Abstract
Robotic systems are well-known to be highly nonlinear processes due to their complex kinematics. The identification of the corresponding system dynamics plays an important role in obtaining an adequate model which is especially hard under changing parametric circumstances. Furthermore, precise knowledge of the state is crucial for a large variety of control tasks. Sparse sensor setups make these problems more challenging due to significant noise impact. For designing an efficient and robust algorithm, an integral transform approach is proposed exploiting the robotic system structure. Specifically, the Modulating Function Method is introduced in the context of multi-body systems for fixed-time parameter and state estimation. An adaptive observer structure is presented in order to give an impression of the general methodology and the related research questions.
Brief Biography
Matti Noack received the B.Sc. degree in 2015 from the Technische Universität Ilmenau, Germany and a M.Sc. double degree in Technical Cybernetics and Systems Theory from the TU Ilmenau and in Control Engineering and Automation from the Pontificia Universidad Católica del Perú, Lima in 2017. He is working as a Ph.D. Student in the Control Engineering Group, TU Ilmenau, since November 2017. His research interests include observer design for robust parameter and state estimation as well as nonlinear systems theory and optimal control.