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Optimal Control Systems with Reduced Parametric Sensitivity Based on Particle Swarm Optimization and Simulated Annealing

  • Radu-Emil Precup
  • Radu-Codruţ David
  • Stefan Preitl
  • Emil M. Petriu
  • József K. Tar
Part of the Studies in Computational Intelligence book series (SCI, volume 366)

Abstract

This chapter discusses theoretical and design aspects for optimal control systems with a reduced parametric sensitivity using Particle Swarm Optimization (PSO) and Simulated Annealing (SA) algorithms. Sensitivity models with respect to the parametric variations of the controlled process are derived and the optimal control problems are defined. The new objective functions in these optimization problems are integral quadratic performance indices that depend on the control error and squared output sensitivity functions. Different dynamic regimes are considered. Relatively simple PSO and SA optimization algorithms are developed for the minimization of the objective functions, which optimize the control system responses and reduce the sensitivity to parametric variations of the controlled process. Examples of optimization problems encountered in the design of optimal proportional-integral (PI) controllers for a class of second-order processes with integral component are used to validate the proposed methods.

Keywords

Particle Swarm Optimization Simulated Annealing Particle Swarm Optimization Algorithm Fuzzy Controller Reference Input 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Radu-Emil Precup
    • 1
  • Radu-Codruţ David
    • 1
  • Stefan Preitl
    • 1
  • Emil M. Petriu
    • 2
  • József K. Tar
    • 3
  1. 1.Department of Automation and Applied Informatics“Politehnica” University of TimisoaraTimisoaraRomania
  2. 2.School of Information Technology and EngineeringUniversity of OttawaOttawaCanada
  3. 3.Institute of Intelligent Engineering SystemsÓbuda UniversityBudapestHungary

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