Robot Control
Module type: compulsory elective
Academic: Prof. Dr.-Ing. Vladislav Nenchev
Field of study: Applied Computer Technology (ACT), Applied Communication Technology (CT), Cyber Security (CYB)
Credit points: 3
The course covers control systems in robotics with a focus on modeling, optimal control, model predictive control (MPC), and learning-based control. After a brief introduction to state-space formulations and fundamental system properties, concepts of optimal control such as linear quadratic regulation (LQR) and dynamic programming are treated. Next, the fundamentals of MPC with constraints, its properties, and handling of uncertainties are discussed. Then, physical model learning with neural networks is introduced and its application to control systems is demonstrated. Finally, an introduction to reinforcement learning is given, including Markov decision processes and both model-based and model-free approaches. The concepts are applied in practical applications.