• DocumentCode
    3582068
  • Title

    GA-PID controller for position control of inverted pendulum

  • Author

    Yusuf, Lukman A. ; Magaji, Nuraddeen

  • Author_Institution
    Dept. of Electr. Eng., Bayero Univ., Kano, Nigeria
  • fYear
    2014
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    Stability is very necessary in control system and it becomes more difficult to achieve for a nonlinear system which inverted pendulum is an example. Most of the controllers available suffer from problems such as difficult in tuning process, sluggishness in response time, quick and global convergence etc. This paper considered Proportional-Integra-Derivative optimized with Genetic Algorithm (GA-PID) Controller on Inverted pendulum for the control of the angle position. Conventional PID controller was used to validate the proposed controller. A MATLAB script for genetic algorithm was written with the aim of obtaining optimum PID parameters that would keep the pendulum angle at equilibrium (i.e. returns the pendulum to a desire point as quick as possible) by minimizing an objective function (Integral time absolute error ITAE). On the other hand, a convention PID controller was designed using MATLAB/Simulink environment; the PID´s gains were manually tuned until an optimum response is achieved. The results obtained in both schemes shows that GA-PID showed superiority in all the performance indices used in evaluating the two controller schemes and therefore can serves as a valuable controller for the system.
  • Keywords
    genetic algorithms; nonlinear control systems; optimal control; pendulums; performance index; position control; stability; three-term control; GA-PID controller; ITAE; MATLAB script; MATLAB/Simulink environment; PID controller design; angle position control; control system; genetic algorithm; integral time absolute error; inverted pendulum; nonlinear system; optimum PID parameters; optimum response; performance indices; proportional-integra-derivative controller; stability; tuning process; Control systems; Genetic algorithms; Linear programming; Mathematical model; Sociology; Statistics; Tuning; Genetic Algorithm; Objective function; Pendulum angle; Stability;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Adaptive Science & Technology (ICAST), 2014 IEEE 6th International Conference on
  • Type

    conf

  • DOI
    10.1109/ICASTECH.2014.7068099
  • Filename
    7068099