Combining Genetic Algorithms and Lyapunov-Based Adaptation for Online Design of Fuzzy Controllers

• Published in: IEEE transactions on systems, man and cybernetics. Part B, Cybernetics Vol. 36; no. 5; pp. 1118 - 1127
• Main Authors: Giordano, V., Naso, D., Turchiano, B.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.2006
• Subjects: Adaptation; Adaptive control; Adaptive fuzzy control (AFC); Algorithm design and analysis; Algorithms; Artificial Intelligence; Automatic control; Automatic frequency control; Computer Simulation; Cybernetics; Design engineering; Feedback; Fuzzy control; Fuzzy Logic; Genetic algorithms; genetic algorithms (GAs); Mathematical models; Models, Genetic; Neural Networks (Computer); Online Systems; Optimization methods; Pattern Recognition, Automated - methods; Performance analysis; Programmable control; Stability; Tuning
• ISSN: 1083-4419; 1941-0492
• DOI: 10.1109/TSMCB.2006.873187

This paper proposes a hybrid approach for the design of adaptive fuzzy controllers (FCs) in which two learning algorithms with different characteristics are merged together to obtain an improved method. The approach combines a genetic algorithm (GA), devised to optimize all the configuration parameters of the FC, including the number of membership functions and rules, and a Lyapunov-based adaptation law performing a local tuning of the output singletons of the controller, and guaranteeing the stability of each new controller investigated by the GA. The effectiveness of the proposed method is confirmed using both numerical simulations on a known case study and experiments on a nonlinear hardware benchmark