Zero-order TSK-type fuzzy system learning using a two-phase swarm intelligence algorithm
Fuzzy Sets and Systems
A self-generating fuzzy system with ant and particle swarm cooperative optimization
Expert Systems with Applications: An International Journal
IEEE Transactions on Fuzzy Systems
Robust L2-gain compensative control for direct-adaptive fuzzy-control-system design
IEEE Transactions on Fuzzy Systems
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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