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Dissipativity learning control (DLC): A framework of input-output data-driven control
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DOI：10.1016/j.compchemeng.2019.106576.png)
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The paper addresses data-driven control based on input-output data in the absence of an underlying dynamic model. It proposes a dissipativity learning control (DLC) framework which involves the data-based learning of the dissipativity property of the control system, followed by a dissipativity-based controller design procedure. Specifically, independent component analysis and parametric distribution inference are adopted to estimate a polyhedral region of input-output trajectory samples, whose dual cone characterizes the dissipativity property; subsequently, an optimal controller in the L-2 sense is designed by solving a nonlinear semidefinite programming problem. The applicability of the proposed method is demonstrated by case studies on regulating control of a polymerization reactor and tracking control of an oscillatory chemical reactor. (C) 2019 Elsevier Ltd. All rights reserved.
Keyword:
Data-driven control
Dissipative systems
Machine learning
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