An experimental adaptive fuzzy controller for differential games

In this paper a reinforcement fuzzy learning scheme for robots playing a differential game is derived. A differential game may be considered a Markov decision process in continuous time, with continuous states and actions. The robots receive reinforcements from the environment after they take an act...

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Bibliographic Details
Published in2009 IEEE International Conference on Systems, Man and Cybernetics pp. 3017 - 3023
Main Authors Givigi, S.N., Schwartz, H.M., Xiaosong Lu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2009
Subjects
Adaptive control
Control systems
Decision making
Differential games
Fuzzy control
Fuzzy systems
Game theory
intelligent systems
Learning
Microcontrollers
Programmable control
pursuer-evader games
Robot sensing systems
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Summary:In this paper a reinforcement fuzzy learning scheme for robots playing a differential game is derived. A differential game may be considered a Markov decision process in continuous time, with continuous states and actions. The robots receive reinforcements from the environment after they take an action; and this reinforcement is then used to adapt a fuzzy controller that stores the experience accumulated by the robot. Every calculation is done in a physical system based on microcontrollers to control the movement of the robots and sensors to measure their position and angle in a 2D-plane. Filters are also implemented to approximate the derivatives of the states. Experiments of a pursuer-evader game are provided in order to show the feasibility of the technique. It should be noted, though, that the technique may also be used in a multi-game environment.
ISBN:9781424427932
1424427932
ISSN:1062-922X
2577-1655
DOI:10.1109/ICSMC.2009.5345932