Nonlinear sparse variational Bayesian learning based model predictive control with application to PEMFC temperature control

TL;DR

This paper introduces a nonlinear sparse variational Bayesian learning approach for model predictive control, enhancing accuracy and uncertainty quantification in PEMFC temperature regulation.

Contribution

It develops a novel NSVB-MPC method that improves nonlinear system modeling and control with uncertainty assessment and recursive constraint feasibility.

Findings

Effective temperature control of PEMFC demonstrated

Enhanced model accuracy with uncertainty quantification

Ensured recursive constraint feasibility

Abstract

The accuracy of the underlying model predictions is crucial for the success of model predictive control (MPC) applications. If the model is unable to accurately analyze the dynamics of the controlled system, the performance and stability guarantees provided by MPC may not be achieved. Learning-based MPC can learn models from data, improving the applicability and reliability of MPC. This study develops a nonlinear sparse variational Bayesian learning based MPC (NSVB-MPC) for nonlinear systems, where the model is learned by the developed NSVB method. Variational inference is used by NSVB-MPC to assess the predictive accuracy and make the necessary corrections to quantify system uncertainty. The suggested approach ensures input-to-state (ISS) and the feasibility of recursive constraints in accordance with the concept of an invariant terminal region. Finally, a PEMFC temperature control model experiment confirms the effectiveness of the NSVB-MPC method.