Reinforcement learning-based predefined-time secure control for fractional-order nonlinear systems with sensor and actuator attacks.

Abstract

A secure control problem is investigated for constrained fractional-order nonlinear systems subject to both sensor and actuator attacks. To handle the effects induced by the dual attacks, a nonlinear mapping-based coordinate transformation is elaborately introduced to convert the original state-constrained system into an unconstrained form. Furthermore, by integrating reinforcement learning with predefined-time stability analysis, an adaptive control strategy is developed to achieve predefined-time convergence. The proposed method enhances the transient response of the system while accelerating the convergence of both the tracking error and the policy learning process. A novel reset-based self-triggered mechanism is proposed. On the one hand, the developed mechanism reduces communication load and eliminates the need for continuous state monitoring. On the other hand, the sampling intervals can be adaptively adjusted according to the system states, thereby further alleviating the computational burden. Under the developed control strategy, the closed-loop system is guaranteed to achieve semi-global practical predefined-time stability, and all signals converge to a bounded neighborhood of the origin within the predefined time.