Neurological disorders and injuries lead to a loss of sensorimotor function in the central nervous system, which controls the musculoskeletal system. Novel systems and control methods can be employed to create neuroprostheses that restore these functions to an unprecedented degree by two major advances: (1) Long standing limitations of inertial motion tracking are overcome by novel parameter estimation and sensor fusion methods. (2) A recent extension of classic learning control methods facilitates real-time pattern adaptation in artificial muscle recruitment. We review the role of these methods in the development of biomimetic neuroprostheses and discuss their potential impact in a range of further application systems including autonomous vehicles, robotics, and multi-agent networks.
Biography: Thomas Seel is with the Control Systems Group at TU Berlin. He is responsible for the group's research focuses 'Learning Control Methods and Applications' and 'Intelligent Inertial Sensor Networks'. Thomas has received his PhD from the department of Electrical Engineering and Computer Science of TU Berlin in 2016. Prior to that, he had studied Engineering Cybernetics at OvGU Magdeburg and UC Santa Barbara and briefly worked at Linde Engineering in Munich.