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Model predictive control with input disturbance and guaranteed Lyapunov stability for controller approximation

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An approximation method that can lower the computational complexity is presented to reduce the processing time of model predictive control (MPC). The effects of the inaccurate inputs, which are caused by the approximation errors, are reflected as input disturbances and are considered in the design of the controller. The closed-loop system’s stability is guaranteed by a restricted Lyapunov-based constraint and input constraints, ensuring that the states will be eventually bounded at a certain confidence level. According to this paper, the effects of the bounded disturbance can be observed via a change in the stability region. The relationship between the regions of the normal and approximated systems is highlighted. The proposed MPC with input disturbance and guaranteed Lyapunov stability is employed in a chemical process, and the simulation results indicate the efficiency of the proposed method.

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Acknowledgements

This work was supported by National Key R&D Program of China (Grant No. 2018AAA0101701) and National Natural Science Foundation of China (Grant Nos. 62073220, 61833012).

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Authors and Affiliations

  1. Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, China

    Yanye Wang, Shaoyuan Li & Yi Zheng

  2. Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, 200240, China

    Yanye Wang, Shaoyuan Li & Yi Zheng

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  1. Yanye Wang
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  2. Shaoyuan Li
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  3. Yi Zheng
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Correspondence to Yi Zheng.

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Wang, Y., Li, S. & Zheng, Y. Model predictive control with input disturbance and guaranteed Lyapunov stability for controller approximation. Sci. China Inf. Sci. 65, 192205 (2022). https://doi.org/10.1007/s11432-021-3338-0

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  • DOI: https://doi.org/10.1007/s11432-021-3338-0

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