Stable Robust Adaptive Impedance Control of a Prosthetic Leg

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) prosthetic leg for transfemoral amputees, 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 prosthetic foot. The simulation results show that the average RMS estimation errors of the thigh, knee, and ankle angles are 0.007, 0.015, and 0.465 rad with the use of four, two, and one measurements respectively. The average GRF estimation errors are 2.914, 7.595, and 20.359 N with the use of four, two, and one measurements respectively. It is shown via simulation that the state estimates remain bounded if the initial estimation errors and the disturbances are sufficiently small.

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“…Fig. 1 shows a diagram of the hip robot and prosthesis combination [17][18]. A general dynamic model for the system is given as follows:…”

Section: ӏI System Modelmentioning

confidence: 99%

Ground reaction force estimation in prosthetic legs with an extended Kalman filter

Fakoorian

Simon

Richter

et al. 2016

2016 Annual IEEE Systems Conference (SysCon)

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“…Much recent research has focused on the control of these prostheses, along with other prostheses [7][8][9][10][11][12]. Recent research has provided significant developments in modeling and control for prosthetic legs [13][14][15][16][17][18][19][20][21][22], and bipedal robots and rehabilitation robots [23][24][25]. Although direct neural integration and electromyogram signals can be recorded from residual limbs, and the ground reaction force (GRF) can be measured from prosthetic legs to recognize user intent for volitional control of the powered prosthetic legs, in this paper a pair of "classical" feedback control strategies (robust adaptive impedance controllers (RAIC)) are presented to control the robot/prosthesis device using feedback measurements of the joints position and velocity and feedback of the GRF model.…”

Section: Introductionmentioning

confidence: 99%

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

Azimi

Fakoorian

Nguyen

et al. 2018

Journal of Dynamic Systems, Measurement, and Control

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This paper presents, compares, and tests two robust model reference adaptive impedance controllers for a three degrees-of-freedom (3DOF) 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 nonparametric 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 nonscalar 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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“…Using this feature, the proposed strategy has gained some advantages over the previously presented robust adaptive control scheme [23,24,28] such as: (I) the controller intelligently adapts to the bounds of non-parametric uncertainties without any requirements for initial identification or estimation of their bounds, and (II) the chattering phenomenon in the input control torques has significantly improved.…”

Section: Introductionmentioning

confidence: 99%

“…Azimi et al [23] have presented a nonlinear robust model reference adaptive impedance controller for an active prosthetic leg. They have used both adaptive and robust (sliding mode) control terms to provide robustness against uncertain parameters and variations of the ground reaction force, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…They have used both adaptive and robust (sliding mode) control terms to provide robustness against uncertain parameters and variations of the ground reaction force, respectively. Additionally, they have introduced a boundary layer for the sliding surface in [23] to not only compromise between the control chattering and tracking performance, but also bound the parameter adaptation to prevent from unfavorable parameter drifts. In another study [24], they have used a prediction-error term in addition to the tracking-error term in the parameter adaptation law to improve the tracking performance.…”

Section: Introductionmentioning

confidence: 99%

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Robust Adaptive Sliding Mode Admittance Control of Exoskeleton Rehabilitation Robots

2017

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A nonlinear robust adaptive sliding mode admittance controller is proposed for exoskeleton rehabilitation robots. The proposed controller has robustness against uncertainties of dynamic parameters using an adaptation law. Furthermore, an adaptive Sliding Mode Control (SMC) scheme is employed in the control law to provide robustness against disturbances (non-parametric uncertainties) with unknown bounds.For this purpose, another adaptation law is defined for the variation of the SMC gain.The proposed scheme is augmented with an admittance control method to provide compliance for the patient during interaction with the rehabilitation robot. The stability of the proposed controller and the tracking performance of the system are proven using the Lyapunov stability theorem. To verify the effectiveness of the proposed control method, some simulations are conducted for a nonlinear lower-limb exoskeleton robot interacting with a patient leg via some braces. Based on the obtained results, the controller is able to provide a flexibility for the patient and appropriately respond to his/her non-compliant interaction torques. Moreover, the proposed controller significantly reduces the chattering of the input torques in comparison with a previous adaptive control method with a constant SMC gain, while it maintains a similar tracking performance.

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Stable Robust Adaptive Impedance Control of a Prosthetic Leg

Cited by 34 publications

References 36 publications

Ground reaction force estimation in prosthetic legs with an extended Kalman filter

Ground reaction force estimation in prosthetic legs with an extended Kalman filter

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

Robust Adaptive Sliding Mode Admittance Control of Exoskeleton Rehabilitation Robots

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