Neural network-based event-triggered finite-time control of uncertain nonlinear systems with full-state constraints and actuator failures

摘要

This work investigates a Neural Network- (NN) based event-triggered finite-time control for a class of uncertain nonlinear systems with full-state constraints and actuator failures. Firstly, practical engineering systems are widely affected by state constraints, actuator failures, etc. Violation of constraints would considerably affect the system's performance. Especially, it is challenging to guarantee system stability when the system is subject to these constraints, which are uncertain in time, form, and value. To address these issues, an intelligent control method is constructed based on logarithm-type Barrier Lyapunov Functions and Backstepping technology. Furthermore, consider that bandwidth resources are limited in practical engineering systems, and the excellent convergence performance of the system is the actual industrial requirement. Thus, a NN-based event-triggered finite-time control strategy is established to save bandwidth and achieve finite-time convergence of the system. Finally, theoretical analysis and simulation examples are presented to demonstrate the effectiveness of the proposed control scheme.

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