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.
Extremum Seeking Control for Stiffness Auto-Tuning of a Quasi-Passive Ankle Exoskeleton
Recently, it has been shown that light-weight, passive, ankle exoskeletons with spring-based energy store-and-release mechanisms can reduce the muscular effort of human walking. The stiffness of the spring in such a device must be properly tuned in order to minimize the muscular effort. However, this muscular effort changes for different locomotion conditions (e.g., walking speed), causing the optimal spring stiffness to vary as well. Existing passive exoskeletons have a fixed stiffness during operation, preventing it from responding to changes in walking conditions. Thus, there is a need of a device and auto-tuning algorithm that minimizes the muscular effort across different walking conditions, while preserving the advantages of passive exoskeletons. In this letter, we developed a quasi-passive ankle exoskeleton with a variable stiffness mechanism capable of self-tuning. As the relationship between the muscular effort and the optimal spring stiffness across different walking speeds is not known a priori, a model-free, discrete-time extremum seeking control (ESC) algorithm was implemented for real-time optimization of spring stiffness. Experiments with an able-bodied subject demonstrate that as the walking speed of the user changes, ESC automatically tunes the torsional stiffness about the ankle joint. The average RMS EMG readings of tibialis anterior and soleus muscles at more »
- Publication Date:
- NSF-PAR ID:
- 10159541
- Journal Name:
- IEEE Robotics and Automation Letters
- Volume:
- 5
- Issue:
- 3
- Page Range or eLocation-ID:
- 4604 - 4611
- ISSN:
- 2377-3774
- Sponsoring Org:
- National Science Foundation
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- https://doi.org/10.1109/LRA.2020.3001541
