A data-based neural policy learning strategy towards robust tracking control design for uncertain dynamic systems

In this paper, a data-based neural policy learning method is established to solve the robust tracking control problem of a class of continuous-time systems which have two kinds of uncertainties at the same time. First, the robust trajectory tracking is achieved by controlling the tracking error to zero. The specific implementation strategy is to construct an augmented system including the tracking error and then transform the robust tracking control problem into an optimal control problem by selecting a suitable cost function. Then, a neural network identifier is built to reconstruct the unknown dynamics and a policy iteration algorithm is adopted by using a critic neural network. In this way, the Hamilton–Jacobi–Bellman equation can be solved. Through this learning algorithm, the approximate optimal control policy is obtained and the solution of the robust tracking control problem can be derived. Finally, two simulation examples are proposed to verify the effectiveness of the developed method.