Fixed-time concurrent learning-based robust approximate optimal control

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 H2 and H∞ performance indices. The effectiveness of the proposed FxT-CL-ACI control scheme is demonstrated by a numerical simulation and a hardware experiment on a UAV system. The results show that the FxT-CL-ACI control scheme can achieve robust optimal tracking control with fixed-time convergence and disturbance rejection, even in the presence of actuator saturation and uncertain system parameters.