Adaptive neural tracking control for non‐affine switched systems with multiple time delays and actuator faults

In this paper, the problem of fault estimation based on intermediate variable-based adaptive observer for a class of nonlinear systems with Lipschitzian nonlinearities and faults is studied. An adaptive observer based on lower triangular matrix transformation is proposed, which comprise the output derivatives information so as to enhance the accuracy and speed of fault estimation effectually. The designed observer can be simplified to the lower triangular estimation observer, and is superior to the comprehensive estimation observer in the related literature. In addition, a more general error dynamics is derived based on the improved adaptive estimation observer, which makes it less conservative to obtain stability conditions. The uniformly ultimately bounded stability conditions of the estimated error dynamics are given by linear matrix inequalities (LMIs). Numerical examples are given to illustrate the effectiveness and merits of the proposed method.

“…On the other hand, most practical control systems are nonlinear in essence 19‐23 . Thus, the nonlinear characteristics can not be overlooked in fault estimation.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intermediate variable‐based adaptive fault estimation for a class of Lipschitz nonlinear systems

Han

Huang

2023

“…In References 34 and 35, two different finite‐time output feedback control methods were designed with a state observer. Compared with the infinite‐time control strategy, 36 the finite‐time control method can make the closed‐loop system converge faster. From the point of convergence time, the control algorithm with finite‐time is a time optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

Observer‐based finite‐time adaptive fault‐tolerant control for nonlinear system with input delay and prescribed performance

Liu

2023

Summary This paper focuses on the issue of the adaptive fuzzy fault‐tolerant control for a type of nonlinear system with input delay and disturbances. Fuzzy logic systems (FLSs) are used to estimate the unknown nonlinear quantities of the system. The Pade approximation is applied to counteract the negative effects of input delay on the system. The prescribed performance control based on error transformation helps to guarantee the transient performance of system errors. A fuzzy state observer is established to estimate the unmeasurable states of the system. Then, in presence of disturbances and actuator faults, a finite‐time control scheme is designed using the Lyapunov function and the backstepping technique. Based on Lyapunov stability, it is proved that the tracking error can converge to a specified range in a finite time, and all signals in the system are bounded in the controller design. In the end, the simulations of a second‐order system and a multi‐area interconnected power system certify the feasibility and effectiveness of the control strategy.

“…The authors in Reference 24 propose the robust fault estimation and fault‐tolerant control (FTC) method of Takagi‐Sugeno fuzzy systems, where the compensation techniques are utilized to eliminate the effects of actuator fault and sensor fault on the system performance. Some adaptive control approaches are developed for nonlinear systems with actuator faults, for example, References 25,26. Nevertheless, there are fewer results on NN consensus control issues for nonlinear MASs with actuator faults based on event‐triggered mechanisms, which is one reason motivating our research.…”

Section: Introductionmentioning

confidence: 99%

Event‐triggered consensus secure control for nonlinear MASs under mixed sensor attacks and actuator faults

Chen,

Li,

Tong

2023

SummaryThis article investigates the adaptive neural network (NN) output‐feedback event‐triggered consensus secure control problem for a class of nonlinear multi‐agent systems (MASs) under mixed sensor attacks and actuator faults. Since the considered nonlinear MASs contain unknown nonlinear dynamics, the NNs are first adopted to model unknown agents. Then, a nove NN learning secure state observer is proposed to estimate the sensor attacks and unmeasured states. To reduce unnecessary updating times of the actuator, an event‐triggered mechanism is constructed. By using the backstepping control design technique and the design NN state observer, a NN adaptive output‐feedback event‐triggered consensus secure control scheme is formulated. It is proved that the developed consensus secure control scheme can guarantee the controlled nonlinear MASs are stable and consensus tracking errors converge even under mixed sensor attacks and actuator faults. Simulation and comparative results illustrate the effectiveness of the proposed scheme.