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Super Twisting control of Passive Gait Training Exoskeleton Driven by Pneumatic Muscles

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Super Twisting Control of Passive Gait Training Exoskeleton ...

International Symposium on Micromechatronics and Human Science (MHS)

作者： Mengshi Zhang Jian Huang Yu Cao Hai-Tao Zhang Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781728126753

ISBN: (纸本)9781728126760

Pneumatic Muscles (PMs) - driven exoskeleton has a promising prospect in the field of rehabilitation and assistance, because of the PMs intrinsic features of compliance and high force-to-weight ratio. However, the precise control of PMs-driven exoskeleton still remains a challenging problem due to the hysteresis, time-varying parameters of PMs and complexity of the mechanism. To solve this problem, this paper presents a super twisting controller (STC), essentially a second-order sliding mode control, to realize the human gait tracking control of lower limb exoskeleton. As a result, the disturbances and uncertainties of the system can be handled, and the chattering caused by the traditional sliding mode control (SMC) could be eliminated. The stability of the closed-loop system is ensured according to the Lyapunov theorem. In addition, we conduct experiments in real exoskeleton system and the results illustrate the validity of the super twisting algorithm..

关键词： Uncertainty Heuristic algorithms Exoskeletons Muscles Stability analysis Closed loop systems Sliding mode control

Metric Learning Based Rolling Bearing Faults Diagnosis with Curvelet Transform

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Metric Learning Based Rolling Bearing Faults Diagnosis with ...

2019 3rd International Conference on Machine Vision and Information Technology (CMVIT 2019)

作者： Ziming Lu Jiang-Wen Xiao Zhengyi Huang School of Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control(Huazhong University of Science and Technology) Ministry of Education

Rolling bearing faults are among the primary causes of breakdown in mechanical equipment. Aiming at the vibration signals of rolling bearing which are non-stationary and easy to be disturbed by noise, a novel fault diagnosis method based on curvelet transform and metric learning is proposed. This method consists of 3 parts. The first one is feature engineering which includes reshaping the original timing features of rolling bearings, employing curvelet transform to transform reshaped features and making its coefficients as the new features. Curvelet transform can analyse the original signal from many angles. The second one is employing metric learning to map these new features into special embedding space. The last one is applying KNN classifier to detect the rolling bearing faults. Metric learning can effectively improve the performance of KNN by learning a mapping matrix to modify the distribution of samples. The proposed method overcomes the problems such as the subjectivity and blindness of manual feature extraction, poor coupling in each stage and sensitive to the effect of noise. Extensive simulations based on several data-sets show that the our method has better performance on bearing fault diagnosis than traditional methods.

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Optimize TSK fuzzy systems for regression problems: Mini-batch gradient descent with regularization, droprule, and adabound (MBGD-RDA)

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arXiv 2019年

作者： Wu, Dongrui Yuan, Ye Huang, Jian Tan, Yihua Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

Takagi-Sugeno-Kang (TSK) fuzzy systems are very useful machine learning models for regression problems. However, to our knowledge, there has not existed an efficient and effective training algorithm that ensures their generalization performance, and also enables them to deal with big data. Inspired by the connections between TSK fuzzy systems and neural networks, we extend three powerful neural network optimization techniques, i.e., mini-batch gradient descent (MBGD), regularization, and AdaBound, to TSK fuzzy systems, and also propose three novel techniques (DropRule, DropMF, and Drop-Membership) specifically for training TSK fuzzy systems. Our final algorithm, MBGD with regularization, DropRule and AdaBound (MBGD-RDA), can achieve fast convergence in training TSK fuzzy systems, and also superior generalization performance in testing. It can be used for training TSK fuzzy systems on datasets of any size;however, it is particularly useful for big datasets, on which currently no other efficient training algorithms exist. Copyright © 2019, The Authors. All rights reserved.

关键词： Gradient methods

Different Set Domain Adaptation for Brain-Computer Interfaces: A Label Alignment Approach

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arXiv 2019年

作者： He, He Wu, Dongrui Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

A brain-computer interface (BCI) system usually needs a long calibration session for each new subject/task to adjust its parameters, which impedes its transition from the laboratory to real-world applications. Domain adaptation, which leverages labeled data from auxiliary subjects/tasks (source domains), has demonstrated its effectiveness in reducing such calibration effort. Currently, most domain adaptation approaches require the source domains to have the same feature space and label space as the target domain, which limits their applications, as the auxiliary data may have different feature spaces and/or different label spaces. This paper considers different set domain adaptation for BCIs, i.e., the source and target domains have different label spaces. We introduce a practical setting of different label sets for BCIs, and propose a novel label alignment (LA) approach to align the source label space with the target label space. It has three desirable properties: 1) LA only needs as few as one labeled sample from each class of the target subject;2) LA can be used as a preprocessing step before different feature extraction and classification algorithms;and, 3) LA can be integrated with other domain adaptation approaches to achieve even better performance. Experiments on two motor imagery datasets demonstrated the effectiveness of LA. Copyright © 2019, The Authors. All rights reserved.

关键词： Brain computer interface

Ripple-Oriented Degree Distribution of Rateless Codes: A Backward Design Approach for Finite Block Lengths

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IEEE Transactions on Vehicular Technology 2025年

作者： Huang, Haobo Yin, Junpeng Fu, Yusun Wang, Zihao Shanghai Jiao Tong University Ningbo Artificial Intelligence Institute Ningbo315000 China Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200240 China Ministry of Education of China School of Electronic Information and Electrical Engineering the Key Laboratory of System Control and Information Processing Shanghai200240 China Shanghai Jiao Tong University Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

Rateless codes are a class of error correction codes designed for efficient and reliable data transmission. Luby transform (LT) codes are the first realization of rateless codes. The degree distribution is crucial for the performance of LT codes, but the existing degree distributions do not perform well in finite block lengths, especially in short block lengths. Different from the forward design, such as robust soliton distribution (RSD), we provide a backward design of degree distribution for LT codes in finite block lengths, which starts from the detailed design of ripple size evolution and then arrives at the corresponding degree distribution. This design pattern addresses the limitation of the traditional forward design that cannot guarantee a high probability of successful decoding under finite block lengths. Specifically, we argue that the ripple should increase and then decrease during the decoding process by analyzing the trade-off between redundancy and ripple disappearance. Therefore, a new random walk model is proposed based on adjustable release probability to describe the ideal ripple size evolution. Furthermore, we present a novel design framework to achieve the desired ripple size evolution and the resulting degree distribution is called ripple-oriented degree distribution (RODD). Simulation results show that RODD is superior to other degree distributions in decoding success rate, block error rate, complexity, etc. 1967-2012 IEEE.

关键词： Size distribution

RGB-based combustion state classification and stability analysis of SOFC afterburner: effects of fuel utilization and steam-to-carbon ratio

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Fuel 2025年 402卷

作者： Jingjing Wang Shuyu Zhang Jingang Lai Jiashu Jin Chun Zou Jung-Sik Kim Zhonghua Deng Yuanwu Xu Xi Li School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry Huazhong University of Science and Technology Wuhan 430074 China State Key Laboratory of Coal Combustion Huazhong University of Science and Technology Wuhan 430074 China Sino-French Engineer School/School of General Engineering Beihang University Beijing 100191 China Wuhan Huamao Automation Co. Ltd. Wuhan 430205 China School of Artificial Intelligence and Automation Wuhan University of Science and Technology Wuhan 430081 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen 518055 China

Solid oxide fuel cell (SOFC) systems efficiently convert chemical energy into electricity but produce high-temperature exhaust gases containing unreacted fuels. The afterburner combusts these residual fuels to enhance heat recovery and system efficiency, with its performance influenced by stack fuel composition variations. This study investigates how fuel utilization rate ( U f ) and steam-to-carbon ratio (S/C) affect afterburner combustion characteristics in a 1 kW methane-reformed SOFC system. Results showed maximum afterburner temperature decreased from 981.3 K to 835 K as U f increased to 0.6, while S/C minimally impacted temperature distribution. A dimensionless stability index was developed to quantitatively assess combustion performance. An image-based analysis approach extracted five key parameters describing flame characteristics under varying conditions. These parameters supported the development of a three-tier hierarchical classification framework to identify weak, stable, and unstable combustion states, achieving an overall recognition accuracy of 74.5 %. These findings provide insights into SOFC operating parameters, residual stack fuel compositions, and afterburner stability relationships. The proposed assessment methodology offers guidance for optimizing thermal management and improving energy recovery efficiency in SOFC systems.

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Optimize TSK fuzzy systems for big data classification problems: Bag of tricks

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arXiv 2019年

作者： Cui, Yuqi Wu, Dongrui Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

Takagi-Sugeno-Kang (TSK) fuzzy systems are flexible and interpretable machine learning models;however, they may not be easily applicable to big data problems, especially when the size and the dimensionality of the data are both large. This paper proposes a mini-batch gradient descent (MBGD) based algorithm to efficiently and effectively train TSK fuzzy systems for big data classification problems. It integrates three novel techniques: 1) uniform regularization (UR), which is a regularization term added to the loss function to make sure the rules have similar average firing levels, and hence better generalization performance;2) random percentile initialization (RPI), which initializes the membership function parameters efficiently and reliably;and, 3) batch normalization (BN), which extends BN from deep neural networks to TSK fuzzy systems to speedup the convergence and improve generalization. Experiments on nine datasets from various application domains, with varying size and feature dimensionality, demonstrated that each of UR, RPI and BN has its own unique advantages, and integrating all three together can achieve the best classification performance. Copyright © 2019, The Authors. All rights reserved.

关键词： Gradient methods

An MR Safe Rotary Encoder Based on Eccentric Sheave and FBG Sensors

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An MR Safe Rotary Encoder Based on Eccentric Sheave and FBG ...

IEEE International Conference on Robotics and Automation (ICRA)

作者： Shaoping Huang Anzhu Gao Zicong Wu Chuqian Lou Yanjun Wang Guang-Zhong Yang The Institute of Medical Robotics and School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China Shanghai Jiao Tong University The Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China The Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China The Imperial College London and Empa-Swiss Federal Laboratories for Materials Science and Technology

MRI-guided robotic systems are emerging platforms for minimally invasive intervention because of high positioning accuracy and excellent tissue contrast. MR safe encoders are critical components for closed-loop robotic control. This paper develops an MR safe absolute rotary encoder based on eccentric sheave and FBG sensors. The eccentric sheave transforms the rotational motion of the shaft to the bending deflection of the beam on which FBG sensors are integrated. A model is built by establishing the relationship of the kinematics of the sheave, the mechanical properties of the beam with unknown length, and the strain model of two Fiber Bragg Grating (FBG) sensors. A Pseudo-Rigid Body (PRB) 3R model is used to solve a set of constrained equations for accurate rotary encoding. A prototype is built to calibrate the parameters and validate the accuracy of the encoder and its MR compatibility. Results show that the maximum angular error is 1.6°, and the RMS error is 0.46°. MRI shows that no noticeable artifacts are observed, and the Signal to Noise Ratio (SNR) is not affected. The results demonstrate the potential of the proposed method for it to be integrated with MR safe robots with easy fabrication, compact structures, and continuous measurement.

关键词： Mechanical sensors Shafts Analytical models Kinematics Transforms Fiber gratings Robot sensing systems

Canonical correlation analysis (CCA) based multi-view classification: An overview

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arXiv 2019年

作者： Guo, Chenfeng Wu, Dongrui The Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

Multi-view learning (MVL) is a strategy for fusing data from different sources or subsets. Canonical correlation analysis (CCA) is very important in MVL, whose main idea is to map data from different views onto a common space with maximum correlation. Traditional CCA can only be used to calculate the linear correlation of two views. Besides, it is unsupervised and the label information is wasted. Many nonlinear, supervised, or generalized extensions have been proposed to overcome these limitations. However, to our knowledge, there is no overview for these approaches. This paper provides an overview of many representative CCA-based MVL approaches. Copyright © 2019, The Authors. All rights reserved.

关键词： Correlation methods