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Design of Fuzzy Controller for PUMA 560 Robot Arm Using Improved Bacterial Foraging Optimization Algorithm

Trial and error method can be used to find a suitable design of a fuzzy controller. Generally, the design of fuzzy controller involves determination of the fuzzy rules, Membership Functions (MFs) and scaling factors. An optimization algorithm facilitates the design process and finds an optimal design to achieve a desired performance. This paper presents an Improved Bacterial Foraging Optimization Algorithm (IBFOA) to design a fuzzy controller for tracking control of a PUMA 560 robot arm driven by permanent magnet DC motors. We use efficiently the IBFOA to form the rule base and MFs. To show the improvement of proposed algorithm, the IBFOA is compared with Bacterial Foraging Optimization Algorithm (BFOA) and Particle Swarm Optimization (PSO) algorithm. Performance of the controller in the joint space and in the Cartesian space is evaluated. Simulation results show superiority of the IBFOA to the BFOA and PSO algorithm.