Multi-objective biogeography-based optimization technique for tuning PUMA 560’S controller

Abstract

Biogeography-based optimization (BBO) is one of the recently developed population-based algorithms which has shown impressive performance over evolutionary algorithms. BBO is based on the study of geographical distribution of biological organisms over space and time. In this paper, non-dominated sorting BBO (NSBBO) is proposed to tune proportionalderivative control system for the six degree arm manipulator PUMA 560. The BBO algorithm is based on mathematical models of biogeography, which describe the migration of species between habitats. To tune six PD controller of a PUMA 560 arm manipulator, we need to minimize simultaneously six position errors so there exists a multi-objective optimization problem. The NSBBO algorithm searches for the controller gains, so that integral absolute error in joint space is minimized. Results obtained show the effectiveness of the algorithm to optimize the controlling parameters and minimizing the error.