Genetic algorithm-optimized fuzzy controller reduces robotic arm simulation error by 89%

Abdullah Abushokor and colleagues published research in the International Journal of Automation and Control, proposing a genetic algorithm-optimized fuzzy controller (GA-FLC) for trajectory tracking control of a three-degree-of-freedom robotic arm. The team states that simulation results show that, compared with traditional feedback control, GA-FLC reduces the average root mean square error by 89% and improves accuracy and disturbance rejection in simulation. This conclusion currently comes only from simulation and has not yet been validated on a real robotic arm. The research team says the next step will be hardware implementation and real-time testing, conducting proof of concept in experimental industrial scenarios.

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