Robust Controller Design Dased on Policy Iteration

An approach is given, which tries to design a robust controller using the plicy iteration algorithm for the nonlinear systems due to uncertainties. The controller design problem is divided into two steps. The first step is introducting the uncertainties into the cost function which changes the robust difficult point into the optimal problem. Second, to obtain the control strategy for the optimization of the new cosst function, the policy iteration technique is utilized to deal with the Hamilton-Jacobi-Bellman (HJB) equation. In the implementation of the given approach, the cost function is obtained approximately by constructing the critic neural networks while the optimal control strategy is derived directly based on system dynamic the critic neural network. Furthermore, the convergence for the uncertain system and the equivalence of the solution to the two kind of questions is proved. Final, the numerical results illustrated the correct of the presented method.