Nonlinear sparse variational Bayesian learning based model predictive control with application to PEMFC temperature control
This work addresses temperature control in proton exchange membrane fuel cells (PEMFCs), an incremental improvement in learning-based MPC for specific industrial applications.
The study tackled the problem of inaccurate model predictions in model predictive control (MPC) for nonlinear systems by developing a nonlinear sparse variational Bayesian learning based MPC (NSVB-MPC) method, which improved applicability and reliability, as confirmed by a PEMFC temperature control experiment.
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.