Fixed-time concurrent learning-based robust approximate optimal control
摘要
In this paper, we investigate a fixed-time concurrent learning-based actor-critic-identifier (FxT-CL-ACI) control scheme for approximating the optimal tracking controller and identifying uncertain system parameters online. The proposed FxT-CL-ACI control scheme is applied to solve the robust optimal tracking control problem for uncertain nonlinear systems with disturbances and actuator saturation. The interaction between the leader and follower in the Stackelberg game is modeled to achieve robust optimal tracking control with sequential optimization of