Our research lab focuses on the theoretical and real-time implementation aspects of constrained predictive model-based control. We deal with linear, nonlinear and hybrid systems in both small scale andcomplex large scale applications. Our contributions include the discovery of fundamental theoretical results, the development of novel control algorithms and their experimental validation carried out with the help of coworkers from industries and universities. We deal with a wide range of systems in the automotive field, in the process industries and in robotics, including several full scale industrial problems.

CDC 2018 workshop on learning for control material can be found here