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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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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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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DOI: https://doi.org/10.1007/s11432-022-3650-8