Robust Adaptive Impedance Control with Application to a Transfemoral Prosthesis and Test Robot

[+] Author and Article Information
Vahid Azimi

School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA

Seyed Abolfazl Fakoorian

Department of Electrical Engineering and Computer Science, Cleveland State University, Cleveland, OH, USA

Thang Tien Nguyen

Modeling Evolutionary Algorithms Simulation and Artificial Intelligence, Faculty of Electrical & Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

Dan Simon

Department of Electrical Engineering and Computer Science, Cleveland State University, Cleveland, OH, USA

1Corresponding author.

ASME doi:10.1115/1.4040463 History: Received June 30, 2017; Revised May 22, 2018


This paper presents, compares and tests two robust model reference adaptive impedance controllers for a three degree-of-freedom (3-DOF) powered prosthesis / test robot. We first present a model for a combined system that includes a test robot and a transfemoral prosthetic leg. We design these two controllers so the error trajectories of the system converge to a boundary layer and the controllers show robustness to ground reaction forces (GRFs) as non-parametric uncertainties and also handle model parameter uncertainties. We prove the stability of the closed-loop systems for both controllers for the prosthesis / test robot in the case of non-scalar boundary layer trajectories using Lyapunov stability theory and Barbalat's lemma. We design the controllers to imitate the biomechanical properties of able-bodied walking and to provide smooth gait. We finally present simulation results to confirm the efficacy of the controllers for both nominal and off-nominal system model parameters. We achieve good tracking of joint displacements and velocities, and reasonable control and GRF magnitudes for both controllers. We also compare performance of the controllers in terms of tracking, control effort, and parameter estimation for both nominal and off-nominal model parameters.

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