Virtual Constraint Control of a Powered Prosthetic Leg: From Simulation to Experiments With Transfemoral Amputees

Abstract

Recent powered (or robotic) prosthetic legs independently control different joints and time periods of the gait cycle, resulting in control parameters and switching rules that can be difficult to tune by clinicians. This challenge might be addressed by a unifying control model used by recent bipedal robots, in which virtual constraints define joint patterns as functions of a monotonic variable that continuously represents the gait cycle phase. In the first application of virtual constraints to amputee locomotion, this paper derives exact and approximate control laws for a partial feedback linearization to enforce virtual constraints on a prosthetic leg. We then encode a human-inspired invariance property called effective shape into virtual constraints for the stance period. After simulating the robustness of the partial feedback linearization to clinically meaningful conditions, we experimentally implement this control strategy on a powered transfemoral leg. We report the results of three amputee subjects walking overground and at variable cadences on a treadmill, demonstrating the clinical viability of this novel control approach.

State Feedback Sliding Mode Controller Design for Human Swing Leg System

“…The test was done on the robot leg from -11.50 o to 11.50 o and obtained high control action about 30 N.m. In 2014, Gregg et al [15] implemented virtual constraints that unify the stance period, coordinate ankle and knee control, and accommodate clinically meaningful conditions on a powered prosthetic leg. The saturate prosthesis torques at 80 N.m to simulate the torque limit of the experimental prosthesis.…”

Stable, Robust Hybrid Zero Dynamics Control of Powered Lower-Limb Prostheses

“…Rather than model all of the DoF of the foot, the function of the foot and ankle is modeled using a circular foot plus an ankle joint [23] to capture both the center of pressure movement [38] and the positive work performed at the stance ankle [39]. To account for the lack of information transmitted between the human and prosthesis, the full model can be divided into a prosthesis subsystem and a human subsystem [19]. The prosthesis subsystem consists of the prosthetic thigh, shank, and foot.…”

“…Recent work has shown that human joint patterns are parameterized well by a phase variable [16], [17], which is a kinematic quantity that measures how far a step (or stride) has progressed. In addition, pre-clinical work has demonstrated that phase-based controllers can successfully control powered prostheses [18], [19], although determination of the best control strategy is still very much an open question.…”

“…Despite these challenges, pre-clinical work has shown that a powered above-knee prosthesis controlled with an HZD-like controller during stance and an impedance-based controller during swing allows amputees to walk at a variety of speeds and ground slopes [19]. That work did not generalize the input-output linearizing controller to the prosthesis swing phase or, to allow for more realistic modeling, to the human subsystem.…”

A Cyber Expert System for Auto-Tuning Powered Prosthesis Impedance Control Parameters

“…7,11,17 However, only modest simplification can be achieved since at least 3 states are typically defined for levelground walking, and parameter values differ across tasks (i.e., ramp ascent/descent, stair ascent/descent). 16,18,38 Another solution to tune fewer parameters is to associate parameter values with one another 36 or with other intrinsic biomechanical measures (e.g., prosthesis joint angles, prosthesis load, walking speed, foot center of pressure, effective leg shape).…”