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Robust ground reaction force estimation and control of lower-limb prostheses: theory and simulation

Azimi, V ; Sharif University of Technology | 2020

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  1. Type of Document: Article
  2. DOI: 10.1109/TSMC.2018.2836913
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2020
  4. Abstract:
  5. Ground reaction force (GRF) characteristics of amputee walking are important for the analysis of clinical gait data, and also to update model reference adaptive impedance (MRAI) controllers. GRF estimation is a better alternative than direct GRF measurement because of the disadvantages of load cells, such as high cost, integration difficulties due to weight and physical dimensions, the possibility of overload, and measurement noise. This paper presents four robust MRAI observer/controller combinations for GRF estimation-based control of a prosthesis and a legged robot model in the presence of parametric uncertainties and unmodeled dynamics, in which the robot model is employed to mimic able-bodied walking. Since unknown GRFs can reduce the performance of prosthesis control systems, the proposed MRAI controllers are designed to compensate their effects. All four proposed observer/controller combinations enable the system to imitate the biomechanics of able-bodied walking as defined by mechanical impedance, and provide flexible and smooth gait. Lyapunov analysis of the observer/controller combinations is used to prove stability. The four MRAI controllers are compared with each other from the perspective of GRF estimation accuracy, prosthetic knee angle tracking accuracy, parameter estimation accuracy, control effort, and computational effort. Simulation results show that all four MRAI controllers achieve good tracking performance, stable limit cycles, and accurate GRF estimation. © 2013 IEEE
  6. Keywords:
  7. Adaptive control ; Estimation ; Prosthetics ; Robust control ; Sliding mode control ; Artificial limbs ; Biophysics ; Computation theory ; Controllers ; Estimation ; Prosthetics ; Robust control ; Sliding mode control ; Adaptation models ; Adaptive Control ; Legged locomotion ; Observers ; Uncertainty ; Parameter estimation
  8. Source: IEEE Transactions on Systems, Man, and Cybernetics: Systems ; Volume 50, Issue 8 , 2020 , Pages 3024-3035
  9. URL: https://ieeexplore.ieee.org/document/8371283