# Performance-oriented model learning for data-driven MPC design

## TL;DR

This paper introduces a novel data-driven approach to optimize model learning for MPC, focusing on enhancing closed-loop performance by selecting the best prediction model through Bayesian optimization.

## Contribution

It applies the 'identification for control' concept to hierarchical MPC using Bayesian optimization, a first in this context, to improve control performance.

## Key findings

- Enhanced closed-loop performance with data-driven model selection
- Successful application of Bayesian optimization in hierarchical MPC
- Improved robustness without conservative assumptions

## Abstract

Model Predictive Control (MPC) is an enabling technology in applications requiring controlling physical processes in an optimized way under constraints on inputs and outputs. However, in MPC closed-loop performance is pushed to the limits only if the plant under control is accurately modeled; otherwise, robust architectures need to be employed, at the price of reduced performance due to worst-case conservative assumptions. In this paper, instead of adapting the controller to handle uncertainty, we adapt the learning procedure so that the prediction model is selected to provide the best closed-loop performance. More specifically, we apply for the first time the above "identification for control" rationale to hierarchical MPC using data-driven methods and Bayesian optimization.