Gravitational Search Algorithm-Based Tuning of Fuzzy Control Systems with a Reduced Parametric Sensitivity

  • Radu-Emil Precup
  • Radu-Codruţ David
  • Emil M. Petriu
  • Stefan Preitl
  • Adrian Sebastian Paul
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 96)


This paper proposes the tuning of a class of fuzzy control systems to ensure a reduced parametric sensitivity on the basis of a new Gravitational Search Algorithm (GSA). The GSA is employed to solve the optimization problems characterized by the minimization of objective functions defined as integral quadratic performance indices. The performance indices depend on the control error and on the squared output sensitivity functions of the sensitivity models with respect to the parametric variations of the controlled process. The controlled processes in the fuzzy control systems are benchmarks modeled by second-order linearized systems with an integral component and Takagi-Sugeno proportional-integral fuzzy controllers are designed and tuned for these processes.


Fuzzy Controller Iterative Learn Control Gravitational Search Algorithm Fuzzy Control System Clonal Selection Algorithm 
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
  • Emil M. Petriu
    • 2
  • Stefan Preitl
    • 1
  • Adrian Sebastian Paul
    • 1
  1. 1.Department of Automation and Applied Informatics“Politehnica” University of TimisoaraTimisoaraRomania
  2. 2.School of Information Technology and EngineeringUniversity of OttawaOttawaCanada

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