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In this article, a multi‐event‐triggered (MET) near‐optimal tracking control algorithm is developed for a discrete‐time (DT) nonlinear non‐zero‐sum game (NZSG). Firstly, the iterative dual heuristic dynamic programming (DHP) approach is utilized to obtain the optimal tracking control set. Subsequently, distinct independent triggering conditions are formulated for control inputs to improve the utilization efficiency of resources and ensure that the independence between them and the closed‐loop system has an excellent control performance. Meanwhile, input‐to‐state stability (ISS) is proven for the MET tracking plant. Additionally, neural networks (NNs) are designed in the iterative DHP algorithm to construct the model module, the critic module and the action modules, with aims to identify the unknown system, approximate costate function and estimate optimal tracking control strategy set, respectively. Finally, two numerical experimental simulations are conducted to verify the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper proposes an event‐triggered adaptive control algorithm for general continuous‐time systems with unknown system models. Unlike existing articles, the developed method does not require prior determination of the knowledge of system dynamics, and effectively reduces the update frequency of key signals through the introduction of event‐trigger mechanism. Three neural networks (NNs) are designed in the identifier‐critic‐actor (ICA) architecture to learn the optimal control solution online. The unknown system is approximated by the identifier NN, the critic NN is designed to approach the optimal cost function, and the actor NN is designed to approach the optimal controller. Besides, under event‐triggered control, the parameters of critic NN and actor NN as well as control signals are updated only at the trigger time determined by the event‐trigger condition, which reduces effectively the computing burden and communication cost. The stability of event‐trigger control and the convergence of parameters of three NNs are verified via Lyapunov method. Finally, two examples are presented to demonstrate the viability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This article studies the weighted memory-event-triggered $H_{\infty}$ static output control issue of Takagi–Sugeno fuzzy wind turbine systems with uncertainty. To decrease the frequency of data communication, a novel adaptive memory-event-triggered mechanism is presented to choose the “necessary” control signals, which has the following two benefits. First, a weighted average signal over a historic period is utilized as the input of event-triggered scheme, instead of the current system information in the conventional one. This could reduce the control signal updating rate and avoid the false triggering events incurred by stochastic environment noises and disturbances. Second, a dynamic triggering threshold is adopted to adaptively regulate the control signal updating frequency along with the average signal. By applying the distributed delay system method to describe the weighted historic signal, a new uncertain T–S fuzzy wind turbine system with distributed delay is established. With the aid of the measured outputs and the integral inequality based on the weighting function of the average signal, the memory-event-triggered static output controller design conditions are obtained to ensure the system exponential stability and the $H_{\infty}$ performance. Lastly, an experiment platform integrating Zigbee modules as the wireless network is set up to illustrate the advantages of the proposed strategy. [ABSTRACT FROM AUTHOR]

In this article, an event-triggered practical fixed-time containment control issue for stochastic nonlinear multiagent systems is addressed. The event-triggering mechanism is designed for the controller update which is reduced update frequency. In combination with the backstepping technique and fuzzy logic systems, an adaptive fuzzy containment control strategy is developed for stochastic uncertain multiagent systems. By adopting the protocol, an adaptive containment controller is devised, which can ensure that the containment errors gather to a small range in fixed time. Finally, simulation results for the practical example demonstrate the correctness of the proposed scheme. [ABSTRACT FROM AUTHOR]

A novel multi-instant gain-scheduling scheme is proposed in order to further improve the state estimation performance of discrete-time Takagi–Sugeno fuzzy systems in this article. Different from those existing methods, the enabled working pattern at each sampling instant can be determined in cycle according to utilization of the updated information of adjacent normalized fuzzy weighting functions in depth, and then the featured time-varying free matrices are introduced for each working pattern, which can remove those existing constraints on traditional free matrices. Consequently, the conservatism can be reduced significantly and it does broaden the range of application for observer-based fuzzy state estimation. Despite the fact that the quantity of the free matrices becomes much bigger than before, it needs to be pointed out that the actual computational complexity only increases by a small amount through depressing the number of cycles. Given that the developed designing process belongs to an offline computation, it is an acceptable tradeoff between the increasement of computational burdens and the reduction of conservatism. In the end, the effectiveness of the developed method is illustrated through the detailed analysis of numerical simulations. [ABSTRACT FROM AUTHOR]

This article is concerned with the fuzzy functional observer-based finite-time adaptive sliding-mode control for nonlinear systems with the partly unmeasurable states and some matched uncertainties. First, considering that the upper bound of the uncertain function exists but unknown, a fuzzy functional observer (FFO) with an adaptive compensator is constructed. Second, an FFO-based fuzzy integral sliding mode controller (ISMCr) is designed such that the closed-loop fuzzy systems are finite-time bounded with $H_{\infty }$ performance over the reaching phase, the sliding phase, and the whole finite-time interval, respectively. To reduce the conservatism and increase the solution space of linear matrix inequality conditions, the fuzzy Lyapunov functional approach and equivalent fuzzy relaxed matrices technique are developed by introducing some relaxed matrices in the derivative of the fuzzy normalized membership function. Compared with the common Luenberger-type observer-based approach, the gain matrices of ISMCr depend on the FFO designed, which also enhances the flexibility of controller design. Finally, a simulation example with some comparison is given to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This article considers an adaptive fuzzy control problem for nonstrict-feedback nonlinear stochastic systems, which contain input delay, output constraints, and unknown control coefficients, simultaneously. First, an original stochastic nonlinear mapping and the Pade approximation transformation techniques are developed to solve the symmetric output constraints and input delay. Then, an adaptive fuzzy controller is designed for the unknown nonlinear systems, in which the Nussbaum function is employed to deal with the unknown time-varying control coefficients. Tracking errors are ensured to converge to a small neighborhood around the origin, and the system output does not violate the predefined constrained conditions. All the signals of the closed-loop systems have proven to remain bounded in probability. Moreover, the asymmetric output-constrained control is also studied. Two simulation examples are provided to show the effectiveness of the developed method. [ABSTRACT FROM AUTHOR]

An observer-based containment control issue is addressed for a class of uncertain nonlinear multiagent systems with a directed topology via the active disturbance rejection control and backstepping techniques. A kind of nonlinear extended state observers (ESOs) based on fractional power functions is developed, and the estimations of extended states are utilized to compensate uncertain dynamics in real time. Compared with linear ESOs, the advantages of the ESOs in this paper lie in peaking reduction and better tolerance of measurement noise for the closed-loop system. Moreover, tracking differentiators are employed to avoid the explosion of complexity caused by repeated differentiations of nonlinear functions. It is proven that the containment errors of the followers converge to small neighborhoods of the origin and they are adjustable by suitable choice of parameters. Finally, two simulation examples, both practical and numerical ones, are shown to demonstrate the effectiveness of the proposed control approach. [ABSTRACT FROM AUTHOR]

In this paper, the security issue of networked T–S fuzzy system (NTFS) is investigated under intermittent DoS jamming attack (I-DoS-JA). This kind attack often causes the hiatus of control input and output feedback in communication channels. In order to compensate the missing data caused by attacks, a model-based predictive control framework is proposed by embedding predictors within the closed-loop NTFS, in which: (1) a T–S fuzzy model is formulated with the consideration of I-DoS-JA. Based on this model, a fuzzy observer is constructed to estimate the unmeasurable system states; (2) the predictors embedded in remote plant and local controller are modeled as a synchronous T–S fuzzy system; and (3) an event-trigger mechanism is integrated in the observer-based predictor, which would take great advantages in saving bandwidth sources. Finally, a nonlinear system is given as an example to substantiate the work of this paper. [ABSTRACT FROM AUTHOR]

This paper is focused on the security control problem for the dynamic event-triggered networked T-S fuzzy control system with non-uniform sampling and stochastic network attacks. Different from existing research, the signals from sensor are sampled in a non-uniform period. To improve communication, a novel dynamic event-triggered method is proposed, which can adjust the trigger parameters dynamically according to the changes of external output. Then, a T-S fuzzy dynamic output feedback control model with event-triggered mechanism and network attacks is constructed. Furthermore, by introducing a set of slack matrix, the asynchronous premise variable problem caused by event-triggered method is solved, and some sufficient conditions that insure the asymptotic stability with an H ∞ performance requirement of the established closed-loop system can be obtained by utilizing Lyapunov function method and stochastic analysis technique. Moreover, the gains of the fuzzy dynamic output feedback controllers are found by solving the linear matrix inequalities (LMIs). Finally, two practical examples are given to support the merits and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The problem of enhancing the adaptability against unreliable communication channels for fuzzy system is investigated by proposing a new multi-instant fuzzy switching controller with a more flexible switching manner. Compared with recent methods in the reported literature, more switching modes have been designed according to the updated information at each sampling instant and thus much more freedom can be created by introducing and assigning additional variables for all the possible switching modes. As a result, the adaptability against unreliable communication channels can be enhanced from two aspects: for the same health quality of communication channels, much larger controllable domain can be provided; or for the same system parameters, a worse health quality of communication channels can be affordable. Finally, several advantages over previous fuzzy approaches are verified by means of numerical simulations. • The adaptability against unreliable communication channels is inhanced by proposing a new multi-instant fuzzy switching controller. • More switching modes have been designed according to the updated information at each sampling instant and thus much more freedom can be created. • For the same health quality of communication channels, much larger controllable domain is obtained. [ABSTRACT FROM AUTHOR]

• Few people consider the data-driven parameter adaptive control in the tracking control of AUV. Based on the traditional sliding mode control, a novel adaptive tracking control considering the external environment interference and uncertainty is designed using a data-based online ADHDP method. • The Lyapunov function of ADHDP algorithm considering experience replay is designed to obtain the stability results and ensure the convergence of data-driven neural network weights. • The weighted experience replay method is considered, and the historical data is weighted with a novel parameter λ and is used for the control strategy online real-time update. At present, there are few simultaneous studies. A novel adaptive trajectory tracking control method is proposed in this paper for autonomous underwater vehicle systems with external disturbances. The control action is composed of sliding mode control and action-depended heuristic dynamic programming (ADHDP) controller to track the expected sailing position and angle in the horizontal coordinate system with fixed depth. As an auxiliary control of sliding mode control, the ADHDP controller observes the difference between the actual sailing position/angle and the expected sailing position/angle, and adaptively provides corresponding supplementary control actions using a data-driven fashion. At the same time, we design a weight-related priority experience replay (PER) technology to update the online weight network by using the relevant historical data stored in the database to improve the learning rate. The proposed algorithm can adjust parameters online under various conditions. Furthermore, it is very suitable for underwater vehicle systems with parameter uncertainties and external disturbances. Based on Lyapunov stability method, the stability of closed-loop system state and network weight error is analyzed. Finally, the validity of our control strategy is verified by simulation. [ABSTRACT FROM AUTHOR]

• This paper proposes a novel adaptive finite-time control strategy for a class of nonstrict-feedback stochastic systems, in which the input delay and output constraints problems exist simultaneously. Compared with Refs. [28,38,51] , this paper dealt with these problems in a unified framework. • A new nonlinear mapping is developed under the stochastic version, which is different from the deterministic one. Based on this approach, the symmetric and asymmetric output constraints problems are researched in the same control scheme. Moreover, some disadvantages existing in the barrier Lyapunov function control approach can be avoided. • The input delay problem is handled by the Pade approximation technique, which transforms the original delay input systems into delay-free ones. Finally, by the presented finite time control strategy, the tracking error signals can converge into a small neighborhood of origin point in probability during the finite time. This paper addresses a novel finite-time adaptive neural control (FTANC) problem for nonstrict-feedback stochastic nonlinear systems (NSFSNS), in which the input delay and output constrained problems are considered simultaneously. First, the Pade approximation technique is adopted to transform the delay input system into a delay-free one. Second, the stochastic nonlinear mapping technique is developed to solve the symmetric and asymmetric output constraints in the system. Then, the adaptive neural controller is designed based on backstepping technique, such that the closed-loop systems are semi-globally practical finite-time stable (SGPFTS) in probability, and the tracking error converges to a small neighborhood of the origin after a finite period of time. Two simulation examples show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper studies the problem of adaptive fault tolerant control for a class of switched nonlinear systems with unknown control directions. Through a coordinate transformation, an equivalent system is constructed to eliminate the adverse effects of the unknown control coefficients. Then combining with fuzzy approximation and backstepping design principle, an adaptive fault tolerant control scheme is proposed. Further, by constructing common Lyapunov functions, it is proved that closed-loop system is bounded and the tracking error remains in a neighborhood of the origin. The given simulation example demonstrates the effectiveness of the proposed control approach. [ABSTRACT FROM AUTHOR]