Event-triggered fractional-order fuzzy sliding mode control using online reinforcement learning for uncertain nonlinear systems: Practical validation

In this paper, a novel event-triggered control strategy is proposed for uncertain nonlinear systems by developing a fractional-order fuzzy sliding mode controller based on a fractional-order actor–critic network. The proposed approach offers several key features. First, a sigma-point Kalman filter is employed to accurately estimate unmeasured states. Second, a fractional-order sliding mode controller with an event-triggered mechanism is designed to achieve practical sliding mode control while preventing the Zeno phenomenon. Third, to reduce chattering in sliding mode control, a fractional-order actor–critic recurrent neural network is proposed, effectively approximating the switching control stage and enhancing system performance while reducing event triggers. The fractional-order actor–critic network incorporates fuzzy rules defined by a generalized Gaussian function with the Mittag-Leffler function, and a critic network approximates the value function, further enhancing performance. Parameter learning is guided by a fractional-order Gauss–Newton method. Stability analysis is performed using the Lyapunov method. Finally, the efficacy of the proposed method is demonstrated via experimental validation on a real inverted pendulum system.