This paper concentrates upon the problem of finite-time fault-tolerant control for a class switched nonlinear systems in lower-triangular form under arbitrary switching signals. Both loss effectiveness and bias fault actuator are taken into account. The method developed extends traditional convergence from nonswitched to version by designing appropriate controller adaptive laws. In contrast previous results, it is first time handle tolerant system while stability also necessary. Meanwhile,...

Understanding complex systems is fundamental to understanding science. The complexity of such makes them very difficult understand because they are composed multiple interrelated levels that interact in dynamic ways. goal this study was how experts and novices differed their two systems, the human respiratory system an aquarium ecosystem. In particular, we examined a representation Structure-Behavior-Function theory (SBF), might account for these differences. SBF particularly relevant...

In this paper, an adaptive neural network (NN) control scheme is proposed for a quarter-car model, which the active suspension system (ASS) with time-varying vertical displacement and speed constraints unknown mass of car body. The NNs are used to approximate It commonly known that stability security ASSs will be weakened when violated. Thus, problem very important task because demand handing safety. barrier Lyapunov functions guarantee not violated, it can prove closed-loop system. Finally,...

This paper is concerned with a reinforcement learning-based adaptive tracking control technique to tolerate faults for class of unknown multiple-input multiple-output nonlinear discrete-time systems less learning parameters. Not only abrupt are considered, but also incipient taken into account. Based on the approximation ability neural networks, action network and critic proposed approximate optimal signal generate novel cost function, respectively. The remarkable feature method that it can...

In this paper, an adaptive neural network (NN) control approach is proposed for nonlinear pure-feedback systems with time-varying full state constraints. The of paper are assumed to possess function uncertainties. By using the mean value theorem, can be transformed into strict feedback forms. For newly generated systems, NNs employed approximate unknown items. Based on scheme and backstepping algorithm, intelligent controller designed. At same time, Barrier Lyapunov functions (BLFs) error...

In this article, the problem of tracking control for a class nonlinear time-varying full state constrained systems is investigated. By constructing asymmetric barrier Lyapunov function (BLF) and combining it with backstepping algorithm, intelligent controller adaptive law are developed. Neural networks (NNs) utilized to approximate uncertain function. It well known that in past research constraints, constraint boundary either constant or boundaries both related time investigated, which makes...

In this article, the adaptive fault-tolerant control (FTC) problem is solved for a switched resistance-inductance-capacitance (RLC) circuit system. Due to existence of faults which may lead instability subsystems, innovation article that unstable subsystems are taken into account in frame output constraint and unmeasurable states. Obviously, there not any unswitched systems. The will involve many serious consequences difficulties. Since system states unavailable, state observer designed....

This paper studies an adaptive neural network (NN) tracking control method for a class of uncertain nonlinear strict-feedback systems with time-varying full-state constraints. As we all know, the states are inevitably constrained in actual because safety and performance factors. The main contributions this that: 1) order to ensure that do not violate asymmetric constraint regions, NN controller is constructed by introducing barrier Lyapunov function (TVBLF) 2) amount learning parameters...

In this paper, the main focus is to cope with fault detection and estimation (FDE) fault-tolerant control (FTC) issues of nonlinear single input output model-free system (MFS), while only input/output data are utilized. First, in accordance pseudo-partial-derivative approach, original transformed into a compact form dynamic linearization model, which one parameter employed. Second, an estimator developed detect fault. A key highlight design time varying residual threshold. Moreover, online...

In this article, we study the control problem of vehicle active suspension systems (ASSs) subject to actuator failure. An adaptive scheme is presented stabilize vertical displacement car-body. Meanwhile, ride comfort, road holding, and space limitation can be guaranteed. order overcome uncertainty, neural network developed approximate continuous function with unknown car-body mass. Furthermore, improve transient regulation performance ASSs when failure occurs, propose a novel prescribed...

In the novel, an adaptive neural network (NN) controller is developed for a category of nonlinear stochastic systems with full state constraints and unknown time delays. The control quality system stability suffer from problems delays which frequently arises in most real plants. considered are transformed into new constrained free based on mappings, such that never violated feasibility conditions virtual controllers (the values its derivative assumed to be known) removed. To compensate...

In this paper, an adaptive neural network (NN) controller is proposed for a class of nonlinear active suspension systems (ASSs) with hydraulic actuator. To eliminate the problem "explosion complexity" inherently in traditional backstepping design actuator, dynamic surface control technique developed to stabilize attitude vehicle by introducing first-order filter. Meanwhile, presented scheme improves ride comfort even when uncertain parameter exists. Due existence terms, NNs are used...

This paper focuses on the adaptive control problem for a class of nonlinear single-input single-output lower triangular systems with partial state constraints and unknown backlash-like hysteresis. To prevent states from transgressing predefined constrained region, barrier Lyapunov function is presented, whose values will increase to infinity when any its parameters grows given boundary value. counteract effect caused by hysteresis, an auxiliary variable mechanism introduced in backstepping...

This article investigates an adaptive finite-time neural control for a class of strict feedback nonlinear systems with multiple objective constraints. In order to solve the main challenges brought by state constraints and emergence stability, new barrier Lyapunov function is proposed first time, not only can it multiobjective effectively but also ensure that all states are always within constraint intervals. Second, combining command filter method backstepping control, controller designed....

This paper concentrates on the reinforcement learning (RL)-based fault-tolerant control (FTC) problem for a class of multiple-input-multiple-output (MIMO) nonlinear discrete-time systems. Both incipient faults and abrupt are taken into account. Based approximation ability neural networks (NNs), an RL algorithm is incorporated FTC strategy, in which action network developed to generate optimal signal critic used approximate novel cost function, respectively. Compared with existing results,...

This article presents an adaptive output feedback approach of nonlinear multi-input-multi-output (MIMO) systems with time-varying state constraints and unmeasured states. An approximator is designed to approximate the unknown functions existing in state-constrained immeasurable To deal tracking problem such systems, a observer barrier Lyapunov (BLFs) introduced controller design procedure. The backstepping BLFs utilized guarantee that all system states remain within time-varying-constrained...

This article investigates an adaptive fuzzy tracking control approach via output feedback for a class of switched uncertain nonlinear systems with full-state constraints under arbitrary switchings. The observer and controller are designed based on approximation. main characteristic discussed is that the state variables not available measurement need to be kept within constraint set. In order estimate unmeasured states, constructed. To guarantee all states do violate time-varying bounds,...

A new fuzzy adaptive control method is proposed for a class of strict feedback nonlinear systems with immeasurable states and full constraints. The logic system used to design the approximator, which deals uncertain continuous functions in process backstepping design. use an integral barrier Lyapunov function not only ensures that all are within bounds constraint, but also mixes errors directly constrain state, reducing conservativeness constraint satisfaction condition. Considering most...

This article concentrates on the event-driven controller design problem for a class of nonlinear single input output parametric systems with full state constraints. A varying threshold triggering mechanism is exploited, which makes communication more flexible. Moreover, from viewpoint energy conservation and consumption reduction, system capability becomes better owing to contribution proposed event-triggered mechanism. In meantime, developed control strategy can avoid Zeno behavior since...

This article addresses a distributed time-varying optimal formation protocol for class of second-order uncertain nonlinear dynamic multiagent systems (MASs) based on an adaptive neural network (NN) state observer through the backstepping method and simplified reinforcement learning (RL). Each follower agent is subjected to only local information measurable partial states due actual sensor limitations. In view optimized strategic needs, dynamics undetectable may jointly affect stability...

This article presents an adaptive fuzzy fixed time time-varying formation control (TVFC) method for uncertain heterogeneous nonlinear multiagent systems (HNMASs) with full state constraints. Meanwhile, both partial loss of effectiveness and bias fault are considered in HNMASs. The logic selected as effective tool to approximate functions. original constrained states the will be converted unconstrained by transformed function. Compared previous papers, it is first handle TVFC problem HNMASs...

This brief investigates the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> state estimation problem for neural networks with time-varying delay. First, an extended reciprocally convex inequality based on notation="LaTeX">$r$ -degree polynomial matrix is presented, which considers more information of high-order time delay and flexibility can be obtained. Second,...