Stable, adaptive interaction and contact transition control of a high inertia haptic interface for haptic simulation in gait rehabilitation

In robot assisted rehabilitation the development of sophisticated training strategies using force and impedance control methods is an important field of research. The objective of the presented work is to provide a haptic control framework for end-effector based gait rehabilitation robots, which allows for the training of arbitrary foot trajectories of daily life and the design of sophisticated, patient-adaptive training environments. Due to the end-effector design principle the machine carries the patient's full bodyweight during the whole training cycle which implies excessively high forces at the interaction ports. To allow for a guided movement under the described conditions the machine features powerful drives and a massive, stiff mechanical structure. This results in exceptionally high device inertia comparable to heavy-duty industrial robots and normally not common in haptic displays. To ensure stable interaction with a passive user and a known range of stiff and viscose virtual environments an adaptive virtual coupling controller is developed. To minimize the complexity of design procedure, a linear, discrete-time coupled and contact transition stability analysis is derived, taking into account the known target range of environments. The developed haptic controller was implemented and tested experimentally on the robotic walking simulator HapticWalker. It was shown to interact stably with virtual environments within the defined stiffness bounds. Further a set of lower target impedance parameters was evaluated in free motion without contact. An algorithm for the adaptation of the target damping was suggested to improve transparency during free motion while still ensuring a stable contact transition and interaction with stiff environment objects. The feasibility of the adaptive coupling controller was proven in free motion and walking experiments on the HapticWalker with the simulation static and dynamic virtual environments.