Disturbance-observer-based fuzzy model predictive control for nonlinear processes with disturbances and input constraints
This paper proposes a disturbance-observer-based fuzzy model predictive control (DOBFMPC) scheme for the nonlinear process subject to disturbances and input constraints. The proposed control scheme is composed of the baseline fuzzy model predictive control (FMPC) law designed on the Takagi–Sugeno fu...
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| Published in | ISA transactions Vol. 90; pp. 74 - 88 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Ltd
01.07.2019
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0019-0578 1879-2022 1879-2022 |
| DOI | 10.1016/j.isatra.2018.12.041 |
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| Abstract | This paper proposes a disturbance-observer-based fuzzy model predictive control (DOBFMPC) scheme for the nonlinear process subject to disturbances and input constraints. The proposed control scheme is composed of the baseline fuzzy model predictive control (FMPC) law designed on the Takagi–Sugeno fuzzy model and the disturbance compensation law. To build a fuzzy model of appropriate complexity and accuracy for the nonlinear process model, a systematic approach is developed via the gap metric to determine the linearization points. With FMPC, the asymptotic stability is theoretically proved, and the input constraints are satisfied by both the free control variables and the future control inputs in the form of the state feedback law. The disturbance compensation gain is designed such that the influence of the disturbance is removed from the output channels by the composite DOBFMPC law at the steady state. The application to a subcritical boiler–turbine system demonstrate the effectiveness of the proposed control scheme.
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•A systematic gap-based approach is proposed to develop an appropriate fuzzy model.•Baseline fuzzy MPC ensures the asymptotic stability of nominal control system.•Input constraints are satisfied by both the free and future control inputs in fuzzy MPC.•Disturbance compensation gain is to remove the disturbance effect at steady state.•Proposed control scheme suits both the matched and mismatched disturbance cases. |
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| AbstractList | This paper proposes a disturbance-observer-based fuzzy model predictive control (DOBFMPC) scheme for the nonlinear process subject to disturbances and input constraints. The proposed control scheme is composed of the baseline fuzzy model predictive control (FMPC) law designed on the Takagi–Sugeno fuzzy model and the disturbance compensation law. To build a fuzzy model of appropriate complexity and accuracy for the nonlinear process model, a systematic approach is developed via the gap metric to determine the linearization points. With FMPC, the asymptotic stability is theoretically proved, and the input constraints are satisfied by both the free control variables and the future control inputs in the form of the state feedback law. The disturbance compensation gain is designed such that the influence of the disturbance is removed from the output channels by the composite DOBFMPC law at the steady state. The application to a subcritical boiler–turbine system demonstrate the effectiveness of the proposed control scheme.
[Display omitted]
•A systematic gap-based approach is proposed to develop an appropriate fuzzy model.•Baseline fuzzy MPC ensures the asymptotic stability of nominal control system.•Input constraints are satisfied by both the free and future control inputs in fuzzy MPC.•Disturbance compensation gain is to remove the disturbance effect at steady state.•Proposed control scheme suits both the matched and mismatched disturbance cases. This paper proposes a disturbance-observer-based fuzzy model predictive control (DOBFMPC) scheme for the nonlinear process subject to disturbances and input constraints. The proposed control scheme is composed of the baseline fuzzy model predictive control (FMPC) law designed on the Takagi-Sugeno fuzzy model and the disturbance compensation law. To build a fuzzy model of appropriate complexity and accuracy for the nonlinear process model, a systematic approach is developed via the gap metric to determine the linearization points. With FMPC, the asymptotic stability is theoretically proved, and the input constraints are satisfied by both the free control variables and the future control inputs in the form of the state feedback law. The disturbance compensation gain is designed such that the influence of the disturbance is removed from the output channels by the composite DOBFMPC law at the steady state. The application to a subcritical boiler-turbine system demonstrate the effectiveness of the proposed control scheme.This paper proposes a disturbance-observer-based fuzzy model predictive control (DOBFMPC) scheme for the nonlinear process subject to disturbances and input constraints. The proposed control scheme is composed of the baseline fuzzy model predictive control (FMPC) law designed on the Takagi-Sugeno fuzzy model and the disturbance compensation law. To build a fuzzy model of appropriate complexity and accuracy for the nonlinear process model, a systematic approach is developed via the gap metric to determine the linearization points. With FMPC, the asymptotic stability is theoretically proved, and the input constraints are satisfied by both the free control variables and the future control inputs in the form of the state feedback law. The disturbance compensation gain is designed such that the influence of the disturbance is removed from the output channels by the composite DOBFMPC law at the steady state. The application to a subcritical boiler-turbine system demonstrate the effectiveness of the proposed control scheme. This paper proposes a disturbance-observer-based fuzzy model predictive control (DOBFMPC) scheme for the nonlinear process subject to disturbances and input constraints. The proposed control scheme is composed of the baseline fuzzy model predictive control (FMPC) law designed on the Takagi-Sugeno fuzzy model and the disturbance compensation law. To build a fuzzy model of appropriate complexity and accuracy for the nonlinear process model, a systematic approach is developed via the gap metric to determine the linearization points. With FMPC, the asymptotic stability is theoretically proved, and the input constraints are satisfied by both the free control variables and the future control inputs in the form of the state feedback law. The disturbance compensation gain is designed such that the influence of the disturbance is removed from the output channels by the composite DOBFMPC law at the steady state. The application to a subcritical boiler-turbine system demonstrate the effectiveness of the proposed control scheme. |
| Author | Kong, Lei Yuan, Jingqi |
| Author_xml | – sequence: 1 givenname: Lei surname: Kong fullname: Kong, Lei email: konglei@sjtu.edu.cn – sequence: 2 givenname: Jingqi surname: Yuan fullname: Yuan, Jingqi email: jqyuan@sjtu.edu.cn |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30803744$$D View this record in MEDLINE/PubMed |
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| Keywords | Disturbance-observer-based control Boiler–turbine system Input constraint Fuzzy model predictive control Gap metric |
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| SubjectTerms | Boiler–turbine system Disturbance-observer-based control Fuzzy model predictive control Gap metric Input constraint |
| Title | Disturbance-observer-based fuzzy model predictive control for nonlinear processes with disturbances and input constraints |
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