WeD-3-1 PREDICTION OF USER'S MOTION INTENTION IN PERCEPTION-ASSIST FOR A LOWER-LIMB POWER-ASSIST EXOSKELETON ROBOT : ZMP-BASED PREDICTION

2015 ◽  
Vol 2015 (0) ◽  
pp. _WeD-3-1-1-_WeD-3-1-3 ◽  
Author(s):  
Kazuo Kiguchi ◽  
Akito Arai
Keyword(s):  
Lower Limb ◽  
Exoskeleton Robot ◽  
Motion Intention

scholarly journals Lower Limb Motion Recognition Method Based on Improved Wavelet Packet Transform and Unscented Kalman Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Xin Shi ◽  
Pengjie Qin ◽  
Jiaqing Zhu ◽  
Shuyuan Xu ◽  
Weiren Shi
Keyword(s):  
Neural Network ◽  
Lower Extremity ◽  
Lower Limb ◽  
Wavelet Packet ◽  
Sliding Window ◽  
Recognition Algorithm ◽  
Lower Limbs ◽  
Exoskeleton Robot ◽  
Motion Intention ◽  
Semg Signals

Exoskeleton robot is a typical application to assist the motion of lower limbs. To make the lower extremity exoskeleton more flexible, it is necessary to identify various motion intentions of the lower limbs of the human body. Although more sEMG sensors can be used to identify more lower limb motion intention, with the increase in the number of sensors, more and more data need to be processed. In the process of human motion, the collected sEMG signal is easy to be interfered with noise. To improve the practicality of the lower extremity exoskeleton robot, this paper proposed a wavelet packet transform- (WPT-) based sliding window difference average filtering feature extract algorithm and the unscented Kalman neural network (UKFNN) recognition algorithm. We established an sEMG energy feature model, using a sliding window difference average filtering method to suppress noise interference and extracted stable feature values and using UKF filtering to optimize the neural network weights to improve the adaptability and accuracy of the recognition model. In this paper, we collected the sEMG signals of three muscles to identify six lower limb motion intentions. The average accuracy of 94.83% is proposed in this paper. Experiments show that the algorithm improves the accuracy and anti-interference of motion intention recognition of lower limb sEMG signals. The algorithm is superior to the backpropagation neural network (BPNN) recognition algorithm in the lower limb motion intention recognition and proves the effectiveness, novelty, and reliability of the method in this paper.

Design of M-G modal space sliding mode control for lower limb exoskeleton robot driven by electrical actuators

Mechatronics ◽  
2021 ◽  
Vol 78 ◽  
pp. 102610
Author(s):  
Jinsong Zhao ◽  
Tao Yang ◽  
Zhilei Ma ◽  
Chifu Yang ◽  
Zhipeng Wang ◽  
...  
Keyword(s):  
Sliding Mode Control ◽  
Lower Limb ◽  
Sliding Mode ◽  
Lower Limb Exoskeleton ◽  
Mode Control ◽  
Exoskeleton Robot ◽  
Modal Space

Trajectory following control of lower limb exoskeleton robot based on Udwadia–Kalaba theory

2021 ◽  
pp. 107754632110317
Author(s):  
Jin Tian ◽  
Liang Yuan ◽  
Wendong Xiao ◽  
Teng Ran ◽  
Li He
Keyword(s):  
Lower Limb ◽  
Control Method ◽  
Uniform Boundedness ◽  
Adaptive Robust Control ◽  
Structural Vibration ◽  
Control Approach ◽  
Lower Limb Exoskeleton ◽  
Exoskeleton Robot ◽  
Constraint Problem ◽  
Trajectory Following

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.

Adaptive Learning Inertia Control of Lower Limb Exoskeleton Robot.

2021 ◽  
Author(s):  
Muhammad Arsalan ◽  
Muhammad Tufail ◽  
SG Khan ◽  
Syed Humayoon Shah
Keyword(s):  
Adaptive Learning ◽  
Lower Limb ◽  
Lower Limb Exoskeleton ◽  
Exoskeleton Robot ◽  
Inertia Control

scholarly journals Design and Voluntary Motion Intention Estimation of a Novel Wearable Full-Body Flexible Exoskeleton Robot

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Chunjie Chen ◽  
Xinyu Wu ◽  
Du-xin Liu ◽  
Wei Feng ◽  
Can Wang
Keyword(s):  
Gait Cycle ◽  
Human Motion ◽  
Experimental Result ◽  
Motion Patterns ◽  
Exoskeleton Robot ◽  
Gait Phase ◽  
Phase Recognition ◽  
Motion Intention ◽  
Full Body ◽  
Testing Set

The wearable full-body exoskeleton robot developed in this study is one application of mobile cyberphysical system (CPS), which is a complex mobile system integrating mechanics, electronics, computer science, and artificial intelligence. Steel wire was used as the flexible transmission medium and a group of special wire-locking structures was designed. Additionally, we designed passive joints for partial joints of the exoskeleton. Finally, we proposed a novel gait phase recognition method for full-body exoskeletons using only joint angular sensors, plantar pressure sensors, and inclination sensors. The method consists of four procedures. Firstly, we classified the three types of main motion patterns: normal walking on the ground, stair-climbing and stair-descending, and sit-to-stand movement. Secondly, we segregated the experimental data into one gait cycle. Thirdly, we divided one gait cycle into eight gait phases. Finally, we built a gait phase recognition model based on k-Nearest Neighbor perception and trained it with the phase-labeled gait data. The experimental result shows that the model has a 98.52% average correct rate of classification of the main motion patterns on the testing set and a 95.32% average correct rate of phase recognition on the testing set. So the exoskeleton robot can achieve human motion intention in real time and coordinate its movement with the wearer.

scholarly journals Design and simulation of PID controller for lower limb exoskeleton robot

2018 ◽  
Author(s):  
Munadi ◽  
M. S. Nasir ◽  
M. Ariyanto ◽  
Norman Iskandar ◽  
J. D. Setiawan
Keyword(s):  
Lower Limb ◽  
Pid Controller ◽  
Lower Limb Exoskeleton ◽  
Exoskeleton Robot

scholarly journals Development of human lower limbs exoskeleton robot for medical rehabilitation

2021 ◽  
pp. 91-97
Author(s):  
E. A. Kotov ◽  
◽  
A. D. Druk ◽  
D. N. Klypin ◽  
◽  
...  
Keyword(s):  
Control System ◽  
Experimental Research ◽  
Lower Limb ◽  
Lower Limbs ◽  
Medical Rehabilitation ◽  
Lower Limb Exoskeleton ◽  
Exoskeleton Robot ◽  
Controlled Motion ◽  
And Control

The article deals with the solution of the problem of optimizing the characteristics of controlled motion of human lower limb exoskeleton robot for improving medical rehabilitation. The aim of the work is to develop a rehabilitation device capable of providing controlled motion in two planes, as well as maintaining balance without loss of mobility. The design and control system of a rehabilitation trainer designed for performing mechanotherapy of the lower limbs of patients with locomotive disorders are proposed and characterized. The developed system has a number of significant differences from analogues and can be recommended for experimental research on patients with impaired locomotive functions

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