Multi-Objective Genetic Algorithm Optimization based Fuzzy Control

A fuzzy controller for anti-swing and positioning control of an overhead traveling crane is proposed based on the SIRMs (Single Input Rule Modules) dynamically connected fuzzy inference model. The trolley position and velocity, the rope swing angle and angular velocity are selected as input items, and the trolley acceleration as output item. With a simple structure, the controller can autonomously adjust the influence of each input item. The control system is further proved to be asymptotically stable near destination. Multi-objective genetic algorithm optimization is successfully implemented to find the controller gains. Control simulation results show that the controller is robust to different rope lengths and has generalization ability for different initial positions. Compared with linear state feedback controller, the fuzzy controller can drive the crane to destination in short time with small swing angle.