Finite‐time prescribed performance adaptive fuzzy fault‐tolerant control for nonstrict‐feedback nonlinear systems

In this paper, the issue of adaptive neural control is discussed for a class of stochastic nonstrict-feedback constrained nonlinear systems with input and state unmodeled dynamics. A dynamic signal produced by the first-order auxiliary system is employed to deal with the dynamical uncertain terms. Radial basis function neural networks are used to reconstruct unknown nonlinear continuous functions. With the help of the mean value theorem and Young's inequality, only one learning parameter is adjusted online at recursive each step. Using the hyperbolic tangent function as nonlinear mapping, the output constrained stochastic nonstrict-feedback system in the presence of unmodeled dynamics is transformed into a novel unconstrained stochastic nonstrict-feedback system. Based on dynamic surface control technology and the property of Gaussian function, adaptive neural control is developed for the transformed stochastic nonstrict-feedback system. The output abides by stochastic constraints in probability. By the Lyapunov method, all signals of the closed-loop control system are proved to be semi-global uniform ultimate bounded (SGUUB) in probability. The obtained theoretical findings are verified by two numerical examples.

“…Based on Equations (25), (26), (27), (28), (29), (30), the system (1) can be transformed into the following form…”

Section: Adaptive Controller Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive dynamic surface control of stochastic nonstrict‐feedback constrained nonlinear systems with input and state unmodeled dynamics

Chen

Zhang

2020

“…Recently, some scholars have concentrated their attention on the finite time (FT) stability, [18][19][20][21] particularly, Reference 18 studied state feedback adaptive FT fuzzy control strategy of non-affine systems, which is enlightening to us. The works 22,23 designed the desired FT controllers for two types of multi-agent systems to guarantee the consensus of output.…”

Section: Introductionmentioning

confidence: 99%

Observer‐based finite time adaptive fault tolerant control for nonaffine nonlinear systems with actuator faults and disturbances

Zhang

et al. 2020

This paper studies the problem of observer-based finite time adaptive fault tolerant control for nonaffine nonlinear systems with actuator faults and disturbances. Based on mean value theorem and convex combination method, a adaptive neural observer with virtual control coefficients is designed to estimate the systems states. Then, by using funnel Lyapunov function and backstepping method, a finite time control scheme is designed in the presence of disturbances and actuator faults. The stability analysis proves that tracking errors can converge to the prescribed performance bound in a finite time and all signals are uniformly ultimately bounded. Finally, simulation results verify efficiency of the studied approach.

“…According to a detailed literature review, 1‐41 main novelties and contributions of this article are clearly stated as follows:An observer‐based adaptive robust neural network leader‐following formation controller is proposed for N tractors each of which connected to n ‐trailers by taking both general forms of kinematic and dynamic models in the presence of model uncertainties which has not been addressed in the literature yet 1‐41 Compared with all previous works including References 18‐41, the proposed leader‐following formation controller ensures prescribed transient and steady‐state performance characteristics including convergence rate, undershoot/overshoot, and steady‐state error in order to provide a smooth transient response without unwanted large overshoots. The most of previous works that employ prescribed performance technique in the literature generally rely on the backstepping design procedure 1‐52 and, consequently, they suffer from an unwanted complexity and multiple control laws. However, the prescribe...…”

Section: Introductionmentioning

confidence: 99%

“…Meanwhile, the robot dynamics is not engaged in the design of the observer and unwanted peaking are also removed. Since system nonlinearities and prescribed performance nonlinear transformation impose a high degree of complexity and nonlinear terms in the open‐loop error dynamics, the traditional adaptive robust control methods could not simply compensate uncertain nonlinearities based on regression‐based adaptive update rules. Therefore, the employment of neural network and fuzzy approximation‐based control methods 43‐52 is strongly recommended to estimate and compensate unknown nonlinearities. In this article, an effective combination of a projection‐type radial basis function neural network (RBFNN) and an adaptive upper‐bounding saturation‐type robust controller is integrated into the controller design to compensate for unknown vehicle parameters, unmodeled dynamics, external disturbances, and NN approximation errors especially nonlinear‐in‐parameter ones.…”

Section: Introductionmentioning

confidence: 99%

Saturated observer‐based adaptive neural network leader‐following control of N tractors with n‐trailers with a guaranteed performance

Shojaei

Abdolmaleki

2020

This article addresses the leader‐following neural network adaptive observer‐based control of N tractors connected to n trailers with the prescribed performance specifications. To propose the controller, a change of coordinates and a nonlinear error transformation are used to transform the constrained error dynamics to a new second‐order Euler‐Lagrange unconstrained error dynamics which inherits all structural properties of ith vehicle dynamic model. By combining a projection‐type neural network and an adaptive robust technique, a novel leader‐following saturated output‐feedback controller is proposed to force that ith vehicle tracks a virtual leader trajectory with the prescribed transient and steady‐state characteristics while reducing the actuator saturation risk and compensating all unknown dynamic model parameters, external disturbances, unmolded dynamics, and NN approximation errors. A saturated velocity observer is heuristically proposed to obviate the requirement for the velocity measurements of ith vehicle without any unwanted peaking. A Lyapunov‐based stability analysis is utilized to prove that all the tracking and state observation errors are semi‐globally uniformly ultimately bounded (SGUUB) and they converge to small bounds including the origin with a prescribed performance. At the end, computer simulations will be shown to validate the efficacy of the proposed controller in practice.