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Design of reduced complexity controllers for linear systems under constraints using data cluster analysis

Author

Listed:
  • Amanda D. O. S. Dantas
  • André F. O. A. Dantas
  • Túlio F. D. Almeida
  • Carlos E. T. Dórea

Abstract

A numerical method is proposed to reduce the complexity and computational effort involved in the application of the multiparametric linear programming technique in the design of offline controllers for linear systems subject to constraints. For this purpose, the concept of controlled invariant sets and the K q-flat data cluster analysis algorithm are applied. Specifically, we show how the K q-flat algorithm can be used to establish a smaller number of polyhedral regions associated with a piecewise affine explicit state feedback control law. We also propose a new approach in the design of sub-optimal controllers that further reduce the number of regions. Numerical examples show that a significant reduction in the complexity of the control law can be achieved by the proposed approach.

Suggested Citation

  • Amanda D. O. S. Dantas & André F. O. A. Dantas & Túlio F. D. Almeida & Carlos E. T. Dórea, 2020. "Design of reduced complexity controllers for linear systems under constraints using data cluster analysis," International Journal of Systems Science, Taylor & Francis Journals, vol. 51(14), pages 2533-2548, October.
    • Handle: RePEc:taf:tsysxx:v:51:y:2020:i:14:p:2533-2548
    DOI: 10.1080/00207721.2020.1795948
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