• DocumentCode
    3086234
  • Title

    Adaptive Tuning of a PID Speed Controller for DC Motor Drives Using Multi-objective Particle Swarm Optimization

  • Author

    El-Gammal, Adel A A ; El-Samahy, Adel A.

  • Author_Institution
    Energy Res. Centre, Univ. of Trinidad & Tobago UTT, Trinidad
  • fYear
    2009
  • fDate
    25-27 March 2009
  • Firstpage
    398
  • Lastpage
    404
  • Abstract
    In this paper, a control scheme based on Multi-Objective Particle Swarm optimization MOPSO is proposed, which is able to tune the PID controller parameters simultaneously in order to find the set of trade-off optimal solutions that is called Pareto-set optimization solution of the conflicting objective functions for DC motor drive system. Multi Objective Particle Swarm Optimization MOPSO is implemented to tackle a number of conflicting goals that define the optimality problem. This paper deals with five conflicting objective functions. These conflicting functions are: 1. Minimize the maximum overshoot, 2. Minimize the rise time, 3. Minimize speed tracking error, 4. Minimize the steady state error, and 5. Minimize the settling time.
  • Keywords
    DC motor drives; Pareto optimisation; adaptive control; angular velocity control; optimal control; particle swarm optimisation; three-term control; DC motor drive system; PID speed controller; Pareto-set optimization solution; adaptive tuning; multiobjective particle swarm optimization; speed tracking error minimisation; steady state error minimisation; trade-off optimal solution; Adaptive control; DC motors; Genetic algorithms; Optimal control; PD control; Particle swarm optimization; Programmable control; Proportional control; Three-term control; Torque control; DC Motor Drive; Maximum Overshoot; Multi-Objective Particle Swarm Optimization PSO; Rise Time; Settling Time; Steady State Error; Tuning PID;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Modelling and Simulation, 2009. UKSIM '09. 11th International Conference on
  • Conference_Location
    Cambridge
  • Print_ISBN
    978-1-4244-3771-9
  • Electronic_ISBN
    978-0-7695-3593-7
  • Type

    conf

  • DOI
    10.1109/UKSIM.2009.60
  • Filename
    4809798