David QuinteroUniversity of Texas at Dallas
Lower-limb amputee population is gradually increasing, primarily due to complications from vascular diseases. The vast majority of lower-limb amputees use mechanically passive prosthetic legs, which has amputees ambulate slower, have an asymmetric gait, and require higher metabolic consumption than an able-bodied person. To improve amputee gait, powered prosthetic legs are in development to restore the biomechanical function of the missing leg muscles. These powered devices require highly sophisticated control strategies, particularly for multi-joint legs, to perform various activities in a natural and safe manner. Generally, these control methods divide the gait cycle into multiple, sequential periods with different impedance-based controllers. This results in many patient-specific control parameters and switching rules that must be tuned for a specific ambulation mode, such as a desired walking speed or slope. Furthermore, when perturbed this control approach could switch to the wrong state and use the wrong controller, which may increase an amputee’s risk of falling.
The different periods of gait could potentially be unified over the entire gait cycle by virtual kinematic constraints, where a single controller drives the joint patterns parameterize with respect to a mechanical phase variable. This provides a sense of phase in synchronizing the joints during ambulation. The idea of virtual constraint control has shown success in providing stable walking and running for underactuated bipedal robots. A unified phase-based controller will be presented using a human-inspired phase variable for powered prosthesis control. Experiments with above-knee amputees using the unified controller will also be presented. A discussion for how this control paradigm from robot control theory can potentially make an impact for other wearable robotic devices.
David Quintero received his B.S. degree in Mechanical Engineering with a Minor in Mathematics from Texas A&M University, College Station in 2006. He went on to complete his M.S. degree in Mechanical Engineering at Stanford University in 2008. In the meantime, he spent a few years as a robotics and controls engineer in industry before returning to pursue his Ph.D. degree at The University of Texas at Dallas in Mechanical Engineering. His research interests include high-performance actuator designs, wearable robots, nonlinear controls, and biomechanics.
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