Self-Tuning of MPC Controller for Mobile Robot Path Tracking Based on Machine Learning

Author:

Liu Yuesheng,He Ning,He Lile,Zhang Yiwen,Xi Kun,Zhang Mengrui

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Reference11 articles.

1. XI Y G, LI D W, LIN S. Model predictive control-status and challenges [J]. Acta Automatica Sinica, 2013, 39(3): 222–236.

2. ZHANG R D, XUE A K, GAO F R. Model predictive control: Approaches based on the extended state space model and extended non-minimal state space model [M]. Singapore: Springer, 2018.

3. CHEN Y, JIANG W L, SHI X H, et al. Tuning parameters for model predictive controller based on CSA-SQP [J]. Journal of Ningbo University (Natural Science & Engineering Edition), 2017, 30(4): 51–57 (in Chinese).

4. IRA A S, MANZIE C, SHAMES I, et al. Tuning of multivariable model predictive controllers through expert bandit feedback [J]. International Journal of Control, 2021, 94(10): 2650–2658.

5. MOUMOUH H, LANGLOIS N, HADDAD M. A novel tuning approach for MPC parameters based on artificial neural network [C]//2019 IEEE 15th International Conference on Control and Automation. Edinburgh: IEEE, 2019: 1638–1643.

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