A Two-Stage Scheduling RPC Based on Time-Varying Coefficient Information of State-Dependent ARX Model
A two-stage scheduling robust predictive control (RPC) algorithm, which is based on the time-varying coefficient information of the state-dependent ARX (SD-ARX) model, is designed for the output tracking control of a class of nonlinear systems. First, by using the parameter variation range informati...
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| Veröffentlicht in: | Mathematical problems in engineering 2020, Vol.2020 (2020), p.1-15 |
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| creator | Wu, Jun Xie, Minghua Zhu, Peidong Zhou, Feng Cao, Lihua |
| description | A two-stage scheduling robust predictive control (RPC) algorithm, which is based on the time-varying coefficient information of the state-dependent ARX (SD-ARX) model, is designed for the output tracking control of a class of nonlinear systems. First, by using the parameter variation range information of the SD-ARX, a strategy for constructing the system’s polytopic model is designed. To further reduce the conservativeness of the convex polytopic sets which are designed to wrap the system’s future dynamics, the variation range information of the SD-ARX model’s parameters is also considered and compressed. In this method, the polytopic state-space model of the system is constructed directly based on the special structure of the SD-ARX model itself, and there is no need to make such assumption that the bounds on the parameter’s variation range in the system model are known or measurable. And then, a two-stage scheduling RPC algorithm is designed for the output tracking control. A numerical example is presented to demonstrate the effectiveness of the proposed RPC strategy. |
| doi_str_mv | 10.1155/2020/5319408 |
| format | Article |
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First, by using the parameter variation range information of the SD-ARX, a strategy for constructing the system’s polytopic model is designed. To further reduce the conservativeness of the convex polytopic sets which are designed to wrap the system’s future dynamics, the variation range information of the SD-ARX model’s parameters is also considered and compressed. In this method, the polytopic state-space model of the system is constructed directly based on the special structure of the SD-ARX model itself, and there is no need to make such assumption that the bounds on the parameter’s variation range in the system model are known or measurable. And then, a two-stage scheduling RPC algorithm is designed for the output tracking control. A numerical example is presented to demonstrate the effectiveness of the proposed RPC strategy.</description><identifier>ISSN: 1024-123X</identifier><identifier>EISSN: 1563-5147</identifier><identifier>DOI: 10.1155/2020/5319408</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Algorithms ; Controllers ; Design ; Mathematical problems ; Neural networks ; Nonlinear systems ; Parameter estimation ; Parameters ; Predictive control ; Robust control ; Scheduling ; State space models ; Tracking control</subject><ispartof>Mathematical problems in engineering, 2020, Vol.2020 (2020), p.1-15</ispartof><rights>Copyright © 2020 Feng Zhou et al.</rights><rights>Copyright © 2020 Feng Zhou et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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| source | Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Wiley Online Library Open Access; Alma/SFX Local Collection |
| subjects | Algorithms Controllers Design Mathematical problems Neural networks Nonlinear systems Parameter estimation Parameters Predictive control Robust control Scheduling State space models Tracking control |
| title | A Two-Stage Scheduling RPC Based on Time-Varying Coefficient Information of State-Dependent ARX Model |
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