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Dynamic event-triggered control design for a class of p-normal nonlinear time-delay systems with actuator failures

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This paper investigates the adaptive tracking control problem for a class of p-normal nonlinear time-delay systems with actuator failures via a dynamic event-triggered strategy. To deal with the time-varying delay, a modified Lyapunov-Krasovskii functional (LKF) with the system powers is constructed. By using radial basis function neural networks and hyperbolic tangent function, the residual terms caused by the derivative of LKF can be handled. Then, to achieve better communication efficiency, a dynamic event-triggered strategy is proposed, in which the threshold parameter is dynamically adjusted with the change of the virtual control signal. With the help of the proposed adaptive controller, all states of the closed-loop system are bounded and a favorable tracking performance is ensured. Finally, two examples are given to demonstrate the effectiveness of the proposed method. (C) 2022 Elsevier Inc. All rights reserved.

Adaptive neural networksActuator failuresEvent-triggeredp-Normal nonlinear systemsTime-varying delaysOUTPUT-FEEDBACKTRACKING CONTROLADAPTIVE STABILIZATIONFUZZY-SYSTEMSCOMPENSATIONSTATE

Li, Min、Guo, Jian、Xiang, Zhengrong

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Nanjing Univ Sci & Technol

2022

Applied mathematics and computation

Applied mathematics and computation

EISCI
ISSN:0096-3003
年,卷(期):2022.421
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