title: Combining genetic algorithms and Lyapunov-based adaptation for online design of fuzzy controllers.
abstract: 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.
authors.affiliation: Dipartimento di Elettrotecnica ed Elettronica, Politecnico di Bari, 70125 Bari, Italy.
authors.forename: Vincenzo
authors.initials: V
authors.lastname: Giordano
authors.forename: David
authors.initials: D
authors.lastname: Naso
authors.forename: Biagio
authors.initials: B
authors.lastname: Turchiano
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D005246:Feedback; D017143:Fuzzy Logic; D008957:Models, Genetic; D016571:Neural Networks, Computer; D009862:Online Systems; D010363:Pattern Recognition, Automated / Q000379:methods*