Improved bacterial foraging strategy for controller optimization applied to robotic manipulator system

Abstract

During the course of evolution, colonies of ants, bees, wasps, bacteria and termites have developed sophisticated behavior, intricate communication capabilities, decentralized colony control, group foraging strategies and a high degree of worker cooperation when tackling tasks. Utilizing these capabilities, any bio-inspired optimization techniques using analogy of swarming principles and social behavior in nature — swarm intelligence — have been adopted to solve a variety of engineering and robotics problems. In this paper, new approaches of bacterial colony optimization method with variable speed based on uniform, Gaussian, and Cauchy probability distribution were tested. Bacterial colony, a swarm intelligence methodology, is evaluated for a problem of optimization of a PID (proportional-integral-derivative) multivariable controller. The simulation results are compared with classical bacterial colony approach with constant velocity for a case study of control of a robotic manipulator system with two degree of freedom.