Date of Award

Spring 2015

Document Type


Degree Name

Doctor of Philosophy (PhD)


Mechanical Engineering

First Advisor

Voglewede, Philip A.

Second Advisor

Craig, Kevin C.

Third Advisor

Silver-Thorn, Barbara


This dissertation aims to develop a dynamic model of human gait, especially the working principle of the central nervous system (CNS), using a novel predictive approach. Based on daily experience, it should be straightforward to understand the CNS controls human gait based on predictive control. However, a thorough human gait model using the predictive approach have not yet been explored. This dissertation aims to fill this gap. The development of such a predictive model can assist the developing of lower limb prostheses and orthoses which typically follows a trial and error approach. With the development of the predictive model, lower limb prostheses might be virtually tested so that their performance can be predicted qualitatively, future cost can be reduced, and the risks can be minimized. The model developed in this dissertation includes two parts: a plant model which represents the forward dynamics of human gait and a controller which represents the CNS. The plant model is a seven-segment six-joint model which has nine degrees of freedom. The plant model is validated using data collected from able-bodied human subjects. The experimental moment profile of each joint is input to the model; the kinematic output of the model is consistent with the experimental kinematics which verifies the fidelity of the plant model. The developed predictive human gait model is first validated by simulating able-bodied human gait. The simulation results show that the controller is able to simulate the kinematic output close to experimental data. The developed model was then validated by simulating variable speed able-bodied human gait. The simulation results showed the dynamic characteristics of variable speed gait could be qualitatively predicted by the developed model. Finally the gait of a unilateral transtibial amputee wearing passive prosthetic ankle joint is simulated to verify its ability to qualitatively predict the dynamic characteristics of pathological gait. This dissertation opens the door for modeling human gait from predictive control perspective. With the development of such a model, future prosthetic and orthotic designers can greatly reduce cost, avoid risk, and save time by using the virtual design and testing of prostheses and orthoses.