Stochastic Error Bounds in Nonlinear Model Predictive Control with Gaussian Processes via Parameter-Varying Embeddings

This study utilized the Gaussian Processes (GPs) regression framework to establish stochastic error bounds between nonlinear systems' actual and predicted state evolution. These systems are embedded in the linear parameter-varying (LPV) formulation and controlled using model predictive control (MPC). Our primary focus is quantifying the uncertainty of the LPVMPC framework's forward error resulting from scheduling signal estimation mismatch. We compared our stochastic approach with a recent deterministic approach and observed improvements in conservatism and robustness. To validate our analysis and method, we solved the regulator problem of an unbalanced disk.