Finite-Horizonε-Optimal Tracking Control of Discrete-Time Linear Systems Using Iterative Approximate Dynamic Programming

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