A parallel‐hierarchical neural network model for motor control of a musculo‐skeletal system

Abstract

AbstractThis paper proposes 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 the author's 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 pseudo‐inverse 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 resolves 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.