Self-learning fuzzy logic controllers for pursuit–evasion differential games

✍ Scribed by Sameh F. Desouky; Howard M. Schwartz

• Publisher: Elsevier Science
• Year: 2011
• Tongue: English
• Weight: 889 KB
• Volume: 59
• Category: Article
• ISSN: 0921-8890
• DOI: 10.1016/j.robot.2010.09.006

✦ Synopsis

This paper addresses the problem of tuning the input and the output parameters of a fuzzy logic controller. The system learns autonomously without supervision or a priori training data. Two novel techniques are proposed. The first technique combines Q(λ)-learning with function approximation (fuzzy inference system) to tune the parameters of a fuzzy logic controller operating in continuous state and action spaces. The second technique combines Q(λ)-learning with genetic algorithms to tune the parameters of a fuzzy logic controller in the discrete state and action spaces. The proposed techniques are applied to different pursuit-evasion differential games. The proposed techniques are compared with the classical control strategy, Q(λ)-learning only, reward-based genetic algorithms learning, and with the technique proposed by Dai et al. (2005) [19] in which a neural network is used as a function approximation for Q-learning. Computer simulations show the usefulness of the proposed techniques.