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Title page for etd-0901111-200331


URN etd-0901111-200331 Statistics This thesis had been viewed 1462 times. Download 0 times.
Author Ching-teng Hsu
Author's Email Address No Public.
Department Electrical Engineering
Year 2010 Semester 2
Degree Master Type of Document Master's Thesis
Language English Page Count 147
Title TUNING THE PARAMETER OF PID CONTROLLER USING SELF-ADAPTIVE DIFFERENTIAL EVOLUTION ALGORITHM
Keyword
  • PID controller
  • differential evolution algorithm
  • fuzzy neural network
  • fuzzy neural network
  • differential evolution algorithm
  • PID controller
  • Abstract A PID control scheme via combinations of fuzzy neural network (FNN) and self adaptive differential evolution algorithm with chaos theory (SADEC-PID) is proposed in this thesis. The SADEC-PID controller is consisted of a FNN estimator, a PID controller and a SADEC optimizer. The PID controller is the main effort which uses the error, integral of the error, and derivation of the error with the corresponding parameters to control uncertain nonlinear systems. The FNN estimator is utilized as the tuning-tool for each parameter of the PID controller. The SADEC optimizer is used to optimally select parameters of the FNN estimator. In order to improve the convergence of SADEC optimizer, firstly, a chaotic sequence based on logistic map is introduced to adjust mutation factor. Next, a dynamic updating of population strategy is utilized in process of evolution that responses more flexibly to best value of the current. Furthermore, a self-adaptive crossover probability factor is presented to improve the diversity of population and the ability of escaping from the local optimum. Finally, according to the index of mean-square-error (MSE) to determine the SADEC optimizer whether it will be continuous evolution or correction. The results of the simulation are implemented to verify the effectiveness of the proposed controller.
    Advisor Committee
  • Hung-ching Lu - advisor
  • none - co-chair
  • none - co-chair
  • Files indicate not accessible
    Date of Defense 2011-06-30 Date of Submission 2011-09-02


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