Parameter Identification of Robot Manipulators: A Heuristic Particle Swarm Search Approach |
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| Authors: | Danping Yan Yongzhong Lu David Levy |
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| Institution: | 1. College of Public Administration, Huazhong University of Science and Technology, Wuhan, Hubei, China.; 2. Non-traditional Security Center of Huazhong University of Science and Technology, Wuhan, Hubei, China.; 3. School of Software Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China.; 4. Faculty of Engineering and Information Technologies, University of Sydney, Sydney, New South Wales, Australia.; Beihang University, CHINA, |
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| Abstract: | Parameter identification of robot manipulators is an indispensable pivotal process of achieving accurate dynamic robot models. Since these kinetic models are highly nonlinear, it is not easy to tackle the matter of identifying their parameters. To solve the difficulty effectively, we herewith present an intelligent approach, namely, a heuristic particle swarm optimization (PSO) algorithm, which we call the elitist learning strategy (ELS) and proportional integral derivative (PID) controller hybridized PSO approach (ELPIDSO). A specified PID controller is designed to improve particles’ local and global positions information together with ELS. Parameter identification of robot manipulators is conducted for performance evaluation of our proposed approach. Experimental results clearly indicate the following findings: Compared with standard PSO (SPSO) algorithm, ELPIDSO has improved a lot. It not only enhances the diversity of the swarm, but also features better search effectiveness and efficiency in solving practical optimization problems. Accordingly, ELPIDSO is superior to least squares (LS) method, genetic algorithm (GA), and SPSO algorithm in estimating the parameters of the kinetic models of robot manipulators. |
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