The Berkeley Lower Extremity Exoskeleton Project

Kazerooni, H.

doi:10.1007/11552246_28

Cited by 15 publications

(8 citation statements)

References 4 publications

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“…Recently, the importance of the natural dynamics of the human body [3], energy input [4] and comfort of human-robot interactions [5][6][7] has been given increased attention in exoskeleton applications. In these approaches to exoskeleton assistance, torque control is crucial for performance.…”

Section: Introductionmentioning

confidence: 99%

“…Many low-level control methods have been employed for torque or position tracking in exoskeletons, including classical feedback control [7], model-based control [6], adaptive control [11] and iterative learning control [12]. However, it remains unclear which method has the best performance, or how performance may vary with high-level controllers.…”

Section: Introductionmentioning

confidence: 99%

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Experimental comparison of torque control methods on an ankle exoskeleton during human walking

Zhang

Cheah

Collins

2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

100

123

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Abstract-Few comparisons have been performed across torque controllers for exoskeletons, and differences among devices have made interpretation difficult. In this study, we compared the torque-tracking performance of nine control methods, including variations on classical feedback control, modelbased control, adaptive control and iterative learning. Each was tested with four high-level controllers that determined desired torque based on time, joint angle, a neuromuscular model, or electromyography. Controllers were implemented on a tethered ankle exoskeleton with series elastic actuation. Measurements were taken while one human subject walked on a treadmill at 1.25 m·s -1 for one hundred steady-state steps. The combination of proportional derivative control with iterative learning resulted in the lowest errors for all high-level controllers. With timebased desired torque, rms errors were 0.6 N·m (1.3% of peak torque) step by step, and 0.1 N·m (0.2%) on average. These results indicate that model-free, integration-free feedback control is suited to the uncertain dynamics of the human-robot system, while iterative learning is effective in the cyclic task of walking.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Experimental comparison of torque control methods on an ankle exoskeleton during human walking

Zhang

Cheah

Collins

2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

100

123

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“…In order to do this, the morphological analysis gives the information about movements, lengths of the members, number of segments and its respective masses. Another justification is that the biological inspirations permits the creation of mechanical systems that are more compacted and reliable and also, more energy-efficient (Kazerooni, 2006). In the nature, the biological designs of the living organisms allows the adaptation with the environment; the evolution mechanisms have made transformation into the body as the circumstances rules, for this reason the diversity of organic designs and also, the extinction www.intechopen.com of those species that didn't could adapt to its environment.…”

Section: Forward Kinematicsmentioning

confidence: 99%

“…(Right), prosthetic robot of upper parts (Rehabilitation Institute of Chicago, 2006) The exoskeleton take part of the orthotics wearable robots field, it's a robotic structure (actually could be considered a mechatronics structure due to the integration of different www.intechopen.com electronics equipments like sensors, actuators and an intelligent controller that are integrated into the mechanical system) fixed to the human body that follows along the movements of the wearer. At the present time are presented several models of exoskeletons: those that are attached to the legs was called lower part exoskeletons (Kazerooni, 2006) and also exists those that are attached to the arms, that are denominated upper part exoskeletons (Perry et al, 2007). In both structures, its applications goes from the medical camp to military applications; with functions like help handicap patients assisting on the body member's movement recuperation (Gopura & Kiguchi, 2007) or helping to the soldiers to carry heavier loads (Kazerooni, 2005).…”

Section: Introductionmentioning

confidence: 99%

Tracking Control in an Upper Arm Exoskeleton with Differential Flatness

Veslin¹,

Dutra²,

Slama³

et al. 2012

Robotic Systems - Applications, Control and Programming

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“…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.…”

mentioning

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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The Berkeley Lower Extremity Exoskeleton Project

Cited by 15 publications

References 4 publications

Experimental comparison of torque control methods on an ankle exoskeleton during human walking

Experimental comparison of torque control methods on an ankle exoskeleton during human walking

Tracking Control in an Upper Arm Exoskeleton with Differential Flatness

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

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