  This study utilized the Gaussian Processes (GPs) regression framework to
establish stochastic error bounds between the actual and predicted state
evolution of nonlinear systems. These systems are embedded in the linear
parameter-varying (LPV) formulation and controlled using model predictive
control (MPC). Our main 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.
