Learning to control unknown nonlinear dynamical systems is a fundamental problem in
reinforcement learning and control theory. A commonly applied approach is to first explore …

We propose an algorithm to actively estimate the parameters of a linear dynamical system.
Given complete control over the system's input, our algorithm adaptively chooses the inputs …

Model-based control design plays a key role in today's industrial practice, and industry
demands cuttingedge methods for identifying the necessary models. However, additional …

System identification methods have played an essential role in the research and industry
projects at the Institute of Flight System Dynamics of the Technical University of Munich …

Model predictive control has become an increasingly popular control strategy thanks to the
ability to handle constrained systems. Obtaining the required models through system …

It is commonly observed that over the lifetime of most model predictive controllers, the
achieved performance degrades over time. This effect can often be attributed to the fact that …

Exploration in unknown environments is a fundamental problem in reinforcement learning
and control. In this work, we study task-guided exploration and determine what precisely an …

We present a robust dual control MPC (RDCMPC) policy with guaranteed constraint
satisfaction for simultaneous closed-loop identification and regulation of state and input …

Identification of systems operating in closed loop is an important problem in industrial
applications, where model-based control is used to an increasing extent. For model-based …

Having an accurate model of a system is essential for many applications today, especially
those related to advanced process control (APC). When executing an industrial delivery …