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arXiv 2020年

作者： Peng, Gang Yang, Jin Khyam, Mohammad Omar Key Laboratory of Image Processing and Intelligent Control Ministry of EducationB School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Automation South East University Nanjing China School of Engineering and Technology Central Queensland University Melbourne Australia

To improve the efficiency of deep reinforcement learning (DRL)-based methods for robot manipulator trajectory planning in random working environments, we present three dense reward functions. These rewards differ from the traditional sparse reward. First, a posture reward function is proposed to speed up the learning process with a more reasonable trajectory by modeling the distance and direction constraints, which can reduce the blindness of exploration. Second, a stride reward function is proposed to improve the stability of the learning process by modeling the distance and movement distance of joint constraints. Finally, in order to further improve learning efficiency, we are inspired by the cognitive process of human behavior and propose a stage incentive mechanism, including a hard stage incentive reward function and a soft stage incentive reward function. Extensive experiments show that the soft stage incentive reward function is able to improve the convergence rate by up to 46.9% with the state-of-the-art DRL methods. The percentage increase in the convergence mean reward was 4.4-15.5% and the percentage decreases with respect to standard deviation were 21.9-63.2%. In the evaluation experiments, the success rate of trajectory planning for a robot manipulator reached 99.6%. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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Neighborhood Interval Observer Based Coordination control for Multi-agent Systems with Disturbances ⁎

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IFAC-PapersOnLine 2020年第2期53卷 10994-10999页

作者： Xiaoling Wang Guo-Ping Jiang Wen Yang Housheng Su Xiaofan Wang College of Automation Nanjing University of Posts and Telecommunications Nanjing 210023 China and also with Jiangsu Engineering Lab for IOT Intelligent Robots (IOTRobot) Nanjing 210023 China Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Shanghai 200237 China School of Artifcial Intelligence and Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Luoyu Road 1037 Wuhan 430074 China Department of Automation Shanghai University Shanghai 200072 China

This paper focuses on multi-agent systems with uncertain disturbances, in which only the bounding functions on the disturbances and the bounds on the initial state of each agent are known. By designing a neighborhood interval observer for this kind of multi-agent system, the estimation of the sum of the relative state of each agent associated with itself and its neighbors is frstly realized. Then, on the basis of these estimated information, local control algorithm is designed to drive the system to achieve bounded consensus.

关键词： Multi-agent system disturbance neighborhood interval observer bounded consensus

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control-oriented fault detection of solid oxide fuel cell system unknown input on fuel supply

Control-oriented fault detection of solid oxide fuel cell sy...

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作者： Wu, Xiao-long Xu, Yuan-wu Xue, Tao Shuai, Junchao Jiang, Jianhua Deng, Zhonghua Fu, Xiaowei Li, Xi School of Automation Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science & Technology Wuhan China College of Computer Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan China Wuhan Industrial Economic Development Research Center Wuhan China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China

As the solid oxide fuel cell (SOFC) system work environment is a high-temperature environment for a long time, it is difficult to obtain the SOFC stack internal state change directly. When the fault occurs, it is difficult to determine where the fault occurs. Moreover, the existing literature ignores the impact of faults, which creates many problems for SOFC system control. Therefore, a state observer-based fault detection method, which is used to detect the input flow sensor fault and the fuel input fault, is proposed. Their advantage is that they do not need data processing. To realize the fault detection, the observer is used to track the changes of SOFC stack chamber temperature. To obtain the observer estimation parameter, an approach from the actual stack structure parameters is employed to approximate the observer parameters. The results show the proposed fault detect method can judge fuel input fault type quickly and shield the disturbances signals from the sensor effectively. The proposed method also can be used to other operating points or air input fault. © 2018 Chinese Automatic control Society and John Wiley & Sons Australia, Ltd

关键词： Fault detection

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Pining control Algorithm for Complex Networks

Pining Control Algorithm for Complex Networks

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第三十八届中国控制会议

作者： Bingjun Wang Hui Liu Jiangnqiao Xu Jiaqi Liu School of Artificial Intelligence and Automation & Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Huazhong University of Science and Technology

In this paper, we quote the concept of resistance distance for Pinning control, and discuss the relationship between the upper bound of the minimum eigenvalue of the grounded Laplacian Matrix and the resistance distance of the Pinning control for one single node. Our simulation results in various networks like BA network, ER network, NW network etc. show that the ranking of the minimum eigenvalue of the grounded Laplacian Matrix is highly consistent with the ranking of resistance distance,much better than the degree ranking, which provides us another approach to estimate the node for Pinning control. In the case of controlling multiple(l) nodes, we use the iterative algorithm to calculate the grounded Laplacian Matrix for better combination of the controlled nodes, through which the computation of NP-hard problem of controlling l nodes can be greatly reduced, and the effect of the iterative algorithm are presented by simulation calculations in different networks,and the smaller l the better effect.

关键词： Complex dynamical network grounded Laplacian matrix pinning control spectral properties

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

Super Twisting Control of Passive Gait Training Exoskeleton ...

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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

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Metric Learning Based Rolling Bearing Faults Diagnosis with Curvelet Transform

Metric Learning Based Rolling Bearing Faults Diagnosis with ...

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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.

关键词：

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

arXiv

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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

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

arXiv

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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

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BoostTree and BoostForest for Ensemble Learning

arXiv

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arXiv 2020年

作者： Zhao, Changming Wu, Dongrui Huang, Jian Yuan, Ye Zhang, Hai-Tao Peng, Ruimin Shi, Zhenhua 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 Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China The Autonomous Intelligence Unmanned Systems Engineering Research Center of Ministry of Education of China The State Key Lab of Digital Manufacturing Equipment and Technology Wuhan China

Bootstrap aggregating (Bagging) and boosting are two popular ensemble learning approaches, which combine multiple base learners to generate a composite model for more accurate and more reliable performance. They have been widely used in biology, engineering, healthcare, etc. This paper proposes BoostForest, which is an ensemble learning approach using BoostTree as base learners and can be used for both classification and regression. BoostTree constructs a tree model by gradient boosting. It increases the randomness (diversity) by drawing the cut-points randomly at node splitting. BoostForest further increases the randomness by bootstrapping the training data in constructing different BoostTrees. BoostForest generally outperformed four classical ensemble learning approaches (Random Forest, Extra-Trees, XGBoost and LightGBM) on 35 classification and regression datasets. Remarkably, BoostForest tunes its parameters by simply sampling them randomly from a parameter pool, which can be easily specified, and its ensemble learning framework can also be used to combine many other base learners. Copyright © 2020, The Authors. All rights reserved.

关键词： Classification (of information)

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Fault Diagnosis of SOFC Stack Based on Neural Network Algorithm

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Energy Procedia 2019年 158卷 1798-1803页

作者： Tao Xue Xiaolong Wu Yuanwu Xu Suwen Jing Zehua Li Jianhua Jiang Zhonghua Deng Xiaowei Fu Li Xi School of Automation Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science & Technology Wuhan 430074 China College of Computer Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan 430065 China

To realize the commercialization of SOFC, it must be ensured that it can work efficiently and stably. SOFC fault diagnosis becomes an essential part of the research. Due to the strong coupling of faults in the stack, this paper uses neural network algorithm to detect and diagnose faults. The simulation results verify that through the diagnosis of the test sample, the recognition rate of the test sample by the network is found to be 95%, which explains the neural network fault diagnosis model established in this paper on identifying the normal working state of the stack, the electrode stacking of the stack, and the gas leakage fault of the stack has good effectiveness and accuracy.

关键词： SOFC fault diagnosis neural network

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