A Data-Driven Automatic Tuning Method for MPC under Uncertainty using Constrained Bayesian Optimization

TL;DR

This paper introduces a data-driven, automated tuning method for model predictive controllers under uncertainty, utilizing constrained Bayesian optimization to improve performance without manual tuning.

Contribution

It presents a novel approach that formulates MPC tuning as a constrained black-box optimization problem solved by Bayesian optimization, handling noisy and costly evaluations.

Findings

Effective automated tuning demonstrated on a benchmark reactor

Improved MPC performance with less manual intervention

Handles noisy, expensive-to-evaluate functions

Abstract

The closed-loop performance of model predictive controllers (MPCs) is sensitive to the choice of prediction models, controller formulation, and tuning parameters. However, prediction models are typically optimized for prediction accuracy instead of performance, and MPC tuning is typically done manually to satisfy (probabilistic) constraints. In this work, we demonstrate a general approach for automating the tuning of MPC under uncertainty. In particular, we formulate the automated tuning problem as a constrained black-box optimization problem that can be tackled with derivative-free optimization. We rely on a constrained variant of Bayesian optimization (BO) to solve the MPC tuning problem that can directly handle noisy and expensive-to-evaluate functions. The benefits of the proposed automated tuning approach are demonstrated on a benchmark continuously stirred tank reactor example.