Use of an artificial neuroadaptive robot model to describe adaptive and learning motor mechanisms in the central nervous system

Based on previous physiological information, this paper proposes a model of cerebellum motor learning based on a neuroadaptive robot manipulator controller. Compliance (or impedance) control is chosen as the basis of the model in preference to alternative robot control strategies because muscles do not act like pure force generators such as torque motors nor as pure displacement devices such as stepper motors but instead act more like tunable springs or compliance devices. Compliance control has the further advantage that it is applicable for a variety of motor tasks, and is both more robust and simple than alternative control strategies. Simulation results are presented to verify the performance of the proposed model. Specific results are presented for the applications of impedance control to the case where the end-effector is interacting with surfaces. By setting the equilibrium position of the end-effector beyond the obstacle (wall), it can be assured that the end-effector will touch the surface rather than crush it. The power of the phase spare to analyze the behavior of the system during movement is demonstrated.