Optimal Tracking Control for a Class of Unknown Discrete-time Systems with Actuator Saturation via Data-based ADP Algorithm

A novel optimal tracking control method for a class of discrete-time systems with actuator saturation and unknown dynamics is proposed in this paper. The scheme is based on the iterative adaptive dynamic programming (ADP) algorithm. In order to implement the control scheme, a data-based identifier is first constructed for the unknown system dynamics. By introducing the M network, the explicit formula of the steady control is achieved. In order to eliminate the effect of the actuator saturation, a nonquadratic performance functional is presented, and then an iterative ADP algorithm is established to achieve the optimal tracking control solution with convergence analysis. For implementing the optimal control method, neural networks are used to establish the data-based identifier, compute the performance index functional, approximate the optimal control policy and solve the steady control, respectively. Simulation example is provided to verify the effectiveness of the presented optimal tracking control scheme.