The C³S Lab focuses on stability and control of complex dynamical systems subject to constraints. Complex dynamical systems typically encompass more interconnected nonlinear systems and perform complex tasks subject to rich safety and performance specifications. Mastering complex dynamical systems plays a key role in the development of smart energy systems, high-tech mechatronics, autonomous vehicles or bio-medical systems.

We make use of model predictive control (MPC) theory to deal with constraints and we design MPC algorithms and fast MPC solvers for complex systems (highly nonlinear, hybrid, uncertain or large-scale interconnected systems). We research flexible control Lyapunov functions to enforce stability for real-time controllers. To increase autonomy and reliability of control systems we focus on integration of artificial intelligence (neural networks) with classical and predictive controllers.

The methods developed by the C³S Lab have been applied in automotive (distributed MPC for platooning, predictive fuel efficiency optimization), energy (distributed control of power systems, constrained control of power converters, predictive temperature control in smart buildings), mechatronics (fast nonlinear MPC for coreless linear motors, Lyapunov control of permanent magnet synchronous machines), robotics (predictive path planning and trajectory tracking for a LEGO robot) and biological systems (stability analysis and switching control therapies for cancer and HPA dynamical models).