Model-Based Control and Optimisation
This fundamental research line deals with model-based design of theory and algorithms for constrained control and optimization. Central to this framework is the implementation of a model (e.g., differential or difference state-space equations, input-output transfer functions, hybrid automata or even Simulink models) to predict the future behavior of controlled dynamical systems in order to verify hard safety requirements and to optimize economic and control performance.
The Control Systems group focuses on constrained control of nonlinear and hybrid dynamical systems, with emphasis on model predictive control (MPC). Our contributions are in the area of stabilizing terminal ingredients for MPC, constrained and distributed stabilization, tracking and disturbance rejection, computation of Lyapunov functions and of domains of attraction, and MPC for process control.
The model-based research theme is motivated by and has an impact on applications in the high-tech industry (disturbance rejection in control of linear motors), automotive (engine, powertrain and autonomous driving control, energy management), mechatronics (control of energy converters), mathematical biology (control of HPA axis and tumor dynamics) and process industry (chemical industry, oil reservoirs).