Self-tuning dynamic impedance control for human arm motion

It is understood that human motion control system uses the motion memory in accomplishment of an appropriate movement. It uses the past experience, learns and creates a precise or incident knowledge of the physical properties of the body and the external environment. In this paper, since the interaction with the environment is one of the main characteristics of the human motion controller design, a dynamic impedance control model is proposed. This model consists of two feedback loops, the internal force loop and the external position loop in the Cartesian space. By exploiting the dynamic impedance control scheme, the controller identifies the mechanical impedance of the environment while interacting and adapting its required impedance coefficients. A neural network self tuning PID controller is proposed to determine the controller coefficients. By this means and through the adaptation properties of neural networks, the proportional, integral and differential coefficients of the dynamic impedance controller is obtained during the interaction with the environment. Finally, the results of proposed controller structure are verified by experiments.