Stability-informed Bayesian Optimization for MPC Cost Function Learning

• URL: http://arxiv.org/abs/2404.12187v1
• Date: Thu, 18 Apr 2024 13:49:09 GMT
• Title: Stability-informed Bayesian Optimization for MPC Cost Function Learning
• Authors: Sebastian Hirt, Maik Pfefferkorn, Ali Mesbah, Rolf Findeisen,
• Abstract summary: This work explores closed-loop learning for predictive control parameters under imperfect information. We employ constrained Bayesian optimization to learn a model predictive controller's (MPC) cost function parametrized as a feedforward neural network. 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.
• Score: 5.643541009427271
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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