Optimizing Model Predictive Control Performance and Stability via Constrained Bayesian Optimization of Neural Cost Functions

This work explores a Bayesian optimization approach to learn the cost function parameters of a model predictive controller, optimizing closed-loop performance while ensuring stability through Lyapunov-based constraints.