Model predictive control is a control scheme in which an optimization problem is solved to find the next control action to apply. Explicit solutions rarely exist, and the optimization problem is typically solved using an optimization algorithm. The control application sets hard limits on how much time can be spent on solving the optimization problem each iteration. Most theory for model predictive control assumes that the optimization problems are solved to optimality, which is not feasible in practice. This project aims at establishing new theory for model predictive control stability analysis with the restriction that the optimization algorithm can only run a finite and pre-defined number of algorithm iterations each time a new problem is solved.
Gällande start-/slutdatum2023/08/17 → …