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DragunaVRABIE,FrankLEWIS
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(United Technologies Research Center;Automation and Robotics Research Institute, University of Texas at Arlington)
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Received:July 14, 2010Revised:May 23, 2011
基金项目:This work was supported by the National Science Foundation (No.ECCS-0801330), and the Army Research Office (No.W91NF-05-1-0314).
Adaptive dynamic programming for online solution of a zero-sum differential game
Draguna VRABIE,Frank LEWIS
(United Technologies Research Center;Automation and Robotics Research Institute, University of Texas at Arlington)
Abstract:
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.
Key words:  Approximate/Adaptive dynamic programming  Game algebraic Riccati equation  Zero-sum differential game  Nash equilibrium