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Data-driven predictive control for continuous-time linear parameter varying systems with application to wind turbine

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  • Control Theory and Applications
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Abstract

A new data-driven predictive control method based on subspace identification for continuous-time linear parameter varying (LPV) systems is presented in this paper. It is developed by reformulating the continuous-time LPV system which utilizes Laguerre filters to obtain the subspace prediction of output. The subspace predictors are derived by QR decomposition of input-output and Laguerre matrices obtained by input-output data. The predictors are then applied to design the model predictive controller. It is shown that the integrated action is incorporated in the control effect to eliminate the steady-state offset. We control the continuous-time LPV systems to obtain the attractive performance with the proposed data-driven predictive control method. The proposed controller is applied to a wind turbine to verify its effectiveness and feasibility.

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Correspondence to Xiaosuo Luo.

Additional information

Recommended by Associate Editor Sung Jin Yoo under the direction of Editor Ju Hyun Park. This work was supported by the Major State Basic Research Development Program 973 (No.2012CB215202), the National Natural Science Foundation of China (No.61134001), Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University), Ministry of Education and Scientific and Technological Research Program of Chongqing Municipal Education Commission (No.KJ1503008).

Xiaouso Luo received his M.S. degree in School of Automation from Chongqing University, Chongqing, China, in 2011. He is currently a Ph.D. candidate at the School of Automation, Chongqing University. His research interests include subspace identification and model predictive control.

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Luo, X. Data-driven predictive control for continuous-time linear parameter varying systems with application to wind turbine. Int. J. Control Autom. Syst. 15, 619–626 (2017). https://doi.org/10.1007/s12555-015-0480-6

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  • DOI: https://doi.org/10.1007/s12555-015-0480-6

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