Event-Triggered Optimal Tracking Control for Strict-Feedback Nonlinear Systems With Non-Affine Nonlinear Faults

TL;DR

This paper develops an event-triggered optimal control scheme using reinforcement learning for strict-feedback nonlinear systems with complex faults, reducing communication needs and enhancing fault tolerance.

Contribution

It introduces a simplified RL-based control framework with a fault-tolerant mechanism and event-triggered design, improving upon existing methods for nonlinear systems with faults.

Findings

The proposed control scheme achieves optimal performance in simulations.

The event-triggered mechanism reduces communication resource usage.

The method effectively handles non-affine nonlinear faults.

Abstract

This article studies the control ideas of the optimal backstepping technique, proposing an event-triggered optimal tracking control scheme for a class of strict-feedback nonlinear systems with non-affine and nonlinear faults. A simplified identifier-critic-actor framework is employed in the reinforcement learning algorithm to achieve optimal control. The identifier estimates the unknown dynamic functions, the critic evaluates the system performance, and the actor implements control actions, enabling modeling and control of anonymous systems for achieving optimal control performance. In this paper, a simplified reinforcement learning algorithm is designed by deriving update rules from the negative gradient of a simple positive function related to the Hamilton-Jacobi-Bellman equation, and it also releases the stringent persistent excitation condition. Then, a fault-tolerant control method is developed by applying filtered signals for controller design. Additionally, to address communication resource reduction, an event-triggered mechanism is employed for designing the actual controller. Finally, the proposed scheme's feasibility is validated through theoretical analysis and simulation.