An iterative is an element of-optimal control scheme for a class of discrete-time nonlinear systems with unfixed initial state
In this paper, a finite horizon iterative adaptive dynamic programming (ADP) algorithm is proposed to solve the optimal control problem for a class of discrete-time nonlinear systems with unfixed initial state. A new is an element of-optimal control algorithm based on the iterative ADP approach is proposed that makes the performance index function iteratively converge to the greatest lower bound of all performance indices within an error is an element of in finite time. The convergence analysis of the proposed ADP algorithm in terms of performance index function and control policy is conducted. The optimal number of control steps can also be obtained by the proposed is an element of-optimal control algorithm for the unfixed initial state. Neural networks are used to approximate the performance index function, and compute the optimal control policy, respectively, for facilitating the implementation of the is an element of-optimal control algorithm. Finally, a simulation example is given to show the effectiveness of the proposed method. (C) 2012 Elsevier Ltd. All rights reserved.
is an element of-optimal control finite horizon adaptive dynamic programming neural networks approximate dynamic programming