This article investigates the leader-following cluster consensus for generic linear heterogeneous multiagent systems (MASs). Unlike the existing research, a novel event-triggered (ET) control mechanism is designed and developed on the transmission side of the agents, over directed communication topologies, to reduce communication load. For this purpose, a variable threshold function as the fully distributed ET condition (ETC) is suggested, which provides a smooth transition and considers both maximum and minimum threshold levels for triggering. A relative-state feedback-based cluster consensus control protocol is designed by considering the cooperative and competitive interaction behavior of agents. Then, the convergence analysis is performed by utilizing the Lyapunov method. This work is then further extended for the ET observer-based output feedback cluster consensus problem. The proposed ETC naturally eliminates the Zeno behavior for each agent. In contrast to existing methods, a variable threshold-based ET scheme, a cooperation-competition network, and an elimination of Zeno behavior for both state-based and output-based methods have been considered for the leader-following cluster consensus. Finally, illustrative examples are used to validate the theoretical results.

Intelligent Automation Systems, such as automated storage and retrieval systems, self-driving vehicles, various types of autonomous robots, and autonomous workshop plants, act independently of direct human supervision. Their impact on society and human life will be significant. These automation systems are safety-critical, complex, and powerful with a higher-level functionality. Safety and reliability to perform their tasks safely and minimize failures is one of the key challenges and becomes costly and difficult to achieve. The development of novel safety and reliability technologies dealing with theoretical aspects and for intelligent automation systems has become a hot spot in recent years. With the novel safety and reliability technologies, the intelligent automation systems can detect system failures, identify operation risks, predict unknown safety hazards and vulnerabilities, and avoid situations that pose risks to humans, property, or the automation systems themselves. Recent developments confirm that there are still areas of research to be explored within the safety-critical approaches. This Special Issue on Emerging Trends in Safety-critical Issues for Intelligent Automation Systems of IEEE Transactions on Automation Science and Engineering (TASE) aims to present recent advances in theories, methods, and applications that address safety-critical challenges in autonomous intelligent systems. The objective is to compile state-of-the-art research that contributes to the development of resilient and trustworthy automation solutions in safety-sensitive scenarios. After a thorough and rigorous peer-review process, 27 high-quality articles were selected from numerous submissions worldwide. These articles closely align with the Special Issue’s scope and can be categorized into seven key topics. - Risk assessment and model-based safety and cybersecurity analysis [A1], [A2], [A3], [A4], [A5]. - Intelligent fault detection and fault-tolerant control [A6], [A7], [A8], [A9], [A10]. - Reliability and traceability of decision-making for intelligent automation systems [A11], [A12], [A13]. - Conflict detection and resolution in intelligent automation systems [A14], [A15]. - Safety- and security-related issues including functional safety and system security [A16], [A17], [A18]. - Design, development, validation, and applications of intelligent automation systems such as UAVs, UGVs, and UUVs [A19], [A20], [A21], [A22], [A23]. - Human-robot collaboration, risk assessment of intelligent automation [A24], [A25], [A26], [A27]. We believe this collection will provide a valuable reference for academia and industry alike, and inspire further research in this rapidly evolving field.

This paper presents an online transfer heuristic dynamic programming (THDP) control approach for a class of nonlinear discrete systems. The proposed approach integrates transfer learning with adaptive critic control. To design a robust optimal control strategy for the nonlinear discrete systems, we utilize sample data collected from a source task to acquire prior knowledge. This prior knowledge is subsequently used to guide the online control process of nonlinear systems of target tasks. To avoid negative transfer effects and conserve computational resources, we introduce a novel attenuation function with a truncation mechanism. Additionally, we develop a disturbance compensation control mechanism to address uncertainties. Furthermore, we demonstrate that the properties of the uncertain nonlinear systems under robust optimal control, as well as the weight error of neural networks, are ultimately uniformly bounded given certain conditions. Finally, two simulations are conducted to verify the performance of the proposed algorithm. Note to Practitioners—Adaptive dynamic programming (ADP) is one of the main methods to solve the Hamilton-Jacobi-Bellman (HJB) equation. However, when using neural network approximation, it often requires a long time of iteration and a large amount of computational process, wasting a lot of computational resources. For this reason, we propose an ADP control scheme with enhanced detection speed: that is, by learning a class of similar tasks to obtain prior knowledge to assist in the online control of our actual system. At the same time, this paper considers system disturbances, which means that they are more universal and robust. After simulation experiments, it has been proven that this scheme has good performance.

This article investigates the cybersecurity problem of active suspension systems (ASSs) subject to random denial-of-service (DoS) attacks. Consider the nonlinearity and uncertainty of ASSs, the Takagi–Sugeno (T-S) fuzzy theory is applied to address these issues. In order to model various potential operating behaviors of the system, a high-order multiswitching-mode (HOMSM) T-S fuzzy control scheme is constructed, in which a series of free-weighted matrix sets are developed for different switching modes, such that the conservatism of controller design is reduced to a certain extent. By designing the HOMSM control method, a certain level of resistance to randomly activated DoS attacks can be achieved. With the help of homogeneous polynomial technology, several time-varying balance matrices are constructed for extracting the properties of different switching modes. Then, the exponential stability conditions of ASSs under DoS attacks can be derived, and the$H_{\infty }$performance criterion is guaranteed. Finally, the theoretical results are validated by the hardware-in-the-loop experiments.

This paper develops a bilayer predefined time fuzzy reinforcement learning (PT-FRL) control strategy to improve the efficiency of autonomous vehicle formation execution and reduce energy consumption. First, the fixed constraints of communication connectivity and collision avoidance are reconstructed into performance constraints, and normalized error mapping techniques are used to transform them into a new unconstrained error system. Then, based on the system, a cost function was constructed that balances cost control and performance. The control strategy adopts a bilayer architecture: In the first layer, a feedforward controller is designed to provide a more concise control object for subsequent PT-FRL controllers by compensating for known nonlinear coupling terms in advance, and can significantly reduce fuzzy logic systems computational load. and in the second layer, a PT optimal formation controller is designed using FRL to ensure that the autonomous vehicles complete the formation task within the predefined time. Finally, the effectiveness of the proposed method was verified through simulation and experiments.

It is quite desirable yet challenging to plan specified motion behaviors from cooperation to opposition in terms of practical human–robot interaction tasks if the settling time, symmetric/asymmetric constraint, limited resolution, and less bandwidth occupation are involved. Based on this fact, this work is devoted to the assimilation control of human–robot interaction, which can flexibly reshape the physical trajectory in practical interaction work. Then, the adaptive parameter estimation terms are designed to address the effect of limited resolution and unknown robotic dynamics. In particular, a novel easy-to-implement self-triggered quantized mechanism is developed, which can better balance the relationship between system performance and resource utilization. Meanwhile, benefiting from the piecewise exponential function and bias state transformation, the practically prescribed-time stability of the controlled robotic dynamics can be guaranteed. The prominent feature of this design lies in that the settling time and convergence precision can be decoupled into separately user-preassigned parameters, and the symmetric/asymmetric output constraints can be implemented in a unified framework. Afterward, experiment results on a robot system verify the benefits and efficacy of the resultant scheme.

This study addresses the sliding mode control (SMC) for Takagi-Sugeno (T-S) fuzzy semi-Markov switching systems (FSMSSs) characterized by multizone probabilistic time-varying delays. The dynamic behaviors of FSMSSs are captured through a comprehensive semi-Markov process framework that accommodates arbitrary switching scenarios. In addition, a tailored SMC law that ensures the system’s trajectory reaches and maintains a preset sliding surface over a specified finite time is applied. To address the challenges of communication load, a novel multizone probabilistic dynamic event-triggered protocol is introduced, leveraging the time-varying nature of transmission delays and incorporating two adjustable internal dynamic variables. The establishment of sufficient conditions for ensuring the stochastic finite-time stability of the closed-loop system is achieved through an effective Lyapunov functional methodology. Finally, the validity and superiority of the proposed methodologies is demonstrated by a mass-spring-damper mechanical system.

This paper studies the disturbance-observer-based adaptive neural network cooperative event-triggered control problem for six-rotor unmanned aerial vehicles with finite-time prescribed performance. Six-rotor unmanned aerial vehicle systems are divided into the position subsystem and the attitude subsystem. The switching threshold event-triggered mechanism that considers the influence of event triggering on the consensus control performance is designed of each six-rotor unmanned aerial vehicle to save the transmission resources, which excludes the Zeno behavior. Neural networks are introduced for estimating the uncertainties and solving the algebraic loop problem. In addition, for the position subsystem, by combining the prescribed performance with the velocity function, a finite-time control scheme is proposed, which can guarantee that the consensus errors converge to a prespecified neighborhood of the origin, and all closed-loop signals are bounded. Based on the designed control input signal of position subsystem, an adaptive neural network event-triggered control mechanism is designed to stabilize the attitude subsystem. Finally, some verification results are given to test the rationality of the proposed control strategy. Note to Practitioners—The purpose of this paper is to design an event-triggered cooperative control scheme to save the transmission resources for six-rotor unmanned aerial vehicle systems with performance limitations and lumped disturbances. In practical applications, if the impact of the event-triggered mechanism on the system performance is not taken into account, the system performance may be significantly degraded while the information transmission resource is saved. Thus, this paper designs an event-triggered mechanism that considers the system performance, which can effectively weigh the relationship between system performance and transmission resource consumption and better meet the practical requirements. Moreover, a finite-time specified performance control strategy is introduced to avoid the problem of difficult determination of residual set and improve the transient-state and steady-state performances of unmanned aerial vehicle systems, which is more in line with the actual demand.

This article studies the problem of memory event-triggered cooperative adaptive control of heterogeneous nonlinear multiagent systems (MASs) under denial-of-service (DoS) attacks based on the multiplayer mixed zero-sum (ZS) game strategy. First, a neural-network-based reinforcement learning scheme is structured to obtain the Nash equilibrium solution of the proposed multiplayer mixed ZS game scheme. Then, a memory-based event-triggered mechanism considering the historical data is proposed. This effectively avoids incorrect triggering information caused by unknown external factors. Moreover, thanks to the idea of switching topology, the mixed ZS game problem under the influence of node-based DoS attacks is solved efficiently. In accordance with the Lyapunov stability theory, it is proved that all signals of heterogeneous MASs are bounded, all heterogeneous followers can track the trajectory of the leader during the no-attack period, the attacked follower can achieve stabilization control during the attack period, and the remaining nonattacked followers can achieve cooperative control during the attack period. Finally, the effectiveness of the designed memory-event-triggered-based mixed ZS game cooperative control strategy is tested by the given simulation results.

The subject of this study is fuzzy predefined-time control for a class of switched nonlinear systems with multiple faults. In comparison to existing research on predefined-time control, this study delves into the realm of switched nonlinear systems, encompassing switched linear sensor faults and switched nonaffine faults. The difficulty in the controller design lies in following the backstepping technique, as taking the derivative of fractional power virtual control laws would trigger singularity issues at equilibrium states or coordinate transformation origins. The study utilizes the unique characteristics of switching and fuzzy logic systems to introduce a continuous piecewise predefined-time controller with a fault-tolerant compensation mechanism to avoid singularity problems. By adjusting a predefined parameter in the developed controller, the system could achieve the objectives of adaptive stability and adaptive tracking within a predefined time, as desired by the user. Moreover, the application of the proposed algorithm to practical systems is presented.

This paper is concerned with the attack detection problem of T–S fuzzy systems with unknown but bounded (UBB) noise subject to malicious attacks via the set-membership estimation and a switching multi-mode high-order free-weighting matrix (SMHFM) method. Initially, the SMHFM is developed to address the so-called conservatism problem caused by the one-order free-weighting matrix (OFM) method. To solve this problem, the homogeneous polynomial technique is employed to introduce groups of free-weighting matrices for different switching modes. As a result, the SMHFM is synchronized with the working modes and possesses a high-order feature. Additionally, a switching multi-mode mechanism is implemented to enable the SMHFM to exhibit multiple modes. Time-variant balanced matrices are introduced for different switching modes to adjust the relevant matrix terms. This adjustment allows for obtaining more relaxed conditions, leading to smaller constraint sets and higher accuracy in state estimation. Furthermore, a zonotope-based set-membership (ZS) attack detection algorithm is introduced for T-S fuzzy systems, which is capable of detecting various types of attacks. By utilizing the proposed SMHFM method for attack detection, the level of conservatism in state estimation can be reduced. Finally, two simulation examples are given and some comparisons are made to validate the effectiveness of the proposed methods.

This article addresses the distributed zero-sum differential game adaptive control problem of six-rotor unmanned aerial vehicles under denial-of-service attacks based on the self-triggered mechanism. First, a fuzzy-logic-system-based identifier-critic framework is structured to obtain the alternative approximate solution with fewer learning parameters. Then, a novel optimal value function, considering the optimal control signal, worst disturbance signal, steady-state, and dynamic performances, is designed. Subsequently, an improved self-triggered strategy, featuring negative feedback adjustment between the threshold and system consensus error, is proposed to reduce the communication resource loss and decrease the influence of introducing the self-triggered mechanism on the system performance. Unlike the event-triggered strategy, the next trigger moment of the self-triggered strategy is determined by the current information, eliminating the need for continuous monitoring of the trigger state, which is more convenient for the physical implementation. Moreover, the connectivity-broken denial-of-service attacks on the information transmission process among unmanned aerial vehicles are considered. Next, through the transformation between stabilization control and cooperative control, the difficulty of realizing cooperative control caused by the temporarily disrupted topological relationships due to denial-of-service attacks is solved effectively. Using Lyapunov stability theory, it is proved that all signals of six-rotor unmanned aerial vehicles are bounded, and the consensus control performance is achieved. Finally, the rationality of the designed zero-sum differential game adaptive control scheme is verified by some simulation results.

This article investigates the optimal tracking problem for discrete-time autonomous nonlinear switched systems with the switching cost. To avoid excessive switching frequency, the switching cost between modes is considered in the performance index, which means that the optimal switching policy is not only related to the tracking error but also the mode applied at the previous instant. The objective is to make the system state track the reference signal while minimizing the defined performance function. A model-free Q-learning algorithm that learns the optimal switching policy from real system data is developed. Furthermore, it is proved by mathematical induction that the iterative Q-functions generated by the proposed Q-learning algorithm will converge to the optimum. To implement the Q-learning algorithm, fuzzy logic systems (FLSs) are applied to approximate the iterative Q-functions. A novel structure of FLSs is designed to ensure the validity of Q-function approximation. Finally, simulation results demonstrate the effectiveness and advantages of the algorithm.

A collision-free formation control problem for multiple unmanned aerial vehicles (UAVs) with directed switching topologies and disturbances is investigated. A novel distributed control algorithm that uses UAVs local directed switching information is proposed for achieving the required flight formation and ensuring a safe distance between UAVs; the algorithm involves the incorporation of the APF method in the virtual leader formation scheme. Two command signals generated by the virtual position controller are transmitted to the attitude subsystem. For each UAV, an adaptive composite controller is designed by combining a fuzzy system and fast terminal sliding mode control technique to guarantee that tracking errors converge to a stable area around zero. Finally, the feasibility of the proposed composite control algorithm is demonstrated through a simulation.

The prescribed-time optimal control problem for nonlinear systems is investigated in this article. First, a transformation function is constructed, which includes the system state and a strictly decreasing auxiliary function related to the prescribed time and accuracy. Second, the control input and the transformation function are incorporated into a new performance index function. This encodes the prescribed-time control into the optimal control problem. Subsequently, a new Hamilton–Jacobi–Bellman (HJB) equation related to the prescribed time and accuracy is derived. To find a solution to the HJB equation, a fuzzy reinforcement learning algorithm is proposed. This algorithm successfully approximates the optimal cost and control policy while ensuring the system stability. Additionally, the system state can converge to a preassigned residual set within a prescribed time. Finally, an example of an electromechanical system is used to illustrate the efficacy of the suggested algorithm.

This article investigates the Nash equilibrium seeking issue in$N$-player nonzero-sum (NZS) games. First, prescribed-time control is a priori encoded into the framework of$N$-player NZS games by defining a cost function that considers the interactions between multiple players and prescribed-time performance requirements. To tackle the complex challenge of solving the coupled Hamilton–Jacobi (HJ) equation, a fuzzy adaptive learning algorithm within the prescribed-time frame is proposed. An identifier is constructed to address the lack of prior knowledge about the system's nonlinear dynamics. Critic and actor-tuning laws are designed to approximate optimal value functions and Nash equilibrium strategies. The proposed algorithm achieves Nash equilibrium and ensures the system state converges to a prescribed range within a specified time. Finally, the feasibility of the proposed algorithm is substantiated through a simulation example involving three-player games.

This article presents a discussion on the adaptive fuzzy fully distributed consensus problem of heterogeneous switched nonlinear multiagent systems (SNMASs). The considered agents contain both first- and second-order dynamics. Fuzzy logic systems are introduced to approximate the unknown nonlinear terms of the SNMASs. Influenced by the systems' own factors or the external environment, the subsystems of agents may be unstabilizable and existing control strategies may not be able to ensure the stability of the systems. Therefore, it is necessary to provide a new consensus protocol to address this problem. In this article, a fully distributed consensus protocol is provided that includes an auxiliary system and a series of state-dependent switching laws. To ensure the stability of whole SNMASs and realize the consensus objective, convex combination technology and the single Lyapunov function method are adopted. Finally, the effectiveness of the proposed scheme is verified through simulation results.

This article investigates the singularity-free finite-time adaptive optimal control problem for coordinated robots, where the position and velocity are constrained within the asymmetric yet time-varying ranges. Different from the existing results concerning constrained control, the imposed feasibility conditions are relaxed by skillfully integrating a nonlinear state-dependent function into the backstepping design procedure. Therein, the typical feature of the designed finite-time controller lies in the application of the modified smooth switching function, rendering the designed controller powerful enough to eliminate singularity problem. Notably, with the aid of the constructed optimal cost function and neural network-based critic architecture, the optimal control law is established under the backstepping design framework. It is theoretically verified that the designed controller is of satisfied optimization and finite-time tracking ability, and desired constrained objective in the meanwhile. The validity of the resulting control algorithm is eventually substantiated via two robotic manipulators.

This article develops a fixed-time${H}_\infty$consensus control protocol for heterogeneous uncertain vehicular platooning to mitigate the adverse effects of disturbance, transmission delay when exchanging information between agents, and input delay from vehicles’ controllers to their actuators. To deal with this problem, a control scheme with a fixed-time observer is proposed by considering the inter-vehicle spacing policy. A fixed-time unknown observer is embedded to reconstruct the composite unknown terms in a fixed-time. Based on the Lyapunov concept, a distributed sliding mode control law, which is independent of the type of topologies, is introduced to ensure the fulfillment of platoon control objectives in a fixed-time interval regardless of the initial states. Simulations verify the main results.

This study is concerned with protocol-based asynchronous filtering for interval type-2 (IT2) fuzzy systems subject to time-varying saturation functions. Two mutually independent Markov processes are proposed to characterize the random manners of intermittently failure and transmission sequence of sensor nodes, and a new joint Markov process is formulated by adopting the merging technique. Aiming at curbing the data collision and improving network utilization in the restricted network, an ETRP is implemented to govern whether to orchestrate the packets and which one to be launched simultaneously. A novel asynchronous filter is formulated under the constraint of saturation with the hope to improve filtering performance. This construction involves the dynamic adaptation of the saturation level in tandem with the estimation error. Additionally, the mismatched modes between the newly joined Markov process and filter are characterized by a hidden Markov model. Eventually, two examples are applied to verify the availability of the theoretical results.

This article develops a resilient-learning control strategy for a kind of cyber-physical system to mitigate the influence of a mixed-type of network attacks. Such an attack is composed of a false-data-injection attack and a replay attack, which can be represented comprehensively by using Markov jump signals. Note that the involved attacks are assumed to be uncertain, which requires a three-layer neural network to learn them. Based on attack approximations as the output from the neural network, a resilient and efficient controller is designed to defend against the mixed-type of network attacks, in which several adaptive laws are proposed to estimate the involved neural network weights. Under the designed controller, the ultimate boundness and asymptotical stability are discussed. Finally, a practical vertical taking-off and landing helicopter model is proposed to verify the developed controller.

In this article, a novel integral reinforcement learning (RL)-based nonfragile output feedback tracking control algorithm is proposed for uncertain Markov jump nonlinear systems presented by the Takagi–Sugeno fuzzy model. The problem of nonfragile control is converted into solving the zero-sum games, where the control input and uncertain disturbance input can be regarded as two rival players. Based on the RL architecture, an offline parallel output feedback tracking learning algorithm is first designed to solve fuzzy stochastic coupled algebraic Riccati equations for Markov jump fuzzy systems. Furthermore, to overcome the requirement of a precise system information and transition probability, an online parallel integral RL-based algorithm is designed. Besides, the tracking object is achieved and the stochastically asymptotic stability, and expected$\mathcal {H}_{\infty }$performance for considered systems is ensured via the Lyapunov stability theory and stochastic analysis method. Furthermore, the effectiveness of the proposed control algorithm is verified by a robot arm system.

In this article, the group consensus problem is addressed for a network of multiagent systems (MASs). Unlike in existing literature, where a relative-state feedback-based distributed control input is used to achieve group consensus, this work aims at designing a relative-output-based distributed control law to achieve the same goal. To that effect, the Lyapunov stability theory is used to formulate the sufficient and necessary conditions for the existence of such a feedback controller and then separate conditions have been included for its design. In addition to that, a new linear matrix inequality is explored to choose the intracluster coupling strengths to ensure group consensus. In this article, the relative-output-based control approach is investigated for both the leaderless and the leader-following frameworks of the group consensus problem, and the theoretical findings presented are validated using numerical examples and simulation results.

The finite-time event-triggered stabilization is studied for a class of discrete-time nonlinear Markov jump singularly perturbed models with partially unknown transition probabilities (TPs). T-S fuzzy strategy is adopted to characterize the related nonlinear Markov jump singularly perturbed models. The control objective is to make sure that the system states remain within a bounded domain during a fixed-time interval. First, a mode-dependent event-triggered scheme is constructed to reduce the communication burden and save the network bandwidth. On that basis, by using a new Lyapunov function, a developed finite-time stability criterion is derived for the corresponding system to avoid an ill-conditioned issue due to a small singular perturbation parameter. Moreover, the mode-dependent fuzzy controller gain and the event-triggered parameter are co-designed under the framework of partially unknown TPs. Finally, the feasibility of the main results is provided to verify the finite-time event-triggered control strategy.

The adaptive fuzzy predefined-time tracking control problem for a class of nonlinear systems with output hysteresis is investigated in this article. An inverse model is utilized to capture the output hysteresis phenomenon, and then, the Nussbaum-type function technique is utilized to overcome the difficulty of unknown time-varying control gain caused by output hysteresis. An adaptive fuzzy control scheme under the backstepping framework is developed using the predefined-time stability criterion. Different from the existing predefined-time design approaches, the adaptive law designed in this article is represented as a nonlinear differential equation. Theoretical analysis demonstrates that all signals of the closed-loop systems are bounded, and the tracking error can converge to the neighborhood near the origin within an expected settling time. The developed scheme's feasibility is verified by an example of an electromechanical system.

For nonlinear space-varying parabolic partial differential equation systems (PPDESs) with random time-varying delay, this article introduces a dynamic fuzzy boundary output feedback (DFBOF) control under noncollocated boundary measurement (NCBM). Initially, the nonlinear delayed PPDESs are represented by Takagi–Sugeno (T–S) fuzzy models and random time-varying delay is considered by taking the influence of uncertain factors, which belongs to two intervals in a probabilistic way. Since the system state is not fully available and NCBM makes the boundary control design very difficult, a fuzzy observer under NCBM is presented to surmount the design difficulty. Subsequently, an observer-based fuzzy boundary controller is proposed and spatial linear matrix inequality (SLMI)-based sufficient conditions to ensure mean-square exponential stability are obtained for closed-loop delayed PPDESs by utilizing the Lyapunov direct method and Wirtinger inequality. Then, to solve the SLMIs, the feasibility conditions of DFBOF controller design for nonlinear delayed PPDES are expressed in LMIs. Finally, two examples are offered to demonstrate the validity of the presented dynamic fuzzy boundary control approach.

This article addresses the design of an event-triggered finite-time singularity-free terminal sliding-mode control algorithm for the tracking of Euler–Lagrange (EL) systems subject to state/error constraints, unstructured dynamics, and external disturbances. First, a novel sliding-mode manifold (SMM), not only devoted to the finite-time convergence of tracking errors to a small residual set around zero but also ensuring stringent constraint requirements, is proposed. The SMM is available for both the constrained and unconstrained EL systems in a unified manner with no structural changes. Then, a fuzzy logic system is introduced to dynamically compensate for uncertainties in the system. An extra robustifying term is added to the training policy to accelerate online learning. Also, an event-triggered mechanism is integrated into the tracker design procedure to reduce the frequency of signal transmission. Stability analysis proves that all the closed-loop signals are uniformly bounded, and numerical simulations further illustrate the theoretical findings.

This article investigates a distributed fuzzy adaptive formation control for quadrotor multiple unmanned aerial vehicles (UAVs) under unmodeled dynamics and switching topologies. The UAVs dynamics model is described by the Newton–Euler formula, and the actuator faults are considered in the system model in the form of multiplicative factors and additive factors. Due to the underactuated characteristics of the UAVs, two objective attitude commands are generated by designing a virtual control signal, which are transmitted to the attitude subsystem, and then the position controller is solved. By constructing a distributed communication mechanism between UAVs, an adaptive formation control strategy is proposed, which can enable UAVs to update their position and speed online according to their neighbor information, and then achieve the required formation. In addition, a fuzzy adaptive sliding mode controller is designed to ensure that the tracking errors of UAVs converge to the neighborhood of the origin. Finally, the simulation results verify the effectiveness of the proposed control strategy.

This article concerns optimal prescribed-time formation control for a class of nonlinear multiagent systems (MASs). Optimal control depends on the solution of the Hamilton–Jacobi–Bellman equation, which is hard to be calculated directly due to its inherent nonlinearity. To overcome this difficulty, the reinforcement learning strategy with fuzzy logic systems is proposed, in which identifier, actor, and critic are used to estimate unknown nonlinear dynamics, implement control behavior, and evaluate system performance, respectively. Different from the existing optimal control algorithms, a new performance index function considering formation error cost and control input energy cost is constructed to achieve optimal formation control of MASs within a prescribed time. The presented control strategy can ensure that the formation error converges to the desired accuracy within a prescribed time. Finally, the validity of the presented strategy is verified via a simulation example.

This work is devoted to an$H_{\infty }$sliding-mode control for nonlinear singularly perturbed system under a network control framework. Aiming at describing the nonlinearities with parameter uncertainties in the studied system, the idea of interval type-2 (IT2) fuzzy method is employed in the system modeling. A disturbance observer is developed to acquire the unknown disturbance, and the estimated value is implemented into the controller for neutralizing the influence of the disturbance. To further reduce the occupation of network resources, an event-triggered communication protocol with a dynamic threshold parameter is adopted, where the triggered condition can be adjusted adaptively as the evolution of the system states. Then, an appropriate fuzzy integral sliding motion is constructed based on the constructed system model and disturbance estimation. It is worth noting that the constructed fuzzy controller follows the idea of nonparallel distribution compensation, which improves the designed flexibility. In terms of fuzzy processing, by introducing the membership functions dependent method into the stability analysis, a series of relaxed criteria are established to ensure the global asymptotic stability for the closed-loop systems with$H_{\infty }$performance level. Finally, two classical examples of circuit model and inverted pendulum model are extended to IT2 fuzzy idea, showing the rationality of the proposed approach.

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 decentralized adaptive fuzzy sampled-data control problem for switched large-scale nonlinear systems with time-varying delays is considered in this article. Fuzzy logic systems are applied to handle unknown nonlinear terms. A novel fuzzy adaptive law is proposed utilizing only the information of the system states at sampling instants. Moreover, a proper CLF and a new decentralized adaptive sampled-data control law are constructed to ensure that all states of the CLS are bounded. The developed strategy’s effectiveness is verified with two examples.

This article proposes a novel unknown input functional observer design approach toward discrete-time interval type-2 Takagi–Sugeno fuzzy system models subject to measurable and unmeasurable premise variables. By constructing a new state vector that contains both the unknown inputs and the system states, functional observers are proposed for the cases with measurable and unmeasurable premise variables to estimate this new state vector for unknown input and/or state estimation. The observer design problem is converted into the solvability issue of a linear matrix equation involving observer gain matrices, and the existence conditions of the observers are explicitly obtained based on matrix rank analysis. Meanwhile, instead of solving the intricate Sylvester equation directly, the solution of the simplified matrix equation is employed to derive the observer gains. Moreover, the effectiveness and the superiority of the presented method are demonstrated via two illustrative examples.

In this paper, relaxed observer-based state estimation of discrete-time Takagi–Sugeno fuzzy systems is proposed via developing a new augmented matrix approach. In contrast with the recent method reported in the literature, a more advanced introduction mode of additional matrices is developed without any redundant constraint under the framework of homogeneous polynomials. Then, the proposed augmented matrices can be built in order to depict the characteristic information of each possible working mode in a superior way, and thus our obtained result is less conservative than the recent one. Moreover, it is worth noting that that the recent method belongs to the special case of ours. Finally, two numerical simulations are given to test and validate the superiority and generality of the developed 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 focuses on the problem of fuzzy-model-based fault-tolerant control for nonlinear semi-Markov jump systems in the discrete-time context. The discrete-time semi-Markov process is employed to describe the mode jumping among several subsystems in the investigated systems. Moreover, the nonlinear characteristics are effectively tackled through utilizing the Takagi–Sugeno fuzzy model. Unlike some previous results, the information of the semi-Markov kernel (SMK) in this article is assumed to be partially available, which conforms better with the practical scenarios. Besides, considering the case that actuators may encounter some unexpected failures during system operation, the fault-tolerant mechanism is introduced in the process of controller design to enhance the fault tolerance of the studied systems. By means of SMK approach and Lyapunov stability theory, some elapsed-time-dependent criteria for guaranteeing the mean-square stability of the closed-loop system are established. According to these criteria, the design methodology of the reliable fuzzy state feedback controller is developed. Eventually, in order to verify the practicability and rationality of the developed control scheme, a single-link robot arm model is presented.

This article considers the H∞sampled-data fuzzy observer (SDFO) design problem for nonlinear parabolic partial differential equation (PDE) systems under spatially local averaged measurements (SLAMs). Initially, the nonlinear PDE system is accurately represented by the Takagi-Sugeno (T-S) fuzzy PDE model. Then, based on the T-S fuzzy PDE model, an SDFO under SLAMs is constructed for the state estimation. To attenuate the effect of the exogenous disturbance and the design disturbance, an H∞SDFO design under SLAMs is developed in terms of linear matrix inequalities by utilizing Lyapunov functional and inequality techniques, which can guarantee the exponential stability and satisfy an H∞performance for the estimation error fuzzy PDE system. Finally, simulation results on the state estimation of the FitzHugh-Nagumo equation are given to support the presented H∞SDFO design method.

Due to the complicated model characteristics, only a few results focusing on stability analysis have appeared on singularly perturbed descriptor systems (SPDSs). This article instead proposes an integral sliding-mode control strategy for a kind of Takagi-Sugeno fuzzy approximation-based nonlinear SPDSs under time-varying nonlinear perturbation. An appropriate fuzzy integral switching manifold that fully accommodates the system features is designed to completely reject the matched perturbation without amplifying the unmatched one. To facilitate the synthesis of the high-level controller (HLC), the sliding-mode dynamics (SMD) is transformed into an augmented form. Thanks to the adoptions of a novel singular perturbation Lyapunov function, Finsler's lemma, as well as the fixed-point principle, the existence and uniqueness of the solution and the exponential admissibility for the augmented SMD are analyzed. A solution for the designed HLC is further provided. To guarantee the sliding motion, a fuzzy integral sliding-mode controller (FISMC) is synthesized by analyzing the sliding motion reachability. An adaptive FISMC is also given to deal with the unknown upper bounds of the matched perturbation. Finally, the applicability of the developed FISMC strategy is testified by a practical example.

This paper applies an input-based triggering approach to investigate the secure consensus problem in multiagent systems under denial-of-service (DoS) attacks. The DoS attacks are based on the time-sequence fashion and occur aperiodically in an unknown attack strategy, which can usually damage the control channels executed by an intelligent adversary. A novel event-triggered control scheme on the basis of the relative interagent state is developed under the DoS attacks, by designing a link-based estimator to estimate the relative interagent state between intermitted communication instead of the absolute state. Compared with most of the existing work on the design of the triggering condition related to the state measurement error, the proposed triggering condition is designed based on the control input signal from the view of privacy protection, which can avoid continuous sampling for every agent. Besides, the attack frequency and attack duration of DoS attacks are analyzed and the secure consensus is reachable provided that the attack frequency and attack duration satisfy some certain conditions under the proposed control algorithm. “Zeno phenomenon” does not exhibit by proving that there exist different positive lower bounds corresponding to different link-based triggering conditions. Finally, the effectiveness of the proposed algorithm is verified by a numerical example.

This article deals with the feedback control of system which contains randomness. As such system particle swarm optimization algorithm was selected. With some parameters setup, the particle swarm optimization algorithm does not converge to a single point, but particles are moving over the searching space. They oscillate and behave chaotically. This article looks at this problem from the time delay feedback control perspective. The selected particles are controlled to a stable point by adding small perturbations to the particle position. For the perturbation estimation, the analytic programming and self-organizing algorithm were used.

This paper investigates the problem of an active fault tolerant control design for a class of constrained Takagi-Sugeno (T-S) systems subject to input and state constraints. The idea is to synthesize an integrated FTC strategy by combining a descriptor approach and a modified structure of parallel distributed compensation control laws. This ensures the closed-loop system stability of the faulty system and respect the given saturation constraints on the control input. A descriptor observer is needed to estimate simultaneously the faults and the faulty system states needed to reconfigure the control law. The optimization problem is formulated in terms of linear matrix inequality constraints (LMIs). Numerical example is given to show the effectiveness of the proposed approach.

The establishment of recent laws regarding wastewater treatment has made the treatment of sewage sludge in incineration processes even more complex. In an attempt to follow this new environmental legislation, SIAAP optimizes its wastewater treatment by using a bubbling fluidized-bed incinerator technique. Its PID control system does not ensure an appropriate operation of incineration due to the complexity of the incinerator structure, what can lead the system into a dangerous behavior. Thus, a predictive control system is required in order to foresee any deviation from incineration behavior. The predictive control allows the incineration system to prevent itself from risky situations by acting in advance. This paper describes a preliminary methodology to obtain a predictive-control numerical corrector. This corrector forecasts the future outputs of a PID-controlled incineration model via Matlab/Simulink simulations and operates the system closed-loop feedback so as to avoid system instabilities.

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 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.

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

This paper 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.

This paper investigates the path-following control problem for four-wheel independently actuated autonomous ground vehicles through integrated control of active front-wheel steering and direct yaw-moment control. A modified composite nonlinear feedback strategy is proposed to improve the transient performance and eliminate the steady-state errors in path-following control considering the tire force saturations, in the presence of the time-varying road curvature for the desired path. Path following is achieved through vehicle lateral and yaw control, i.e., the lateral velocity and yaw rate are simultaneously controlled to track their respective desired values, where the desired yaw rate is generated according to the path-following demand. CarSim-Simulink joint simulation results indicate that the proposed controller can effectively improve the transient response performance, inhibit the overshoots, and eliminate the steady-state errors in path following within the tire force saturation limits.

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

In this work, we focus on the synthesis of a Proportional Integral (PI) observer for the actuators and sensors faults diagnosis based on Takagi-Sugeno (TS) fuzzy model with unmeasurable premise variables. The faults estimation method is based on the assumption that these faults act as unknown inputs under polynomials form whose their kth derivatives are bounded. The convergence conditions of the observer as well as the faults reconstruction are established on the basis of the Lyapunov stability theory and the L2optimization technique, expressed as Linear Matrix Inequalities (LMI) constraints. In order to validate the proposed approach, a hydraulic system with two tanks is proposed.

This paper deals with the stabilization problem of discrete-time Takagi-Sugeno (T-S) fuzzy systems via static output controller (SOFC). The proposed method uses the descriptor approach to study this problem and leads to strict linear matrix inequality (LMI) formulation. In contrast with the existing results, the method allows coping with multiple output matrices, as well as uncertainties. Moreover, the new proposed method can lead to less conservative results by introducing slack variables and considering multiple Lyapunov matrices. A robust SOFC for uncertain T-S fuzzy models is also derived in strictLMIterms. Numerical examples are given to illustrate the effectiveness of the proposed design results.

The investigated fault tolerant control (FTC) problem for uncertain nonlinear systems with external disturbances against actuator faults is addressed. The aim is to synthesize a fault tolerant controller ensuring trajectory tracking for a class of nonlinear systems represented by Takagi-Sugeno (T-S) models with measurable premise variables. In order to design the FTC law a proportional integral observer (PIO) is adopted which estimate both of the faults and the faulty system states. Based on Lyapunov theory, and L2optimization, the trajectory tracking performance and the stability of the closed loop system are analyzed. Sufficient conditions are obtained in terms of linear matrix inequalities (LMI). Simulation results show the effectiveness of the proposed method.

This paper describes a methodology for estimating both vehicle dynamics and road geometry using a Fuzzy unknown input observer. Vehicle sideslip and roll parameters are estimated in presence of the road bank angle and the road curvature as unknown inputs. The unknown inputs are then estimated using the observer results. The used nonlinear model deduced from the vehicle lateral and roll dynamics with a vision system is represented by a Takagi-Sugeno (TS) fuzzy model in order to take into account the nonlinearities of the cornering forces. Taking into account the unmeasured variables, an unknown inputs (TS) observer is then designed on the basis of the measure of the roll rate, the steering angle and the lateral offset given by the distance between the road centerline and the vehicle axe at a look-ahead distance. Synthesis conditions of the proposed fuzzy observer are formulated in terms of Linear Matrix Inequalities (LMI) using Lyapunov method. Simulation results show good efficiency of the proposed method to estimate both vehicle dynamics and road geometry.

This paper deals with the Hinfinestimation of both system state and faults for T-S (Takagi-Sugeno) fuzzy model with bounded input disturbances. Based on the descriptor technique, the sensor faults are considered as an auxiliary state variable. Then a descriptor observer for the obtained augmented system is designed and the observer gains are determined in LMI (Linear Matrix Inequalities) formulation. This method has the advantage to estimate the state variables and the sensor faults simultaneously with Hinfinapproach. Numerical example shows the efficiency of the proposed method.

This paper deals with the robust fuzzy observer design problem for a class of uncertain nonlinear system represented by Takagi-Sugeno model. Stability conditions of such observers are expressed in terms of linear matrix inequalities (LMI). An example of simulation is given to illustrate the proposed method.

This paper deals with the stabilization of Takagi-Sugeno (T-S) models using state feedback controllers. Relaxed sufficient exponential stability conditions are given for both continuous and discrete multiple models. The stability conditions of the closed loop multiple models are expressed in linear matrix inequalities (LMI) form. To optimize the degree of stability, a formulation in term of generalized eigenvalues problem (GEVP) is proposed.

This paper discusses conditions on stability and stabilization of continuous T-S fuzzy systems. Stability analysis is derived via a non-quadratic Lyapunov function technique and LMIs (linear matrix inequalities) formulation to obtain an efficient solution. The non-quadratic Lyapunov function is built by inference of quadratic Lyapunov function of each local model. We show that the stability condition of the open-loop T-S systems is assured under certain restrictions on the rate of change of state variables. Following a similar approach, the stabilization of closed-loop continuous T-S fuzzy systems using the well-known PDC (parallel distributed compensation) technique is investigated. The design methodology is illustrated by numerical examples.