This paper addresses the problem of adaptive tracking control for a class strict-feedback nonlinear state constrained systems with input delay. To alleviate major challenges caused by appearances full constraints and delay, an appropriate barrier Lyapunov function opportune backstepping design are used to avoid constraint violation, Pade approximation intermediate variable employed eliminate effect Neural networks estimate unknown functions in procedure. It is proven that closed-loop signals...

In the paper, adaptive observer and controller designs based fuzzy approximation are studied for a class of uncertain nonlinear systems in strict feedback. The main properties considered that all state variables not available measurement at same time, they required to limit each constraint set. Due systems, it will be difficult task designing stability analysis. Based on structure is framed estimate unmeasured states. To ensure states do violate their bounds, Barrier type functions employed...

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,...

In this paper, a framework of adaptive control for switched nonlinear system with multiple prescribed performance bounds is established using an improved dwell time technique. Since the subsystems are different from each other, coordinate transformations have to be tackled when transformed, which not been encountered in some systems. We deal by finding specific relationship between any two transformations. To obtain much less conservative result, contrast common law, laws both active and...

In this paper, a new adaptive approximation-based tracking controller design approach is developed for class of uncertain nonlinear switched lower-triangular systems with an output constraint using neural networks (NNs). By introducing novel barrier Lyapunov function (BLF), the constrained system first transformed into without any constraint, which means control objectives both are equivalent. Then command filter technique applied to solve so-called "explosion complexity" problem in...

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 proposes an adaptive neural control method for a class of nonlinear time-varying delayed systems with full-state constraints. To address the problems constraints and delays in unified framework, is investigated first time. The time delay constraint are main factors limiting system performance severely even cause instability. effect unknown eliminated by using appropriate Lyapunov-Krasovskii functionals. In addition, constant only special case which leads to more complex difficult...

This paper addresses formation control with obstacle avoidance problem for a class of second-order stochastic nonlinear multiagent systems under directed topology. Different deterministic systems, cases are more practical and challenging because the exogenous disturbances depicted by Wiener process considered. In order to achieve objective, both leader-follower approach artificial potential field (APF) method combined together, where is utilized solve problem. For obtaining good system...

In this paper, the problem of adaptive neural tracking control for a type uncertain switched nonlinear nonlower-triangular system is considered. The innovations paper are summarized as follows: 1) input to state stability unmodeled dynamics removed, which an indispensable assumption design nonswitched dynamic systems; 2) difficulties caused by structure handled applying universal approximation ability radial basis function networks and inherent properties Gaussian functions, avoids...

This article studies the predefined time control design issue for uncertain nonlinear systems with full-state error constraints and unmeasurable states first time. Compared existing works, this study enables controlled system to stabilize within a predetermined ensures that tracking errors converge desired accuracy range, even in absence of measurable state information. Fuzzy logic (FLSs) are applied handle unknown dynamics, FLSs-based observer is constructed estimate states. With universal...

Although adaptive control design with function approximators, for example, neural networks (NNs) and fuzzy logic systems, has been studied various nonlinear the classical laws derived based on gradient descent algorithm σ-modification or e-modification cannot guarantee parameter estimation convergence. These nonconvergent learning methods may lead to sluggish response in system make tuning complex. The aim of this paper is propose a new strategy driven by error alternative servo systems....

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 article, an adaptive neural network (NN) decentralized output-feedback control design is studied for the uncertain strict-feedback large-scale interconnected nonlinear systems with nonconstant virtual and gains. NNs are utilized to approximate unknown functions, immeasurable states estimated via designing NN state observer. By constructing logarithm Lyapunov observer-based backstepping developed in framework of control. The proposed can make that closed-loop system semiglobally...

In this paper, an optimal control algorithm is designed for uncertain nonlinear systems in discrete-time, which are nonaffine form and with unknown dead-zone. The main contributions of paper that the first time framed dead-zone, adaptive parameter law dead-zone calculated by using gradient rules. mean value theory employed to deal input implicit function based on reinforcement learning appropriately introduced find ideal controller approximated action network. Other neural networks taken as...

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...

Considering the uncertain nonstrict nonlinear system with dead-zone input, an adaptive neural network (NN)-based finite-time online optimal tracking control algorithm is proposed. By using errors and Lipschitz linearized desired function as new state vector, extended present. Then, a novel Hamilton-Jacobi-Bellman (HJB) defined to associate nonquadratic performance function. Further, upper limit of integration selected convergence time, in which input considered. In addition, Bellman error...

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...