Precision force control of an underactuated stance leg exoskeleton for human performance augmentation

Author(s):  
Shan Chen ◽  
Tenghui Han ◽  
Fangfang Dong ◽  
Lei Lu ◽  
Haijun Liu ◽  
...  

Lower limb exoskeleton which augments the human performance is a wearable human–machine integrated system used to assist people carrying heavy loads. Recently, underactuated lower limb exoskeleton systems with some passive joints become more and more attractive due to the advantages of smaller weight, lower system energy consumption and lower cost. However, because of the less of control inputs, the existed control methods of fully actuated exoskeletons cannot be extended to underactuated systems, which makes the robust controller design of underactuated lower limb exoskeletons becomes more challenged. This article focuses on the high-performance human–machine interaction force control design of underactuated lower limb exoskeletons with passive ankle joint. In order to solve the reduction of control inputs, the holonomic constraint from the wearer is considered, which help transform the dynamics of 3-degree-of-freedom underactuated exoskeleton in joint space into a 2-degree-of-freedom fully actuated system in Cartesian space. A two-level interaction force controller using adaptive robust control algorithm is proposed to effectively address the negative effect of various model uncertainties and external disturbances. In order to facilitate the control parameter selection, a gain tuning method is also presented. Comparative simulations are carried out, which indicate that the proposed two-level interaction force controller achieves smaller interaction force and better robust performance to various modeling errors and disturbances.

Author(s):  
Shan Chen ◽  
Bin Yao ◽  
Zheng Chen ◽  
Xiaocong Zhu ◽  
Shiqiang Zhu

The control objective of exoskeleton for human performance augmentation is to minimize the human machine interaction force while carrying external loads and following human motion. This paper addresses the dynamics and the interaction force control of a 1-DOF hydraulically actuated joint exoskeleton. A spring with unknown stiffness is used to model the human-machine interface. A cascade force control method is adopted with high-level controller generating the reference position command while low level controller doing motion tracking. Adaptive robust control (ARC) algorithm is developed for both two controllers to deal with the effect of parametric uncertainties and uncertain nonlinearities of the system. The proposed adaptive robust cascade force controller can achieve small human-machine interaction force and good robust performance to model uncertainty which have been validated by experiment.


2021 ◽  
pp. 107754632110317
Author(s):  
Jin Tian ◽  
Liang Yuan ◽  
Wendong Xiao ◽  
Teng Ran ◽  
Li He

The main objective of this article is to solve the trajectory following problem for lower limb exoskeleton robot by using a novel adaptive robust control method. The uncertainties are considered in lower limb exoskeleton robot system which include initial condition offset, joint resistance, structural vibration, and environmental interferences. They are time-varying and have unknown boundaries. We express the trajectory following problem as a servo constraint problem. In contrast to conventional control methods, Udwadia–Kalaba theory does not make any linearization or approximations. Udwadia–Kalaba theory is adopted to derive the closed-form constrained equation of motion and design the proposed control. We also put forward an adaptive law as a performance index whose type is leakage. The proposed control approach ensures the uniform boundedness and uniform ultimate boundedness of the lower limb exoskeleton robot which are demonstrated via the Lyapunov method. Finally, simulation results have shown the tracking effect of the approach presented in this article.


2011 ◽  
Vol 48-49 ◽  
pp. 589-592 ◽  
Author(s):  
Shi Xiang Tian ◽  
Sheng Ze Wang

In this paper, a novel hybrid position/force controller has been proposed for a three degree of freedom (3-DOF) of robot trajectory following that is required to switch between position and force control. The whole controller consists of two components: a positional controller and a force controller. Depending on whether the end-effector is in free space or in contact with the environments during work, the two subcontrollers run simultaneously to guide the manipulator tracking in free space and constraint environments. After the principle and stability of the controller are briefly analyzed, simulation results verify that the proposed controller attains a high performance.


2017 ◽  
Vol 105 ◽  
pp. 183-190 ◽  
Author(s):  
A.P.P.A. Majeed ◽  
Z. Taha ◽  
A.F.Z. Abidin ◽  
M.A. Zakaria ◽  
I.M. Khairuddina ◽  
...  

2012 ◽  
Vol 8 (1) ◽  
pp. 427-432
Author(s):  
Zhi-Yong Tang ◽  
Zhen-Zhong Tan ◽  
Ming-Yi Yang ◽  
Zhong-Cai Pei

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Zhenlei Chen ◽  
Qing Guo ◽  
Huiyu Xiong ◽  
Dan Jiang ◽  
Yao Yan

AbstractIn this study, a humanoid prototype of 2-DOF (degrees of freedom) lower limb exoskeleton is introduced to evaluate the wearable comfortable effect between person and exoskeleton. To improve the detection accuracy of the human-robot interaction torque, a BPNN (backpropagation neural networks) is proposed to estimate this interaction force and to compensate for the measurement error of the 3D-force/torque sensor. Meanwhile, the backstepping controller is designed to realize the exoskeleton's passive position control, which means that the person passively adapts to the exoskeleton. On the other hand, a variable admittance controller is used to implement the exoskeleton's active follow-up control, which means that the person's motion is motivated by his/her intention and the exoskeleton control tries best to improve the human-robot wearable comfortable performance. To improve the wearable comfortable effect, serval regular gait tasks with different admittance parameters and step frequencies are statistically performed to obtain the optimal admittance control parameters. Finally, the BPNN compensation algorithm and two controllers are verified by the experimental exoskeleton prototype with human-robot cooperative motion.


2010 ◽  
Vol 30 (4) ◽  
pp. 486-499 ◽  
Author(s):  
Gabriel Aguirre-Ollinger ◽  
J. Edward Colgate ◽  
Michael A. Peshkin ◽  
Ambarish Goswami

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