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Convergence of adaptive MPC for linear stochastic systems

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Abstract

The convergence of an adaptive model predictive control (MPC) algorithm for discrete-time linear stochastic systems with unknown parameters is investigated in this paper. The proposed adaptive MPC is designed by solving a finite horizon constrained linear-quadratic optimal control problem of online estimated models, which are built on a recursive weighted least-squares (WLS) algorithm together with a random regularization method. By incorporating an attenuating excitation signal into adaptive MPC, the proposed adaptive MPC is shown to converge asymptotically to the ergodic MPC performance with known parameters by using the Markov chain ergodic theory.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant No. 12288201) and National Center for Mathematics and Interdisciplinary Sciences, CAS.

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Authors and Affiliations

  1. Institute of Systems Science, Academy of Mathematics and Systems Science (AMSS), Chinese Academy of Sciences, Beijing, 100190, China

    Hui Chen & Lei Guo

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  1. Hui Chen
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  2. Lei Guo
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Correspondence to Hui Chen or Lei Guo.

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Chen, H., Guo, L. Convergence of adaptive MPC for linear stochastic systems. Sci. China Inf. Sci. 66, 152201 (2023). https://doi.org/10.1007/s11432-022-3650-8

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