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A Comparison of Genetic Programming and Genetic Algorithms in the Design of a Robust, Saturated Control System

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Genetic and Evolutionary Computation – GECCO 2004 (GECCO 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3103))

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Abstract

The design of a robust control system for a specified second order plant is considered using three different approaches. Initially, a control system evolved by a genetic programming algorithm is reproduced and analysed in order to identify its advantages and drawbacks. The automatic design technique is compared to a traditional one through the analysis of the constraints and performance indices obtained by simulation. A set of unspecified control constraints explored by the GP search process is found to be the cause of a better performance. Hence, giving a better constraints specification, a genetic algorithm is used to evolve an alternative controller. A PID structure is used by the GA to produce and tune the controller. Simulations show a significant gain in performance thanks to a more aggressive and complete exploration of the search space within the constraints. The effectiveness of the two methods compared to the traditional approach is discussed with regard to performance, complexity of design and computational viability.

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Author information

Authors and Affiliations

  1. Department of Computer and Information Science, Norwegian University of Science and Technology, N-7491, Trondheim, Norway

    Andrea Soltoggio

Authors
  1. Andrea Soltoggio
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Editor information

Editors and Affiliations

  1. Indian Institute of Technology Kanpur, Department of Mechanical Engineering, Kanpur Genetic Algorithms Laboratory (KanGAL), Kanpur, 208016, Uttar Pradesh, India

    Kalyanmoy Deb

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© 2004 Springer-Verlag Berlin Heidelberg

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Soltoggio, A. (2004). A Comparison of Genetic Programming and Genetic Algorithms in the Design of a Robust, Saturated Control System. In: Deb, K. (eds) Genetic and Evolutionary Computation – GECCO 2004. GECCO 2004. Lecture Notes in Computer Science, vol 3103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24855-2_16

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  • DOI: https://doi.org/10.1007/978-3-540-24855-2_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22343-6

  • Online ISBN: 978-3-540-24855-2

  • eBook Packages: Springer Book Archive

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