FINITE-HORIZON ε-OPTIMAL TRACKING CONTROL OF DISCRETE-TIME LINEAR SYSTEMS USING ITERATIVE APPROXIMATE DYNAMIC PROGRAMMING

In this paper, we propose a finite-horizon neuro-optimal tracking control strategy for a class of discrete-time linear systems. In applying the iterative approximate dynamic programming (ADP) algorithm to determine the optimal tracking control law for linear systems, we need finite iterations to obtain the result in practical applications, instead of infinite iterations. An epsilon-error bound is introduced into the ADP algorithm to determine the number of iteration steps. The approximation optimal tracking control law will approach the solution of the Hamilton-Jacobi-Bellman (HJB) equation through a self-adaptive iteration within the given value of epsilon-error bound. epsilon error bound is used to stop the iteration process. So, we can obtain the epsilon-approximation tracking control law in a finite number of iterations. Nevertheless, different epsilon will produce different control performances. Furthermore, we will find an optimal epsilon error bound, which can obtain optimal performance of the ADP algorithm on the basis of the controlled system tracking the desired trajectory. One example is included to complete the ADP algorithm under different error bounds. From the simulation results, we can find the optimal epsilon error bound. Finally, the simulation validates the efficiency of the proposed algorithm.