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• W4313644954 abstract "Optimal set-point control is important in maintaining good control performance of practical industrial processes. Considering the challenges when modeling a complex process, this work presents a data-driven optimal set-point control (DDOSC) scheme for nonlinear nonaffine systems bypassing modeling steps. The outer loop of the proposed DDOSC adopts an ideal nonlinear set-point control function which is theoretically achievable to obtain a perfect tracking. Then, a dynamic linearization (DL) is used to transfer the ideal nonlinear set-point control law into a linear parametric one such that it is implementable through a parameter updating law. To address the nonlinear nonaffine system within a data-driven framework, the DL method is again used to obtain its linear data model which is further updated by a parameter-adaptive law. With a proportional feedback controller in the inner loop, the convergence of the DDOSC method is shown. In addition, the results are further extended by considering a proportional-integral-derivative feedback controller in the inner loop. The simulation results on a numerical example and a car suspension system verify the effectiveness of the proposed DDOSC in improving the performance of the local feedback controller." @default.
• W4313644954 created "2023-01-07" @default.
• W4313644954 creator A5004282593 @default.
• W4313644954 creator A5047053338 @default.
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• W4313644954 date "2023-05-01" @default.
• W4313644954 modified "2023-10-16" @default.
• W4313644954 title "Data-driven set-point control for nonlinear nonaffine systems" @default.
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• W4313644954 doi "https://doi.org/10.1016/j.ins.2022.12.115" @default.
• W4313644954 hasPublicationYear "2023" @default.
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