Learning Trajectory and Force Control of an Artificial Muscle Arm

We propose a new parallel-hierarchical neural network model to enable motor learning for simultaneous control of both trajectory and force. by integrating Hogan's control method and our previous neural network control model using a feedback-error-learning scheme. Furthermore. two hierarchical control laws which apply to the model, are derived by using the Moore-Penrose pseudoinverse matrix. One is related to the minimum muscle-tension-change trajectory and the other is related to the minimum motor-command-change trajectory. The human arm is redundant at the dynamics level since joint torque is generated by agonist and antagonist muscles. Therefore, acquisition of the inverse model is an ill-posed problem. However. the combination of these control laws and feedback-error-learning resolve the ill-posed problem. Finally. the efficiency of the parallel-hierarchical neural network model is shown by learning experiments using an artificial muscle arm and computer simulations.