Model Predicitve Control

Model predictive control (MPC) is a highly successful optimisation-based control technique. It involves repeatedly solving a finite-horizon optimal control problem at each sampling time and applying the first part of the corresponding optimal solution to the system up to the next sampling time. The main advantages of MPC, and the reasons for its popularity and widespread success in many application areas, are that

1. satisfaction of hard constraints on states and inputs can be guaranteed, 
2. optimization of a performance criterion can be incorporated directly in the controller design,
3. it can be applied to general nonlinear systems with possibly multiple inputs.

Our research focuses on various current topics of interest in MPC. These include developing robust, stochastic and adaptive MPC schemes for uncertain systems; considering more general control objectives than setpoint/trajectory tracking (e.g. economic MPC); studying distributed MPC schemes for large-scale systems; and investigating data- and learning-based MPC methods. We are also investigating the application of MPC to various problems, including reactive test environments for autonomous vehicles, optimal medication strategies for thyroid diseases and multimodal energy systems.