ESO-Based Data Driven Set-Point Learning Control for Nonlinear batch Processes with PD-Type Feedback Control Structure Subject to Nonrepetitive Uncertainties
In this paper, an extended state observer (ESO) based data-driven set-point learning control (DDSPLC) scheme is proposed for nonlinear batch processes configured with an inherent PD-type control loop, so as to actively suppress the adverse effect of nonrepetitive external disturbances and nonidentic...
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| Vydané v: | Data Driven Control and Learning Systems Conference (Online) s. 2082 - 2088 |
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| Hlavní autori: | , , , , , , |
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IEEE
17.05.2024
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| Abstract | In this paper, an extended state observer (ESO) based data-driven set-point learning control (DDSPLC) scheme is proposed for nonlinear batch processes configured with an inherent PD-type control loop, so as to actively suppress the adverse effect of nonrepetitive external disturbances and nonidentical initial resetting conditions, by only utilizing the available process input/output data. The controlled process is firstly reformulated as an iterative dynamic linearization data model (IDLDM) with an extra generalized disturbance, where the unknown pseudo partial derivative and the generalized disturbance are, respectively, estimated by a projection-like algorithm and iterative ESO along the batch direction. By virtue of the estimated PPD and generalized disturbance, a set-point learning updating law with adaptive learning gain is then developed to optimize the tracking performance from batch-to-batch, by tuning the set-point command of the closed-loop PD-type control. Robust convergence analysis for the established closed-loop learning system is analyzed rigorously. The effectiveness and superiority of the proposed scheme are verified by an illustrative example. |
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| AbstractList | In this paper, an extended state observer (ESO) based data-driven set-point learning control (DDSPLC) scheme is proposed for nonlinear batch processes configured with an inherent PD-type control loop, so as to actively suppress the adverse effect of nonrepetitive external disturbances and nonidentical initial resetting conditions, by only utilizing the available process input/output data. The controlled process is firstly reformulated as an iterative dynamic linearization data model (IDLDM) with an extra generalized disturbance, where the unknown pseudo partial derivative and the generalized disturbance are, respectively, estimated by a projection-like algorithm and iterative ESO along the batch direction. By virtue of the estimated PPD and generalized disturbance, a set-point learning updating law with adaptive learning gain is then developed to optimize the tracking performance from batch-to-batch, by tuning the set-point command of the closed-loop PD-type control. Robust convergence analysis for the established closed-loop learning system is analyzed rigorously. The effectiveness and superiority of the proposed scheme are verified by an illustrative example. |
| Author | Hao, Shoulin Zhang, Jiyan Wang, Haixia Ahmad, Naseem Liu, Tao Gong, Yihui Zhu, Yong |
| Author_xml | – sequence: 1 givenname: Naseem surname: Ahmad fullname: Ahmad, Naseem organization: Dalian University of Technology,Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education,Dalian,China,116024 – sequence: 2 givenname: Shoulin surname: Hao fullname: Hao, Shoulin organization: Dalian University of Technology,Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education,Dalian,China,116024 – sequence: 3 givenname: Tao surname: Liu fullname: Liu, Tao email: liurouter@ieee.org organization: Dalian University of Technology,Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education,Dalian,China,116024 – sequence: 4 givenname: Yihui surname: Gong fullname: Gong, Yihui organization: Dalian University of Technology,Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education,Dalian,China,116024 – sequence: 5 givenname: Jiyan surname: Zhang fullname: Zhang, Jiyan organization: Institute of Advanced Control Technology, Dalian University of Technology,Dalian,P. R. China,116024 – sequence: 6 givenname: Haixia surname: Wang fullname: Wang, Haixia organization: Institute of Advanced Control Technology, Dalian University of Technology,Dalian,P. R. China,116024 – sequence: 7 givenname: Yong surname: Zhu fullname: Zhu, Yong organization: School of Biomedical Engineering, Dalian University of Technology,Dalian,P. R. China,116024 |
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| Snippet | In this paper, an extended state observer (ESO) based data-driven set-point learning control (DDSPLC) scheme is proposed for nonlinear batch processes... |
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| SubjectTerms | Batch production systems Data-driven set-point learning control Heuristic algorithms Iterative algorithms iterative extended state observer Learning systems nonlinear batch processes nonrepetitive uncertainties Observers PD-type feedback control Process control Uncertainty |
| Title | ESO-Based Data Driven Set-Point Learning Control for Nonlinear batch Processes with PD-Type Feedback Control Structure Subject to Nonrepetitive Uncertainties |
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