Abstract
The paper presents using the shuffled frog leaping algorithm (SFLA) to optimally tune parameters of a fuzzy logic controller stabilizing a ball and beam system at its equilibrium position. The SFLA is a meta-heuristic search method inspired from the memetic evolution of a group of frogs when seeking for food. It consists of a frog leaping rule for local search and a memetic shuffling rule for global information exchange. In this study, the rule base of the fuzzy controller is brought by expert experience, and the parameters of the controller, i.e. the membership function parameters and scaling gains, are optimally tuned by the SFLA such that a predefined criterion is minimized. Simulation results show that the designed fuzzy controller is able to balance the ball and beam system around its equilibrium state and the performance of the fuzzy controller is better than that of the well-known LQR controller.
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