Dynamic Simulation of Human Gait Using a Combination of Model Predictive and PID Control

Human gait studies have not been applied frequently to the prediction of the performance of medical devices such as prostheses and orthoses. The reason is most biomechanics simulations require experimental data such as muscle activity or joint moment information a priori. In addition, biomechanical models are normally too complicated to be adjusted and these simulations normally take a long period of time to be performed which makes testing of various possibilities time consuming; therefore they are not suitable for prediction purpose. The objective of this research is to develop a control oriented human gait model that is able to predict the performance of prostheses and orthoses before they are experimentally tested. This model is composed of two parts. The first part is a seven link nine degree-of-freedom (DOF) plant to represent the forward dynamics of human gait. The second part is a control system which is a combination of Model Predictive Control (MPC) and Proportional-Integral-Derivative (PID) control. The purpose of this control system is to simulate the central nervous system (CNS). This model is sufficiently simple that it can be simulated and adjusted in a reasonable time, while still representing the essential principles of human gait