Title: Particle swarm optimisation (PSO)-based controller strategies for energy efficient PMDC motor drives

Authors: Adel M. Sharaf, Adel A.A. El-Gammal

Addresses: Centre for Energy Systems, University of Trinidad and Tobago, UTT, P.O. Box 957, Point Lisas Campus Esperanza Road, Brechin Castle, Couva, Trinidad and Tobago. ' Centre for Energy Systems, University of Trinidad and Tobago, UTT, P.O. Box 957, Point Lisas Campus Esperanza Road, Brechin Castle, Couva, Trinidad and Tobago

Abstract: The paper presents the application of particle swarm optimisation (PSO) technique for online tuning of error-driven self-adjusting multi-loop dynamic speed regulators for large industrial PMDC motor drives. Novel multi-loop dynamic error regulator include Tan-sigmoid controller, multi-zone controller and incremental self-regulating controllers are developed by the first author using multi-objective particle swarm optimisation (MOPSO) for high performance efficient PMDC motor drives. The three novel dynamic efficient control schemes utilise speed, current, dynamic momentum excursion error and limited current ripple errors as main inputs to vary the firing delay angle α of the 6-pulse controlled thyristor rectifier. The tuning selection criterion for optimal control gains is based on effective robust dynamic tracking of selected speed reference trajectories. The optimisation process is based on minimising the system control total error, the steady state error, settling time, rising time, and maximum overshoot.

Keywords: PMDC motor drives; multi-objective PSO; particle swarm optimisation; MOPSO; tri loop; Tan-sigmoid control; multi-zone control; minimum settling time; rise time; maximum overshoot; energy efficiency; online tuning; dynamic speed regulators; thyristor rectifiers; optimal control; dynamic tracking; speed reference trajectories; self-regulating control.

DOI: 10.1504/IJRIS.2010.036864

International Journal of Reasoning-based Intelligent Systems, 2010 Vol.2 No.3/4, pp.184 - 201

Published online: 12 Nov 2010 *

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