TY - JOUR
T1 - Dissipativity learning control (DLC)
T2 - Theoretical foundations of input–output data-driven model-free control
AU - Tang, Wentao
AU - Daoutidis, Prodromos
N1 - Funding Information:
This work was supported by NSF-CBET, USA .
Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2021/1
Y1 - 2021/1
N2 - Data-driven, model-free control strategies leverage statistical or learning techniques to design controllers based on data instead of dynamic models. We have previously introduced the dissipativity learning control (DLC) method, where the dissipativity property is learned from the input–output trajectories of a system, based on which L2-optimal P/PI/PID controller synthesis is performed. In this work, we analyze the statistical conditions on dissipativity learning that enable control performance guarantees, and establish theoretical results on performance under nominal conditions as well as in the presence of statistical errors. The implementation of DLC is further formalized and is illustrated on a two-phase chemical reactor, along with a comparison to model identification-based LQG control.
AB - Data-driven, model-free control strategies leverage statistical or learning techniques to design controllers based on data instead of dynamic models. We have previously introduced the dissipativity learning control (DLC) method, where the dissipativity property is learned from the input–output trajectories of a system, based on which L2-optimal P/PI/PID controller synthesis is performed. In this work, we analyze the statistical conditions on dissipativity learning that enable control performance guarantees, and establish theoretical results on performance under nominal conditions as well as in the presence of statistical errors. The implementation of DLC is further formalized and is illustrated on a two-phase chemical reactor, along with a comparison to model identification-based LQG control.
KW - Data-driven control
KW - Dissipative systems
KW - Model-free control
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U2 - 10.1016/j.sysconle.2020.104831
DO - 10.1016/j.sysconle.2020.104831
M3 - Article
AN - SCOPUS:85097714872
SN - 0167-6911
VL - 147
JO - Systems and Control Letters
JF - Systems and Control Letters
M1 - 104831
ER -