تقرير
Stability-informed Bayesian Optimization for MPC Cost Function Learning
| العنوان: | Stability-informed Bayesian Optimization for MPC Cost Function Learning |
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| المؤلفون: | Hirt, Sebastian, Pfefferkorn, Maik, Mesbah, Ali, Findeisen, Rolf |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Electrical Engineering and Systems Science - Systems and Control, Computer Science - Machine Learning |
| الوصف: | Designing predictive controllers towards optimal closed-loop performance while maintaining safety and stability is challenging. This work explores closed-loop learning for predictive control parameters under imperfect information while considering closed-loop stability. We employ constrained Bayesian optimization to learn a model predictive controller's (MPC) cost function parametrized as a feedforward neural network, optimizing closed-loop behavior as well as minimizing model-plant mismatch. Doing so offers a high degree of freedom and, thus, the opportunity for efficient and global optimization towards the desired and optimal closed-loop behavior. We extend this framework by stability constraints on the learned controller parameters, exploiting the optimal value function of the underlying MPC as a Lyapunov candidate. The effectiveness of the proposed approach is underlined in simulations, highlighting its performance and safety capabilities. Comment: 7 pages, 3 figures, accepted for NMPC 2024 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2404.12187 |
| رقم الأكسشن: | edsarx.2404.12187 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |