Data-driven robust optimal control design for uncertain cascaded systems using value iteration

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, a new non-model-based control design is proposed to solve the H∞ control problem for linear continuous-time systems. Our first contribution is to develop a robust control design by combining the zero-sum differential game theory with the gain assignment technique together. Compared with traditional game theory-based approaches, the obtained result allows us to assign arbitrarily the input-to-output L2 gain for a class of continuous-time linear cascaded systems. Moreover, the presence of dynamic uncertainty is tackled using the small-gain theory. Our second contribution is to give a new non-model-based robust adaptive dynamic programming (RADP) algorithm. In sharp contrast to the existing methods, the obtained algorithm is based on continuous-time value iteration (VI), and an initial stabilizing control policy is no longer required. Finally, an example of a power system is adopted to illustrate the effectiveness of the obtained algorithm.

Original languageEnglish (US)
Title of host publication54rd IEEE Conference on Decision and Control,CDC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages7610-7615
Number of pages6
ISBN (Electronic)9781479978861
DOIs
StatePublished - Feb 8 2015
Event54th IEEE Conference on Decision and Control, CDC 2015 - Osaka, Japan
Duration: Dec 15 2015Dec 18 2015

Publication series

NameProceedings of the IEEE Conference on Decision and Control
Volume54rd IEEE Conference on Decision and Control,CDC 2015
ISSN (Print)0743-1546

Other

Other54th IEEE Conference on Decision and Control, CDC 2015
CountryJapan
CityOsaka
Period12/15/1512/18/15

Keywords

  • Gain
  • Game theory
  • Games
  • Optimal control
  • Power system dynamics
  • Robustness
  • Symmetric matrices

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

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