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Seyed Fakoorian Masterthesis.pdf (2.11 MB)

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Ground Reaction Force Estimation in Prosthestic Legs with an Extended Kalman Filter

Fakoorian, Seyed Abolfazl
http://rave.ohiolink.edu/etdc/view?acc_num=csu148227120124386

Abstract Details

2016, Master of Science in Electrical Engineering, Cleveland State University, Washkewicz College of Engineering.
A method to estimate ground reaction forces (GRFs) in a robot/prosthesis system is presented. The system includes a robot that emulates human hip and thigh motion, along with a powered (active) transfemoral prosthetic leg, and includes four degrees of freedom (DOF): vertical hip displacement, thigh angle, knee angle, and ankle angle. We design a continuous-time extended Kalman filter (EKF) to estimate not only the states of the robot/prosthesis system, but also the GRFs that act on the foot. The performance of the robot in various gait modes (normal walk, fast walk, and slow walk) is studied, when we use four measurement sensors and when we use only three and two measurements. The simulation results show that the average RMS estimation errors of the GRF in normal walking are 9.54, 17.19, and 24.38 N with the use of four, three and two measurements respectively. It is proven using stochastic Lyapunov functions that the estimation error is exponentially bounded if the initial estimation errors and the disturbances are sufficiently small in our robot/prosthesis system. The robustness of the EKF is compared with the H8 filter in terms of RMS estimation error, when the robot/prosthesis system is perturbed by ±10% parameter variations. Moreover, the accuracy of the estimation performed by the EKF is considered when the robot/prosthesis system is disturbed by non-Gaussian noise. For this purpose, we first propose a new Kalman type filtering approach, called the maximum correntropy criterion Kalman filter (MCC-KF). Then, the results are generalized to introduce a new extended Kalman filter, called extended MCC-KF (EMCC-KF). We also employ the continuous-time unscented Kalman filter (UKF) as a derivative-free Kalman filter which typically performs better than the EKF for state estimation in the presence of non-Gaussian noise. Simulation results indicate that the H8 filter outperforms the EKF in the presence of system parameter uncertainties and we achieve around 30% improvement in the average estimation error of the thigh, knee, and ankle angles, when the system parameters are varied by 10%. In addition, we show that the EMCC-KF has significantly better performance compared with the EKF and UKF in the presence of large outliers or non-Gaussian noise, since correntropy contains second and higher-order information of the signal.
Dan Simon, PhD (Committee Chair)
Hanz Richter, PhD (Committee Member)
Antonie Van den Bogert, PhD (Committee Member)
Electrical Engineering

Recommended Citations

Citations

  • Fakoorian, S. A. (2016). Ground Reaction Force Estimation in Prosthestic Legs with an Extended Kalman Filter [Master's thesis, Cleveland State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=csu148227120124386

    APA Style (7th edition)

  • Fakoorian, Seyed Abolfazl. Ground Reaction Force Estimation in Prosthestic Legs with an Extended Kalman Filter. 2016. Cleveland State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=csu148227120124386.

    MLA Style (8th edition)

  • Fakoorian, Seyed Abolfazl. "Ground Reaction Force Estimation in Prosthestic Legs with an Extended Kalman Filter." Master's thesis, Cleveland State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=csu148227120124386

    Chicago Manual of Style (17th edition)

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This open access ETD is published by Cleveland State University and OhioLINK.