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RBF-ARX model-based two-stage scheduling RPC for dynamic systems with bounded disturbance

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Abstract

With directly considering the unknown and bounded disturbance, a RBF-ARX model-based two-stage scheduling quasi-min–max robust predictive control (RBF-ARX-TRPC) algorithm for output-tracking control is proposed for a class of smooth nonlinear systems with unknown steady-state knowledge. Firstly, from the RBF-ARX model that is identified using input/output data of the system, the two local linearization state-space models that consider the bounded disturbance and a polytopic uncertain LPV state-space model are built to approximate the present and future system’s nonlinear dynamics, respectively. Based on the state-space models, the RBF-ARX-TRPC algorithm is designed without relying on the system steady-state knowledge. In the RBF-ARX-TRPC algorithm, the future nonlinear behavior of the system is forced to vary within the region constructed by the polytopic uncertain LPV state-space model. Closed-loop stability is guaranteed when the algorithm is implemented in a receding horizon fashion by including a Lyapunov constraint in the formulation. The comparative experiments demonstrate the effectiveness of the proposed strategy on a continuously stirred tank reactor (CSTR) simulator.

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Acknowledgements

The authors would like to thank the editors and the anonymous referees for their valuable comments and suggestions, which substantially improved the original manuscript. This work was supported by the National Natural Science Foundation of China (61773402, 61540037, 71271215, 51575167), the International Science and Technology Cooperation Program of China (2011DFA10440), and the Collaborative Innovation Center of Resource-conserving and Environment-friendly Society and Ecological Civilization.

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Authors and Affiliations

  1. School of Information Science and Engineering, Central South University, Changsha, 410083, Hunan, China

    Feng Zhou, Hui Peng, Xiaoyong Zeng & Xiaoying Tian

  2. College of Electronic Information and Electrical Engineering, Changsha University, Changsha, 410003, Hunan, China

    Feng Zhou

  3. Collaborative Innovation Center of Resource-conserving & Environment-friendly Society and Ecological Civilization, Changsha, 410083, Hunan, China

    Hui Peng, Xiaoyong Zeng & Xiaoying Tian

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  1. Feng Zhou
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Correspondence to Hui Peng.

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Zhou, F., Peng, H., Zeng, X. et al. RBF-ARX model-based two-stage scheduling RPC for dynamic systems with bounded disturbance. Neural Comput & Applic 31, 4185–4200 (2019). https://doi.org/10.1007/s00521-018-3347-y

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  • DOI: https://doi.org/10.1007/s00521-018-3347-y

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