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.

该研究发展了一种基于非线性稀疏变分贝叶斯学习的模型预测控制方法，在该方法中通过 NSVB 方法学习模型，利用变分推断来评估预测准确性并对系统不确定性进行必要的修正，以确保输入到状态的稳定性和约束条件的可行性，最后通过 PEMFC 温度控制模型实验证实了 NSVB-MPC 方法的有效性。