Stability-informed Bayesian Optimization for MPC Cost Function Learning

arXiv:2404.12187v1 Announce Type: cross
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 …

abstract arxiv bayesian control cost cs.lg cs.sy designing eess.sy function information learn loop mpc optimization parameters performance predictive safety stability type work