The team’s research focus is on optimization and control with a current application focus on mechatronic and renewable energy systems. Our core area of expertise are embedded optimization algorithms, i.e. methods for real-time optimization on embedded platforms, with a focus on nonlinear systems. The work of the group spans from dynamic system modelling and optimal control problem formulations, including modelling languages, to the development of numerical algorithms, their efficient implementation in higher level programming languages, automatic C-code generation for embedded platforms, and experimental implementation. The group’s interdisciplinary work is located between numerical mathematics, computer science, and control engineering, a mix that is also reflected in the master degrees that PhD students have when they start working in the group.

The two most important special cases of embedded optimization for control applications are model predictive control (MPC) and moving horizon estimation (MHE), which can be seen as computationally intensive but extremely powerful generalizations of the linear quadratic regulator and the Kalman filter. These advanced optimization-based control and estimation techniques allow one to naturally deal with constrained, nonlinear, and multi- input-multi-output systems. They require the real-time solution of optimal control problems and have already pervaded large parts of chemical process control, where timescales are in the order of minutes, which safely allows for the real-time solution of these MHE and MPC optimization problems. But MHE and MPC are currently starting to revolutionize also the control of mechatronic and of power systems, where the allowable computational times are in the range of milli and microseconds. These systems are characterized by tight constraints, multiple inputs and outputs, and strong but well known nonlinearities. Applications the team is interested in are for example solar power plants, induction motors, power converters, energy storage, hybrid electric vehicles, eco-driving, robotic arms, and machine tools. We also expect a strong impact of embedded optimization in advanced mechatronics and microsystems engineering. One very specific application problem class that is very prominent in the team is Airborne Wind Energy (AWE), which concerns an innovative concept to harness wind power with help of tethered but free flying wings, similar to kites.

Below is a list of all research projects pursued by the systems control and optimization laboratory.

Current Projects

Real-world realization and control of a rotary airborne wind energy system.

Numerical optimal control for Multi-wing Airborne Wind Energy Robustness Optimization

Nonlinear Control and State Estimation of Model Race Cars

Past Projects

Airborne Wind Energy System Modelling, Control and Optimisation

Highly Dynamic Control of Photovoltaic Inverters

Numerical Methods for Optimization-Based Control of Cyclic Processes

Nonlinear Model Predictive Control for wind turbines

Software and methods for system identification and optimization-based control of renewable energy systems

Network for research in embedded optimization-based control

Simulation, Optimization and Control of High-Altitude Wind Power Generators.