Powered Prosthesis Locomotion on Varying Terrains: Model-Dependent Control With Real-Time Force Sensing

Gehlhar, Rachel; Yang, Jehan; Ames, Aaron D.

doi:10.1109/lra.2022.3154810

Cited by 7 publications

(6 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The whole system weighs 10.54 kg, while the prosthesis on its own weighs 5.95kg. For more details on the platform, see [32], and for the force sensors and IMU, [27].…”

Section: Resultsmentioning

confidence: 99%

“…Force Sensing ID-CLF-QP. To develop a hardware implementable form of a RES-CLF, we construct a variation of the inverse dynamics CLF quadratic program (ID-CLF-QP), introduced in [23], that was developed the prosthesis subsystem in [27]. To prescribe a desired behavior to our output dynamics ( ẏsT 1,v , ÿsT 2,v ) = ν, we define a desired auxiliary control input,…”

Section: Controller Realizationmentioning

confidence: 99%

“…This paper differs from [21] in both method and results, using a formally grounded force-based controller to achieve humanlike walking, verified through the kinematics. The work of [27] realized the first model-based lower-limb prosthesis controller with real-time force sensing on the knee joint of a powered prosthesis in 2 phases, stance and swing, with 1 prescribed walking gait. This paper extends the work of [27] in 3 major ways:…”

Section: Introductionmentioning

confidence: 99%

“…The work of [27] realized the first model-based lower-limb prosthesis controller with real-time force sensing on the knee joint of a powered prosthesis in 2 phases, stance and swing, with 1 prescribed walking gait. This paper extends the work of [27] in 3 major ways:…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Emulating Human Kinematic Behavior on Lower-Limb Prostheses via Multi-Contact Models and Force-Based Nonlinear Control

Gehlhar¹,

Ames²

2022

Preprint

Self Cite

View full text Add to dashboard Cite

Ankle push-off largely contributes to limb energy generation in human walking, leading to smoother and more efficient locomotion. Providing this net positive work to an amputee requires an active prosthesis, but has the potential to enable more natural assisted locomotion. To this end, this paper uses multi-contact models of locomotion together with force-based nonlinear optimization-based controllers to achieve human-like kinematic behavior, including ankle push-off, on a powered transfemoral prosthesis for 2 subjects. In particular, we leverage model-based control approaches for dynamic bipedal robotic walking to develop a systematic method to realize human-like walking on a powered prosthesis that does not require subject-specific tuning. We begin by synthesizing an optimization problem that yields gaits that resemble human joint trajectories at a kinematic level, and realize these gaits on a prosthesis through a control Lyapunov function based nonlinear controller that responds to real-time ground reaction forces and interaction forces with the human. The proposed controller is implemented on a prosthesis for two subjects without tuning between subjects, emulating subject-specific human kinematic trends on the prosthesis joints. These experimental results demonstrate that our force-based nonlinear control approach achieves better tracking of human kinematic trajectories than traditional methods.

show abstract

“…The whole system weighs 10.54 kg, while the prosthesis on its own weighs 5.95kg. For more details on the platform, see [32], and for the force sensors and IMU, [27].…”

Section: Resultsmentioning

confidence: 99%

Section: Controller Realizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Emulating Human Kinematic Behavior on Lower-Limb Prostheses via Multi-Contact Models and Force-Based Nonlinear Control

Gehlhar¹,

Ames²

2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…For the first aspect, the dynamics involved in PHSs are often characterized by uncertainties or partial knowledge, such as the unknown mass distribution [19] and the intricate foot-ground interaction [20]. In other words, system uncertainties can arise from various sources, including inaccurate modeling of dynamics [21], sensor noise [22] [23], and environmental disturbances [24].…”

Section: Introductionmentioning

confidence: 99%

Robotic Leg Prosthesis: A Survey From Dynamic Model to Adaptive Control for Gait Coordination

Ma,

Zhang,

2024

IEEE Trans. Neural Syst. Rehabil. Eng.

View full text Add to dashboard Cite

Gait coordination (GC), meaning that one leg moves in the same pattern but with a specific phase lag to the other, is a spontaneous behavior in the walking of a healthy person. It is also crucial for unilateral amputees with the robotic leg prosthesis to perform ambulation cooperatively in the real world. However, achieving the GC for amputees poses significant challenges to the prostheses' dynamic modeling and control design. Still, there has not been a clear survey on the initiation and evolution of the detailed solutions, hindering the precise decision of future explorations. To this end, this paper comprehensively reviews GCoriented dynamic modeling and adaptive control methods for robotic leg prostheses. Considering the two representative environments concerned with adaptive control, we first classify the dynamic models into the deterministic model for structured terrain and the constrained stochastic model for stochastically uneven terrain. Inspired by the concept of synchronization, we then emphasize three typical problems for the GC realization, i.e., complete coordination on structured terrain, stochastic coordination on stochastically uneven terrain, and finite-time delayed stochastic coordination. Finally, we conclude with a discussion on the remaining challenges and opportunities in controlling robotic leg prostheses.

show abstract

Dynamic model refining and identification for accurate gait control of a powered knee–ankle prosthesis

Zhang,

Lv,

Zhang

et al. 2024

Nonlinear Dyn

View full text Add to dashboard Cite

Powered Prosthesis Locomotion on Varying Terrains: Model-Dependent Control With Real-Time Force Sensing

Cited by 7 publications

References 35 publications

Emulating Human Kinematic Behavior on Lower-Limb Prostheses via Multi-Contact Models and Force-Based Nonlinear Control

Emulating Human Kinematic Behavior on Lower-Limb Prostheses via Multi-Contact Models and Force-Based Nonlinear Control

Robotic Leg Prosthesis: A Survey From Dynamic Model to Adaptive Control for Gait Coordination

Dynamic model refining and identification for accurate gait control of a powered knee–ankle prosthesis

Contact Info

Product

Resources

About