4.7 Article

Design of intelligent PID/PIλDμ speed controller for chopper fed DC motor drive using opposition based artificial bee colony algorithm

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.engappai.2013.12.009

关键词

DC motor drive; Fractional order (FO) PID; Iso-damping characteristics; Artificial bee colony; Opposition search; Global optimization

资金

  1. IT4 Innovations Center of Excellence project by operational program Research and Development for Innovations [CZ.1.05/1.1.00/02.0070]
  2. European Union
  3. state budget of the Czech Republic, EU

向作者/读者索取更多资源

This paper deals with the design, implementation and analysis of an integer order (10) and fractional order (FO) based Proportional Integral Derivative (PID) controller, for speed regulation in a chopper fed Direct Current (DC) motor drive. The interdependent parameters of PID and FOPID controllers are designed in both time and frequency domain. In both domains, designs of controllers are formulated as a single objective optimization problem based on time indices integrals and frequency domain rules. Time domain based design of controllers focus mainly on minimization of indices like rise time, settling time etc. On the other hand frequency domain based design of controllers focuses on achieving iso-damping characteristics, which tries to meet user specified gain crossover frequency or phase margin while also maintaining constant overshoot for wide range of motor gain apart from maintaining optimum time indices. A newly evolved artificial bee colony (ABC) algorithm enhanced with opposition search has been used to perform the optimization task. This type of multiple designs enables users to choose the controller based on their requirement. A comparative study has been made to highlight the advantage of using fractional order controller over conventional integer order PID control scheme for speed regulation. To illustrate the efficacy of the opposition based ABC, we also compared the performance with the conventional ABC, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) methods. Computer simulations and extensive analysis over results obtained shows the effectiveness of the proposed approach. (C) 2013 Elsevier Ltd. All rights reserved.

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