Towards data-based adaptive control

Aniruddha Datta, Ritwik Layek, Hazem Nounou, Mohamed Nounou, Navid Mohsenizadeh, Shankar P. Bhattacharyya

Research output: Contribution to journalArticle

20 Citations (Scopus)

Abstract

This paper presents a possible new direction for the design of adaptive controllers. It begins by outlining a measurement-based approach to the analysis, synthesis, and design of unknown systems. The systems under consideration can typically be electrical, mechanical, hydraulic, continuous, discrete, real, artificial, or biological, or of societal origin. Our approach is novel in the sense that we propose to use the measurement, or measured data, to directly design the control or decision variables whereas the current approach in the mainstream of system theory advocates that measured data be used to first identify the plant, and control design, or synthesis, then be based on the identified model. This latter approach has necessitated the creation of fields such as robust control and (indirect) adaptive control that try to account for errors in the identified model. Our approach, being data based, would potentially be automatically robust and adaptive. We show that such a measurement-based approach is certainly feasible by considering DC circuits, AC circuits, block diagrams, and control systems. These results are then used to formulate the research problems for data-based adaptive control.

Original languageEnglish
Pages (from-to)122-135
Number of pages14
JournalInternational Journal of Adaptive Control and Signal Processing
Volume27
Issue number1-2
DOIs
Publication statusPublished - 1 Jan 2013

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Keywords

  • Adaptive control
  • Data-based controller synthesis
  • Robust control
  • Thévenin's theorem

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Electrical and Electronic Engineering

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