A novel adaptive control of a human musculoskeletal arm model

Abstract

The paper proposes a novel adaptive control of the human musculoskeletal arm model in order to simulate reaching movements of the human upper extremity. The controller is based on a normalized radial basis function neural network, which takes the Actor-Critic structure. The normalized radial basis function neural network simultaneously approximates the policy function of the actor network and the value function of the critic network. An adaptive adjustment mechanism is dynamically established to realize the state space constructions. The approach could effectively overcome the curse of dimensionality caused by state space division and always keep the structure in an optimal state. The human arm model adopts a Hill-type model with two joints, six muscles. As a validation, numerical simulations are performed to achieve the reaching movements of the human upper extremity. The results show that the adaptive control can well instruct the human arm model to mimic the reaching movements.