An admittance shaping controller for exoskeleton assistance of the lower extremities

Lower limb rehabilitation exoskeleton robots integrate sensing, control, and other technologies and exhibit the characteristics of bionics, robotics, information and control science, medicine, and other interdisciplinary areas. In this review, the typical products and prototypes of lower limb exoskeleton rehabilitation robots are introduced and stateof-the-art techniques are analyzed and summarized. Because the goal of rehabilitation training is to recover patients' sporting ability to the normal level, studying the human gait is the foundation of lower limb exoskeleton rehabilitation robot research. Therefore, this review critically evaluates research progress in human gait analysis and systematically summarizes developments in the mechanical design and control of lower limb rehabilitation exoskeleton robots. From the performance of typical prototypes, it can be deduced that these robots can be connected to human limbs as wearable forms; further, it is possible to control robot movement at each joint to simulate normal gait and drive the patient's limb to realize robot-assisted rehabilitation training. Therefore human-robot integration is one of the most important research directions, and in this context, rigid-flexible-soft hybrid structure design, customized personalized gait generation, and multimodal information fusion are three key technologies.

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“…Recently, robots not based on sensors have been developed. In this case, an admittance shaping controller is used [86].…”

Section: Control Systemmentioning

confidence: 99%

A Review on Lower Limb Rehabilitation Exoskeleton Robots

Shi

Zhang

et al. 2019

Chin. J. Mech. Eng.

345

132

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“…The exoskeleton is a device that interacts with the human. The main applications of exoskeletons are physical assistance and rehabilitation [1][2][3][4][5][6][7][8][9][10]. During human-machine interaction, under no circumstances should the exoskeleton directly or indirectly cause injury to the human, either in the routine operation or in fault [11].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Parameter Identification of a Lower Extremity Exoskeleton Using RLS-PSO

et al. 2019

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The lower extremity exoskeleton is a device for auxiliary assistance of human movement. The interaction performance between the exoskeleton and the human is determined by the lower extremity exoskeleton's controller. The performance of the controller is affected by the accuracy of the dynamic equation. Therefore, it is necessary to study the dynamic parameter identification of lower extremity exoskeleton. The existing dynamic parameter identification algorithms for lower extremity exoskeletons are generally based on Least Square (LS). There are some internal drawbacks, such as complicated experimental processes and low identification accuracy. A dynamic parameter identification algorithm based on Particle Swarm Optimization (PSO) with search space defined by Recursive Least Square (RLS) is developed in this investigation. The developed algorithm is named RLS-PSO. By defining the search space of PSO, RLS-PSO not only avoids the convergence of identified parameters to the local minima, but also improves the identification accuracy of exoskeleton dynamic parameters. Under the same experimental conditions, the identification accuracy of RLS-PSO, PSO and LS was quantitatively compared and analyzed. The results demonstrated that the identification accuracy of RLS-PSO is higher than that of LS and PSO. Appl. Sci. 2019, 9, 324 2 of 17 dynamics of the exoskeleton. Third, obtaining the dynamic parameters through dynamic parameter identification algorithms. This method has high accuracy and requires a series of experiments for data acquisition [20][21][22]. The exoskeleton is made up of many non-standard parts, such as pipes and actuators [6][7][8][9][10], which cause the dynamic parameters of the exoskeleton to fluctuate during working. Obviously, it is difficult to obtain the dynamic parameters from the manufacturers or 3D design software. Therefore, parameter identification algorithms are expected to be an effective way to obtain more accurate dynamic parameters [23,24].In order to obtain more accurate dynamic parameters, researchers are increasingly paying attention to the application of parameter identification algorithms for the exoskeleton. Researchers have carried out some preliminary research works. Targeting the swing phase [25,26], Justin et al. identified the dynamic parameters of Berkeley Exoskeleton (BLEEX) [21] by means of Least Square (LS) [27,28]. The joint friction coefficients of BLEEX were identified by static experiments, and the inertial parameters [29] were identified by dynamic experiments. The method did not make full use of the non-correlation between the parameters and the linear relationship between the parameters and the parameterized dynamic equations [30,31]. Therefore, the method only identified one parameter in each experiment. Designing a corresponding identification experiment for each parameter was necessary. The process of parameter identification experiment was very complicated, and the friction parameters on the hip joint lost identifiability because of the limited range of motion. ...

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“…Recently, there has been an increasing tendency to apply admittance control in exoskeletons to allow compliant human-robot interaction without a pre-defined trajectory (Aguirre-Ollinger et al, 2016 ; Ayas and Altas, 2017 ). This control scheme consists of two parts, namely, an admittance filter and an inner position loop.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Admittance Control for an Ankle Exoskeleton Using an EMG-Driven Musculoskeletal Model

Yao

Zhuang

et al. 2018

Front. Neurorobot.

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Various rehabilitation robots have been employed to recover the motor function of stroke patients. To improve the effect of rehabilitation, robots should promote patient participation and provide compliant assistance. This paper proposes an adaptive admittance control scheme (AACS) consisting of an admittance filter, inner position controller, and electromyography (EMG)-driven musculoskeletal model (EDMM). The admittance filter generates the subject's intended motion according to the joint torque estimated by the EDMM. The inner position controller tracks the intended motion, and its parameters are adjusted according to the estimated joint stiffness. Eight healthy subjects were instructed to wear the ankle exoskeleton robot, and they completed a series of sinusoidal tracking tasks involving ankle dorsiflexion and plantarflexion. The robot was controlled by the AACS and a non-adaptive admittance control scheme (NAACS) at four fixed parameter levels. The tracking performance was evaluated using the jerk value, position error, interaction torque, and EMG levels of the tibialis anterior (TA) and gastrocnemius (GAS). For the NAACS, the jerk value and position error increased with the parameter levels, and the interaction torque and EMG levels of the TA tended to decrease. In contrast, the AACS could maintain a moderate jerk value, position error, interaction torque, and TA EMG level. These results demonstrate that the AACS achieves a good tradeoff between accurate tracking and compliant assistance because it can produce a real-time response to stiffness changes in the ankle joint. The AACS can alleviate the conflict between accurate tracking and compliant assistance and has potential for application in robot-assisted rehabilitation.

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An admittance shaping controller for exoskeleton assistance of the lower extremities

Cited by 35 publications

References 60 publications

A Review on Lower Limb Rehabilitation Exoskeleton Robots

A Review on Lower Limb Rehabilitation Exoskeleton Robots

Dynamic Parameter Identification of a Lower Extremity Exoskeleton Using RLS-PSO

Adaptive Admittance Control for an Ankle Exoskeleton Using an EMG-Driven Musculoskeletal Model

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