Trial and error method can be used to find a suitable design of a fuzzy controller. However, there are many options including fuzzy rules, Membership Functions (MFs) and scaling factors to achieve a desired performanc...Trial and error method can be used to find a suitable design of a fuzzy controller. However, there are many options including fuzzy rules, Membership Functions (MFs) and scaling factors to achieve a desired performance. An optimiza-tion algorithm facilitates this process and finds an optimal design to provide a desired performance. This paper presents a novel application of the Bacterial Foraging Optimization algorithm (BFO) to design a fuzzy controller for tracking control of a robot manipulator driven by permanent magnet DC motors. We use efficiently the BFO algorithm to form the rule base and MFs. The BFO algorithm is compared with a Particle Swarm Optimization algorithm (PSO). Performance of the controller in the joint space and in the Cartesian space is evaluated. Simulation results show superiority of the BFO algorithm to the PSO algorithm.展开更多
文摘Trial and error method can be used to find a suitable design of a fuzzy controller. However, there are many options including fuzzy rules, Membership Functions (MFs) and scaling factors to achieve a desired performance. An optimiza-tion algorithm facilitates this process and finds an optimal design to provide a desired performance. This paper presents a novel application of the Bacterial Foraging Optimization algorithm (BFO) to design a fuzzy controller for tracking control of a robot manipulator driven by permanent magnet DC motors. We use efficiently the BFO algorithm to form the rule base and MFs. The BFO algorithm is compared with a Particle Swarm Optimization algorithm (PSO). Performance of the controller in the joint space and in the Cartesian space is evaluated. Simulation results show superiority of the BFO algorithm to the PSO algorithm.
