Modelling of Magnetorheological Damper for Intelligent Bionic Leg and Simulation of Knee Joint Movement Control

Xie, Hualong; Liu, Z. B.; Yang, Jian

doi:10.2507/ijsimm15(1)co2

Cited by 23 publications

(16 citation statements)

References 17 publications

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“…ANNs are biologically inspired; they mimic the human brain processes and have emerged as one of the tools that can handle the classification problem. ANNs have been used to solve many problems in the Economic, Social and Engineering Sciences, as well as Health Sciences [43,44,45]. They are made of constitutive units called neurons, which are interconnected to each other with connecting links, where each link has a weight that is multiplied by the signal transmitted in the network [46].…”

Section: Methodsmentioning

confidence: 99%

Classifying Parkinson’s Disease Based on Acoustic Measures Using Artificial Neural Networks

Berus

Klančnik

Brezočnik

et al. 2018

Sensors

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In recent years, neural networks have become very popular in all kinds of prediction problems. In this paper, multiple feed-forward artificial neural networks (ANNs) with various configurations are used in the prediction of Parkinson’s disease (PD) of tested individuals, based on extracted features from 26 different voice samples per individual. Results are validated via the leave-one-subject-out (LOSO) scheme. Few feature selection procedures based on Pearson’s correlation coefficient, Kendall’s correlation coefficient, principal component analysis, and self-organizing maps, have been used for boosting the performance of algorithms and for data reduction. The best test accuracy result has been achieved with Kendall’s correlation coefficient-based feature selection, and the most relevant voice samples are recognized. Multiple ANNs have proven to be the best classification technique for diagnosis of PD without usage of the feature selection procedure (on raw data). Finally, a neural network is fine-tuned, and a test accuracy of 86.47% was achieved.

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Section: Methodsmentioning

confidence: 99%

Classifying Parkinson’s Disease Based on Acoustic Measures Using Artificial Neural Networks

Berus

Klančnik

Brezočnik

et al. 2018

Sensors

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show abstract

“…In addition to the FSM-based approaches, Xie et al applied the neural network in the control of the robotic knee prosthesis to improve the walking gait of a four-bar mechanism robotic knee prosthesis actuated via the magnetorheological fluid damper [62] . Moreover, Ekkachai et al also proposed a robotic knee prosthesis neural network predictive control method based on particle swarm optimization [63] .…”

Section: Automatic Controlmentioning

confidence: 99%

Review of Recent Progress in Robotic Knee Prosthesis Related Techniques: Structure, Actuation and Control

Sun

Tang

et al. 2021

J Bionic Eng

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As the essential technology of human-robotics interactive wearable devices, the robotic knee prosthesis can provide above-knee amputations with functional knee compensations to realize their physical and psychological social regression. With the development of mechanical and mechatronic science and technology, the fully active knee prosthesis that can provide subjects with actuating torques has demonstrated a better wearing performance in slope walking and stair ascent when compared with the passive and the semi-active ones. Additionally, with intelligent human-robotics control strategies and algorithms, the wearing effect of the knee prosthesis has been greatly enhanced in terms of stance stability and swing mobility. Therefore, to help readers to obtain an overview of recent progress in robotic knee prosthesis, this paper systematically categorized knee prostheses according to their integrated functions and introduced related research in the past ten years (2010–2020) regarding (1) mechanical design, including uniaxial, four-bar, and multi-bar knee structures, (2) actuating technology, including rigid and elastic actuation, and (3) control method, including mode identification, motion prediction, and automatic control. Quantitative and qualitative analysis and comparison of robotic knee prosthesis-related techniques are conducted. The development trends are concluded as follows: (1) bionic and lightweight structures with better mechanical performance, (2) bionic elastic actuation with energy-saving effect, (3) artificial intelligence-based bionic prosthetic control. Besides, challenges and innovative insights of customized lightweight bionic knee joint structure, highly efficient compact bionic actuation, and personalized daily multi-mode gait adaptation are also discussed in-depth to facilitate the future development of the robotic knee prosthesis.

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“…The artificial leg is required to simulate the normal gait of the remaining leg in an accurate manner, and provide the bionic leg with the precise gait following trajectory. Therefore, the control of the artificial leg is critical to the development of the entire BRHL [16,18]. To satisfy the requirements on the artificial leg, this paper adopts the PAMs based on highspeed on-off valve control to replace the "flexor and extensor muscle groups"of the biceps and quadriceps of the human leg, and drives the joint movement.…”

Section: Introductionmentioning

confidence: 99%

Design, Modeling and Control of Bionic Knee in Artificial Leg

Xie

Xie²,

Li³

2019

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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The biped robot with heterogeneous legs (BRHL) greatly facilitates the development of intelligent lower-limb prosthesis (ILLP). In the BRHL, the remaining leg of the amputee is simulated by an artificial leg, which provides the bionic leg with the precise gait following trajectory. Therefore, the artificial leg must closely mimic the features of the human leg. After analyzing the motion mechanism of the human knee, this paper designs a four-link bionic knee in light of the coexistence of rolling and sliding between the femur, the meniscus and the tibia. Drawing on the driving mechanism of leg muscles, two pneumatic artificial muscles (PAMs) were adopted to serve as the extensor and flexor muscles on the thigh. The two PAMs move in opposite direction, driving the knee motions in the artificial leg. To overcome the complexity of traditional PAM modelling methods, the author set up a PAM feature test platform to disclose the features of the PAMs, and built static and dynamic nonlinear mathematical models of the PAMs based on the test data. Next, a proportional-integral-derivative (PID) closed loop controller and sliding mode controller was designed for the bionic knee, referring to the kinetics equation of the knee. Through experimental simulation, it is confirmed that the proposed controller can accurately control the position of the four-link bionic knee, and that the designed bionic knee and PAM driving mode are both correct.

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Modelling of Magnetorheological Damper for Intelligent Bionic Leg and Simulation of Knee Joint Movement Control

Cited by 23 publications

References 17 publications

Classifying Parkinson’s Disease Based on Acoustic Measures Using Artificial Neural Networks

Classifying Parkinson’s Disease Based on Acoustic Measures Using Artificial Neural Networks

Review of Recent Progress in Robotic Knee Prosthesis Related Techniques: Structure, Actuation and Control

Design, Modeling and Control of Bionic Knee in Artificial Leg

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