Data-driven optimal tracking control for SMA actuated systems with prescribed performance via reinforcement learning

This article addresses the data-driven performance-prescribed continuous-time optimal tracking control problem for the shape memory alloy (SMA) actuated systems with completely unknown model knowledge. Firstly, the error constraint problem is transformed into the unconstrained error tracking problem by the prescribed performance control (PPC) method. Then, the optimal tracking control problem (OTCP) is pre-processed by establishing an unconstrained augmented system. Furthermore, the Hamilton-Jacobi-Bellman equation (HJBE) of the OTCP is solved iteratively by utilizing reinforcement learning (RL) without the SMA actuator model information requirement. The value function and execution strategy of the RL are approximated by two neural networks, acting as actor and critic, respectively, and the actor-critic based RL is implemented using the least-squares method. In addition, the Lyapunov method ensures the stability of the closed-loop system actuated by SMA, as well as the user-specified error constraints concerning the error convergence rate, overshoot, and tracking accuracy. Finally, the experimental results and comparisons illustrate the validity of the proposed method.