Controller and Predictive Filter Design In Human Arm Motion
motion control system uses the motion memory to accomplish 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 thesis, since the interaction with the environment is one of the main characteristics of the human motion controller design, a dynamic impedance control model is considered. This model consists of two feedback loops, the internal force loop and the external position loop in the Cartesian space. By exploiting 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 used to determine the impedance control coefficients. By this means and through the adaptation properties of neural networks, the proportional, integral and differential coefficients of the impedance controller is obtained during the interaction with the environment. Finally, the results of proposed controller structure are compared with the same experiments and past researches, and the accuracy and precision of its performance is studied in detail.