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This paper will present an approximate/adaptive dynamic programming(ADP) algorithm,that uses the idea of integral reinforcement learning(IRL),to determine online the Nash equilibrium solution for the two-player zerosum differential game with linear dynamics and infinite horizon quadratic cost.The algorithm is built around an iterative method that has been developed in the control engineering community for solving the continuous-time game algebraic Riccati equation(CT-GARE),which underlies the game problem.We here show how the ADP techniques will enhance the capabilities of the offline method allowing an online solution without the requirement of complete knowledge of the system dynamics.The feasibility of the ADP scheme is demonstrated in simulation for a power system control application.The adaptation goal is the best control policy that will face in an optimal manner the highest load disturbance.  相似文献
2.
A new online iterative algorithm for solving the H control problem of continuous‐time Markovian jumping linear systems is developed. For comparison, an available offline iterative algorithm for converging to the solution of the H control problem is firstly proposed. Based on the offline iterative algorithm and a new online decoupling technique named subsystems transformation method, a set of linear subsystems, which implementation in parallel, are obtained. By means of the adaptive dynamic programming technique, the two‐player zero‐sum game with the coupled game algebraic Riccati equation is solved online thereafter. The convergence of the novel policy iteration algorithm is also established. At last, simulation results have illustrated the effectiveness and applicability of these two methods. Copyright © 2016 John Wiley & Sons, Ltd.  相似文献
3.
针对具有未知动态线性系统的二人零和博弈问题,本文提出了一种新的基于单环迭代方法的在线学习方案.为保证单环迭代方法的收敛性,给出了一种新的分析方法.在系统内部矩阵A,控制输入矩阵B以及干扰输入矩阵D均未知的情况下,通过在线迭代策略,同步得到了博弈代数黎卡提方程的近似解,以及控制和干扰策略.仿真结果表明了所提方法的有效性.  相似文献
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