In this paper, it is proposed that the central nervous system (CNS) controls human gait using a predictive control approach in conjunction with classical feedback control instead of exclusive classical feedback control theory that controls based on past error. To validate this proposition, a dynamic model of human gait is developed using a novel predictive approach to investigate the principles of the CNS. The model developed includes two parts: a plant model that represents the dynamics of human gait and a controller that represents the CNS. The plant model is a seven-segment, six-joint model that has nine degrees-of-freedom (DOF). The plant model is validated using data collected from able-bodied human subjects. The proposed controller utilizes model predictive control (MPC). MPC uses an internal model to predict the output in advance, compare the predicted output to the reference, and optimize the control input so that the predicted error is minimal. To decrease the complexity of the model, two joints are controlled using a proportional-derivative (PD) controller. The developed predictive human gait model is validated by simulating able-bodied human gait. The simulation results show that the developed model is able to simulate the kinematic output close to experimental data.
The development of current prostheses and orthoses typically follows a trial and error approach where the devices are designed based on experience, tried on human subjects and then redesigned iteratively. This design approach is costly, risky and time consuming. A predictive human gait model is desired such that prostheses can be virtually tested so that their performance can be predicted qualitatively, the cost can be reduced, and the risks can be minimized. The development of such a model is explained in this paper. The developed model includes two parts: a plant model which represents the forward dynamics of human gait and a controller which represents the central nervous system (CNS). The development of the plant model is explained in a different paper. This paper focuses on the control algorithm development and able-bodied gait simulation. The controller proposed in this paper utilizes model predictive control (MPC). MPC uses an internal model to predict the output in advance, compare the predicted output to the reference, and optimize control input so that the error between them is minimal. The developed predictive human gait model was validated by simulating able-bodied human gait. The simulation results showed that the controller is able to simulate the kinematic output close to experimental data.
This paper develops an improvement to an existing forward dynamic human gait model. A human gait model was developed previously to assist virtual testing prostheses and orthoses. The model consists of a plant model and a controller model. The central tenet to the model is the model predictive control (MPC) algorithm, which is a highly robust controller. In the previous model, however, there are several drawbacks. First, the anthropometric and mechanical parameters in the parts of the model are specific to one person. Second, the simulation result of ground reaction force (GRF) is not realistic. In this paper, the anthropometric parameters are calculated based on commonly used models that approximate an average person’s size. As for the mechanical parameters, the spring and damper coefficients in the human joints and ground reaction force (GRF) system are estimated by using the parameter estimation module in MATLAB based on the experimental subject data. The paper concludes with a simulation results between the new improved model and the previous developed model.
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