Safe control of redundant musculoskeletal systems using nonlinear MPC

Simulating musculoskeletal system models reveals that the mapping from muscle stimulations to movement dynamics is highly nonlinear and complex. This makes it difficult to control those systems with classical techniques. Nevertheless, in previous studies we were able to synthesize controllers for arm and leg motion in simulation and biomimetic robots using appropriate heuristic and optimization techniques. Additionally, we showed that biology exhibits properties, which are favorable for learning the controller, autonomously. In this project, our aim is to use nonlinear MPC on top of low-level feedback control to mimic human motion for point-reaching and cyclic movements both with arm and leg models in simulation and with muscle-driven robots in real world experiments. The leading research question is as to whether and how the design of the biological motor system facilitates the control task as well as the learning task to control the movement.