Online Learning-Based Predictive Control for Nonlinear System
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917956954357760 |
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| author | Zhang, Yuanqing Zhang, Huanshui |
| author_facet | Zhang, Yuanqing Zhang, Huanshui |
| contents | In this paper, we propose an online learning-based predictive control (LPC) approach designed for nonlinear systems that lack explicit system dynamics. Unlike traditional model predictive control (MPC) algorithms that rely on known system models to optimize controller outputs, our proposed algorithm integrates a reinforcement learning component to learn optimal policies in real time from the offline dataset and real-time data. Additionally, an optimal control problem (OCP)-based optimization framework is incorporated to enhance real-time computational efficiency while ensuring stability during online operation. Moreover, we rigorously establish the super-linear convergence properties of the algorithm. Finally, extensive simulations are performed to evaluate the feasibility and effectiveness of the proposed approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_11014 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Online Learning-Based Predictive Control for Nonlinear System Zhang, Yuanqing Zhang, Huanshui Optimization and Control In this paper, we propose an online learning-based predictive control (LPC) approach designed for nonlinear systems that lack explicit system dynamics. Unlike traditional model predictive control (MPC) algorithms that rely on known system models to optimize controller outputs, our proposed algorithm integrates a reinforcement learning component to learn optimal policies in real time from the offline dataset and real-time data. Additionally, an optimal control problem (OCP)-based optimization framework is incorporated to enhance real-time computational efficiency while ensuring stability during online operation. Moreover, we rigorously establish the super-linear convergence properties of the algorithm. Finally, extensive simulations are performed to evaluate the feasibility and effectiveness of the proposed approach. |
| title | Online Learning-Based Predictive Control for Nonlinear System |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2503.11014 |