Uncertainty characterization in robust MPC using an approximate convex hull
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Model Predictive Control (MPC) of processes when uncertainty is involved is the topic of this thesis. Specifically, a method to characterize parametric uncertainty for robust model predictive control is studied. The goal is to reduce the computational complexity of robust MPC and robust Moving Horizon Estimation (MHE). The main element of this method is the computation of an approximate convex hull that approximately covers the system uncertainty in a new output prediction mapping. Given the complete uncertainty set, an approximate convex hull is computed to determine an efficient set of extreme ...