A novel practically designated-time fuzzy optimal control via reinforcement learning for stochastic constrained nonlinear systems - Qiu, Lifang; Zhao, Junsheng; Sun, Zong-Yao; Mu, Chaoxu (Neurocomputing (Amsterdam), 2025)
This paper investigates the practically designated time fuzzy optimal control problem for stochastic constrained nonlinear systems with input delay, using reinforcement learning (RL) algorithms. For the uncertain stochastic nonlinear system, a new practically designated time control scheme is proposed and proved to be effective. Meanwhile, a fourth-order asymmetric time-varying barrier Lyapunov function (BLF) is leveraged to mitigate the severe uncertainty caused by the constraints, and Padé approximation and intermediate variables are deployed to counteract the impact of input delay. Additionally, the optimal RL-based instruction filtering control scheme is implemented by exploiting the designated time stability, which not only achieves a prescribed convergence time based on dynamic event triggering, but also ensures that the necessary state constraints are satisfied. The bounded command filtering design avoids the need to prove the continuity of the higher order derivatives of the virtual control signals, thus significantly reducing the complexity of the stability analysis associated with the designated-time convergence of the closed-loop signals. In the optimal control design, the controller put forward ensures that all signals within the closed-loop system remain bounded in probability and the tracking error meets the performance requirements. Finally, the simulation results of two examples verify the effectiveness and applicability.