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An adaptive particle swarm optimization algorithm for robust trajectory tracking of a class of under actuated system

An adaptive particle swarm optimization algorithm for robust trajectory tracking of a class of... This paper presents an adaptive particle swarm optimization (APSO) based LQR controller for optimal tuning of state feedback controller gains for a class of under actuated system (Inverted pendulum). Normally, the weights of LQR controller are chosen based on trial error approach to obtain the optimum controller gains, but it is often cumbersome tedious to tune the controller gains via trial error method. To address this problem, an intelligent approach employing adaptive PSO (APSO) for optimum tuning of LQR is proposed. In this approach, an adaptive inertia weight factor (AIWF), which adjusts the inertia weight according to the success rate of the particles, is employed to not only speed up the search process but also to increase the accuracy of the algorithm towards obtaining the optimum controller gain. The performance of the proposed approach is tested on a bench mark inverted pendulum system, the experimental results of APSO are compared with that of the conventional PSO GA. Experimental results prove that the proposed algorithm remarkably improves the convergence speed precision of PSO in obtaining the robust trajectory tracking of inverted pendulum. Key words: inverted pendulum, LQR controller, particle swarm optimization, genetic algorithm, adaptive inertia weight factor, state feedback http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Archives of Electrical Engineering de Gruyter

An adaptive particle swarm optimization algorithm for robust trajectory tracking of a class of under actuated system

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Publisher
de Gruyter
Copyright
Copyright © 2014 by the
ISSN
2300-2506
eISSN
2300-2506
DOI
10.2478/aee-2014-0026
Publisher site
See Article on Publisher Site

Abstract

This paper presents an adaptive particle swarm optimization (APSO) based LQR controller for optimal tuning of state feedback controller gains for a class of under actuated system (Inverted pendulum). Normally, the weights of LQR controller are chosen based on trial error approach to obtain the optimum controller gains, but it is often cumbersome tedious to tune the controller gains via trial error method. To address this problem, an intelligent approach employing adaptive PSO (APSO) for optimum tuning of LQR is proposed. In this approach, an adaptive inertia weight factor (AIWF), which adjusts the inertia weight according to the success rate of the particles, is employed to not only speed up the search process but also to increase the accuracy of the algorithm towards obtaining the optimum controller gain. The performance of the proposed approach is tested on a bench mark inverted pendulum system, the experimental results of APSO are compared with that of the conventional PSO GA. Experimental results prove that the proposed algorithm remarkably improves the convergence speed precision of PSO in obtaining the robust trajectory tracking of inverted pendulum. Key words: inverted pendulum, LQR controller, particle swarm optimization, genetic algorithm, adaptive inertia weight factor, state feedback

Journal

Archives of Electrical Engineeringde Gruyter

Published: Sep 1, 2014

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