Nonlinear sparse variational Bayesian learning based model predictive
  control with application to PEMFC temperature control
Qi Zhang Lei Wang Weihua Xu Hongye Su Lei Xie
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.