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

arXiv:2404.09519v1 Announce Type: new
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 …

abstract accuracy analyze application applications arxiv bayesian control cs.lg cs.sy dynamics eess.sy mpc performance predictions predictive stability success type