ArticleIEEE transactions on control systems technology : a publication of the IEEE Control Systems Society2020
Extremum Seeking Control for Model-Free Auto-Tuning of Powered Prosthetic Legs.
Article in IEEE transactions on control systems technology : a publication of the IEEE Control Systems Society, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- An Active Spring Mass Model With Biomimetic Ground Reaction Forces for Multiple Terrains.IEEE transactions on bio-medical engineering · 2026Article
- Extremum-Seeking Control for a Robotic Leg Prosthesis with Sensory Feedback.Sensors (Basel, Switzerland) · 2025Article
- The exoskeleton expansion: improving walking and running economy.Journal of neuroengineering and rehabilitation · 2020Review
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Authors and funding
6 authors.
Funding
Abstract
This paper proposes an extremum seeking controller (ESC) for simultaneously tuning the feedback control gains of a knee-ankle powered prosthetic leg using continuous-phase controllers. Previously, the proportional gains of the continuous-phase controller for each joint were tuned manually by trial-and-error, which required several iterations to achieve a balance between the prosthetic leg tracking error performance and the user's comfort. In this paper, a convex objective function is developed, which incorporates these two goals. We present a theoretical analysis demonstrating that the quasi-steady-state value of the objective function is independent of the controller damping gains. Furthermore, we prove the stability of error dynamics of continuous-phase controlled powered prosthetic leg along with ESC dynamics using averaging and singular perturbation tools. The developed cost function is then minimized by ESC in real-time to simultaneously tune the proportional gains of the knee and ankle joints. The optimum of the objective function shifts at different walking speeds, and our algorithm is suitably fast to track these changes, providing real-time adaptation for different walking conditions. Benchtop and walking experiments verify the effectiveness of the proposed ESC across various walking speeds.
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