtering design of the underling continuous-time T-S fuzzy affine systems. An examples is given to illustrate the effectiveness and applicability of the proposed design methods.

This paper is concerned with the problem of robust${\mathscr H}_{\infty }$output feedback control for a class of continuous-time Takagi–Sugeno (T–S) fuzzy affine dynamic systems using quantized measurements. The objective is to design a suitable observer-based dynamic output feedback controller that guarantees the global stability of the resulting closed-loop fuzzy system with a prescribed${\mathscr H}_{\infty }$disturbance attenuation level. Based on common/piecewise quadratic Lyapunov functions combined with S-procedure and some matrix inequality convexification techniques, some new results are developed to the controller synthesis for the underlying continuous-time T–S fuzzy affine systems with unmeasurable premise variables. All the solutions to the problem are formulated in the form of linear matrix inequalities (LMIs). Finally, two simulation examples are provided to illustrate the advantages of the proposed approaches.

This paper presents a novel approach to control general nonlinear systems based on Takagi-Sugeno (T-S) fuzzy dynamic models. It is first shown that a general nonlinear system can be approximated by a generalized T-S fuzzy model to any degree of accuracy on any compact set. It is then shown that the stabilization problem of the general nonlinear system can be solved as a robust stabilization problem of the developed T-S fuzzy system with the approximation errors as the uncertainty term. Based on a piecewise quadratic Lyapunov function, the robust semiglobal stabilization and H∞ control of the general nonlinear system are formulated in the form of linear matrix inequalities. Simulation results are provided to illustrate the effectiveness of the proposed approaches.

In this paper, the problem of generalized H2filtering is concerned for a class of discrete-time T-S fuzzy systems with measurement quantization and packet loss. The quantized measurements are transmitted to the filter via an imperfect communication channel, where the phenomenon of packet loss can be encountered. A random binary process is utilized to describe the packet dropouts, while the quantization errors are treated as sector bound uncertainties. Attention is focused on the design of generalized H2piecewise filter such that the filtering error system is stochastically stable and preserves a guaranteed generalized H2performance. The developed filter gains can be obtained by solving a set of linear matrix inequalities. Finally, an illustrative example is provided to show the effectiveness of the proposed method.

This paper investigates the problem of robustH∞state estimation for a class of multichannel networked nonlinear systems with multiple packet dropouts. The nonlinear plant is represented by Takagi-Sugeno (T-S) fuzzy-affine dynamic models with norm-bounded uncertainties, and stochastic variables with general probability distributions are adopted to characterize the data missing phenomenon in output channels. The objective is to design an admissible state estimator guaranteeing the stochastic stability of the resulting estimation-error system with a prescribedH∞disturbance attenuation level. It is assumed that the plant premise variables, which are often the state variables or their functions, are not measurable so that the estimator implementation with state-space partition may not be synchronized with the state trajectories of the plant. Based on a piecewise-quadratic Lyapunov function combined with S -procedure and some matrix-inequality-convexifying techniques, two different approaches are developed to robust filtering design for the underlying T-S fuzzy-affine systems with unreliable communication links. All the solutions to the problem are formulated in the form of linear-matrix inequalities (LMIs). Finally, simulation examples are provided to illustrate the effectiveness of the proposed approaches.

This paper investigates the problem of robust $\hbox{\scr{H}}_{\infty }$ output-feedback control for a class of networked nonlinear systems with multiple packet dropouts. The nonlinear plant is represented by Takagi–Sugeno (T–S) fuzzy affine dynamic models with norm-bounded uncertainties, and stochastic variables that satisfy the Bernoulli random binary distribution are adopted to characterize the data-missing phenomenon. The objective is to design an admissible output-feedback controller that guarantees the stochastic stability of the resulting closed-loop system with a prescribed $\hbox{\scr{H}}_{\infty }$ disturbance attenuation level. It is assumed that the plant premise variables, which are often the state variables or their functions, are not measurable so that the controller implementation with state-space partition may not be synchronous with the state trajectories of the plant. Based on a piecewise quadratic Lyapunov function combined with an S-procedure and some matrix inequality convexifying techniques, two different approaches to robust output-feedback controller design are developed for the underlying T–S fuzzy affine systems with unreliable communication links. The solutions to the problem are formulated in the form of linear matrix inequalities (LMIs). Finally, simulation examples are provided to illustrate the effectiveness of the proposed approaches.

This paper is concerned with the problem of H∞model approximation for discrete-time Takagi-Sugeno (T-S) fuzzy time-delay systems. For a given stable T- S fuzzy system, our attention is focused on the construction of a reduced-order model, which not only approximates the original system well in an H∞performance but is also translated into a linear lower dimensional system. By applying the delay partitioning approach, a delay-dependent sufficient condition is proposed for the asymptotic stability with an H∞error performance for the error system. Then, the H∞model approximation problem is solved by using the projection approach, which casts the model approximation into a sequential minimization problem subject to linear matrix inequality (LMI) constraints by employing the cone complementary linearization algorithm. Moreover, by further extending the results, H∞model approximation with special structures is obtained, i.e., delay-free model and zero-order model. Finally, two numerical examples are provided to illustrate the effectiveness of the proposed methods.

This paper investigates the problem of H∞filtering for a class of nonlinear discrete-time systems with measurement quantization and packet dropouts. Each output is transmitted via an independent communication channel, and the phenomenon of packet dropouts in transmission is governed by an individual random binary distribution, while the quantization errors are treated as sector-bound uncertainties. Based on a piecewise-Lyapunov function, an approach to the design of H∞-piecewise filter is pro posed such that the filtering-error system is stochastically stable with a guaranteed H∞performance. Some slack matrices are introduced to facilitate the filter design procedure by eliminating the coupling between the Lyapunov matrices and the system matrices. The filter parameters can be obtained by solving a set of linear matrix inequalities (LMIs), which are numerically tractable with commercially available software. Finally, two illustrative examples are provided to show the effectiveness of the proposed method.

This paper investigates the problem of robust H∞output-feedback control for a class of nonlinear systems under unreliable communication links. The nonlinear plant is represented by a Takagi-Sugeno (T-S) uncertain fuzzy model, and the communication links between the plant and controller are assumed to be imperfect, i.e., data-packet dropouts occur intermittently, which is often the case in a network environment. Stochastic variables that satisfy the Bernoulli random-binary distribution are adopted to characterize the data-missing phenomenon, and the attention is focused on the design of a piecewise static-output-feedback (SOF) controller such that the closed-loop system is stochastically stable with a guaranteed H∞performance. Based on a piecewise Lyapunov function combined with some novel convexifying techniques, the solutions to the problem are formulated in the form of linear matrix inequalities (LMIs). Finally, simulation examples are also provided to illustrate the effectiveness of the proposed approaches.

This paper investigates the problem of delay-dependent robustHinfinfiltering design for a class of uncertain discrete-time state-delayed Takagi-Sugeno (T-S) fuzzy systems. The state delay is assumed to be time-varying and of an interval-like type, which means that both the lower and upper bounds of the time-varying delay are available. The parameter uncertainties are assumed to have a structured linear fractional form. Based on a novel fuzzy-basis-dependent Lyapunov-Krasovskii functional combined with Finsler's lemma and an improved free-weighting matrix technique for delay-dependent criteria, a new sufficient condition for robustHinfinperformance analysis is first derived, and then, the filter synthesis is developed. It is shown that by using a simple linearization technique incorporating a bounding inequality, a unified framework can be developed such that both the full-order and reduced-order filters can be obtained by solving a set of linear matrix inequalities (LMIs), which are numerically efficient with commercially available software. Finally, simulation examples are provided to illustrate the advantages and less conservatism of the proposed approach.

This paper investigates the problem of delay-dependent robust H℞filtering design for a class of uncertain discrete-time state-delayed T-S fuzzy systems. The state delay is assumed to be time-varying and of an interval-like type, which means that both the lower and upper bounds of the time-varying delay are available. The parameter uncertainties are assumed to have a structured linear fractional form. Based on a novel delay and fuzzy-basis-dependent Lyapunov-Krasovskii functional combined with Finsler’s Lemma, a new sufficient condition for robust H℞performance analysis is firstly derived and then the filter synthesis is developed. It is shown that by using a new linearization technique incorporating a bounding inequality, a unified framework can be developed such that both the full-order and reduced-order filters can be obtained by solving a set of linear matrix inequalities, which are numerically efficient with commercially available software. Finally, a numerical example is provided to illustrate the advantages and less conservatism of the proposed approach.

This paper revisits the problem of delay-dependent robust Hinfinfiltering design for a class of continuous-time polytopic linear systems with a time-varying state delay. Based on a delay and parameter-dependent Lyapunov-Krasovskii functional combined with Projection Lemma, a new sufficient condition for robust Hinfinperformance analysis is firstly derived and then the filter synthesis is developed by using a novel matrix linearization technique. It is shown that the desired filters can be constructed by solving a set of linear matrix inequalities. Finally, a numerical example is given to show the effectiveness and less conservatism of the proposed method in comparison with the existing approaches.

A hybrid mechanism is a configuration that combines the motions of two characteristically different electric motors by means of a mechanism to produce programmable output. In this paper, by means of establishing mathematic model for the influence of output accuracy of a planar hybrid five-bar mechanism affected by manufacturing error of link length, the sensitive coefficient of manufacturing error of link-length was determined and the law of influence on output accuracy of hybrid mechanism affected by manufacturing error of link length was analyzed. Living example of design shows that reasonable designed manufacturing tolerance of link length of hybrid five-bar mechanism could make the mechanism not only satisfy the requirement of accuracy but also achieve lower manufacturing cost.