Control Design and Implementation of an Active Transtibial Prosthesis

Prior work at Marquette University developed the Marquette Prosthesis, an active transtibial prosthesis that utilized a torsional spring and a four-bar mechanism. The controls for the Marquette Prosthesis implemented a finite state control algorithm to determine the state of gait of the amputee along with two lower level controllers, a PI moment controller to control the moment during stance and a PID position controller to control the position during stance. The Marquette Prosthesis was successful in mimicking the gait profile presented by Winter. However, after completing human subject testing, the Marquette Prosthesis was insufficient in trying to match the gait profile of those who varied from this textbook stride. Active transtibial prostheses typically apply finite state control algorithms that struggle with cadence and gait variability of the amputee. Recent work in artificial neural networks (ANN) have shown the possibility to predict the user's intent which can be used as an input signal in an improved controller. The Marquette Prosthesis II was developed that uses a stiffness controller to control the relationship between the position and torque of the ankle. A model of the improved Marquette Prosthesis II was developed in Simulink to ensure that the stiffness controller was robust enough and that this type of control was possible with the limitations of the Marquette Prosthesis, i.e., the link lengths, torsional spring and motor. The mechanical system of the Marquette Prosthesis was then changed such that the spring was in series between the motor and four-bar mechanism to establish a relationship between the motor position, torque of the spring and four-bar mechanism. The control hardware was selected and the stiffness controller was implemented on the Marquette Prosthesis II. The Marquette Prosthesis II control algorithm was tested and validated to show that this approach is feasible