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2018 Chinese Automation Congress, CAC 2018

作者： Sun, Lanlan Hei, Xinhong Xie, Guo Qian, Fueai Wang, Zhuxin Liu, Ran System Control and Intelligent Information Processing University of Technology Xi'An Shaanxi Key Laboratory of Complex Xi'an China

ISBN: (纸本)9781728113128

The axle temperature is an important performance index for high-speed train axle, and many types of high-precision detection device have been developed. However, because the existing hotbox detection system works by comparing the measured value with fixed and experiential temperature thresholds, without considering the dynamic influence of running environment, the rates of fault and failure alarm are still high. Regarding this problem, this paper is to realize high precision fault diagnosis. Firstly, the characteristics of the temperature rising rates of all axles, the axles on the same side of a carriage, the axles on the same side of a whole train are analyzed. Secondly, the support vector machine (SVM) is employed to establish a data-based fault diagnosis model, and the model is optimized using genetic algorithm. Finally, the proposed method is applied to an actual running data, the analysis results demonstrated the effectiveness and feasibility. © 2018 IEEE.

关键词： Genetic algorithms

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Predicting Remaining Useful Life of Industrial Equipment Based on Multivariable Monitoring Data Analysis

Predicting Remaining Useful Life of Industrial Equipment Bas...

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2018 Chinese Automation Congress, CAC 2018

作者： Li, Xin Xie, Guo Liu, Han Wang, Wenqing Information Processing Xi 'An University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Xi'an China

ISBN: (纸本)9781728113128

Modern industrial equipment plays a vital role in the operation of large industrial systems. Accurate prediction of its remaining useful life will greatly reduce the possibility of unsafe accidents. The internal structure of modern industrial equipment is very complex, and a variety of state monitoring data affect the remaining useful life of the equipment, and the mutual influence between different monitoring data has greatly increased the difficulty of remaining useful life prediction. In this paper, the long short-term memory network in deep learning is used to solve this problem. The long short-term memory network has many advantages in time series prediction. The remaining useful life prediction model of industrial equipment based on long short-term memory network and multivariable monitoring data is established, and aiming at the characteristics of multivariable monitoring data, Mini-batch Gradient Descent algorithm (MBGD) is adopted to train the network. Finally, the engine monitoring data set provided by the NASA prediction center is used to verify the algorithm. The data set is divided into training set and test set, the training set is put into the long short-term memory network to get the prediction model, and then the remaining useful life prediction is carried out by the test set. The prediction accuracy and generalization ability of the algorithm are verified by predicting the remaining useful life of three different turbofan engines. © 2018 IEEE.

关键词： Long short-term memory

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Input-to-state stability of impulsive switched nonlinear time-delay systems with two asynchronous switching phenomena

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International Journal of Robust and Nonlinear control 2020年第12期30卷

作者： Zhichuang Wang Jian Sun Jie Chen School of Automation Beijing Institute of Technology Beijing China Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing China

This summary addresses the input-to-state stability (ISS) and integral ISS (iISS) problems of impulsive switched nonlinear time-delay systems (ISNTDSs) under two asynchronous switching effects. In our investigated systems, impulsive instants and switching instants do not necessarily coincide with each other. Meanwhile, systems switching signals are not simultaneous with the corresponding controllers switching signals, which will induce instability seriously, and cause many difficulties and challenges. By utilizing methods of Lyapunov-Krasovskii and Lyapunov-Razumikhin, mode-dependent average dwell time approach, and mode-dependent average impulsive interval technique, some stability criteria are presented for ISNTDSs under two asynchronous switching effects. Our proposed results improve the related existing results on the same topic by removing some restrictive conditions and cover some existing results as special cases. Finally, some simulation examples are presented to illustrate the effectiveness and advantages of our results.

关键词： impulsive switched systems input-to-state stability nonlinear time-delay systems two asynchronous switching phenomena

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Real-world Robot Reaching Skill Learning Based on Deep Reinforcement Learning

Real-world Robot Reaching Skill Learning Based on Deep Reinf...

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第32届中国控制与决策会议

作者： Naijun Liu Tao Lu Yinghao Cai Rui Wang Shuo Wang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced TechnologyChinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology of the Chinese Academy of Sciences

Traditional programming method can achieve certain manipulation tasks with the assumption that robot environment is known and ***,with robots gradually applied in more domains,robots often encounter working scenes which are complicated,unpredictable,and *** overcome the limitation of traditional programming method,in this paper,we apply deep reinforcement learning(DRL) method to train robot agent to obtain skill *** policy trained with DRL on real-world robot is time-consuming and costly,we propose a novel and simple learning paradigm with the aim of training physical robot ***,our method train a virtual agent in an simulated environment to reach random target position from random initial ***,virtual agent trajectory sequence obtained with the trained policy,is transformed to real-world robot command with coordinate transformation to control robot performing reaching *** show that the proposed method can obtain self-adaptive reaching policy with low training cost,which is of great benefits for developing intelligent and robust robot manipulation skill system.

关键词： Robot reaching skill learning deep reinforcement learning

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Optimization of Missile Path Planning Based on APMO-HV Algorithm

Optimization of Missile Path Planning Based on APMO-HV Algor...

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2019 IEEE International Conference on Unmanned systems, ICUS 2019

作者： Zhang, Hu Wang, Shuai Liu, Tonglin Zhou, Aimin Zhang, Yi Beijing Electro-mechanical Engineering Institute Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Changzhou University School of Mechanical Engineering Changzhou China East China Normal University Shanghai Key Laboratory of Multidimensional Information Processing School of Computer Science and Technology Shanghai China

ISBN: (纸本)9781728137926

In this paper, with considerations of low efficiency of missile path planning (MPP) by traditional aggregation technology, it uses affinity propagation based multi-objective evolutionary algorithm with hypervolume environment selection (APMO-HV) to solve the problem of MPP after establishing the MPP model. The experimental part compares and analyzes APMO-HV with six state-of-the-art algorithms, and applies it to address the MPP problem. The experimental results show that compared with the other six algorithms, APMO-HV has achieved the best solution performance in both the GLT test suite and MPP problem. This not only validates the effect of the proposed algorithm, but also enriches and improves the research results of MPP. © 2019 IEEE.

关键词： Evolutionary algorithms

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Transformer Winding Deformation Diagnosis Based on Signal Distance

Transformer Winding Deformation Diagnosis Based on Signal Di...

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第30届中国过程控制会议（CPCC 2019）

作者： LiShengmin ZhaoDan HeYuanyuan WangXiaoafan School of Automation and Information Engineering Xi'an University of Technology Key Laboratory of Shaanxi Province for Complex System Control and Intelligent Information Processing

Aiming at the problem that the correlation coefficient method cannot judge the deformation of the transformer winding when the measured frequency response curve is the same as the reference frequency response curve(frequency response analysis,FRA) and the amplitude is different, this paper proposed a new transformer winding diagnosis method based on signal *** method can accurately determine whether the winding is deformed according to the mutual distance value [0, 1] by calculating the mutual distance between the measured frequency response curve and the reference frequency response *** the simulation experiment and the national standard judgment criterion, the criteria for judging the deformation of the winding by mutual distance were *** simulation results show that the method is sensitive, reliable and universal.

关键词： FRA Frequency response curve Winding deformation signal distance mutual distance

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A New Intrusion Detection Method Based on Adaptive Feature Extraction

A New Intrusion Detection Method Based on Adaptive Feature E...

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第三十八届中国控制会议

作者： Ya-Li Wu Guo-Ting Li Yu-Long Fu Xiao-Peng Wang School of Automation and Information Engineering Xi'an University of Technology Shaanxi Province Key Laboratory of Complex System Control and Intelligent Information Processing

Compared with traditional machine learning, intrusion detection based on deep learning has the advantage of feature extraction. Auto-encoders, owing to having good feature extraction and dimensionality reduction ability, are known for the superior performance. However, the network structure selection is time-consuming and laborious. The essential features cannot be automatically extracted according to different distribution characteristics of the data set. Moreover, the existing data pre-processing method fails to adapt the model better. Based on the stack sparse auto-encoders(SSAE), we propose a new intrusion detection model named DBSO-SSAE. The algorithm is based on difference brain storm optimization(DBSO) which automatically adapts to selection network framework. Meanwhile, a new normalization strategy is introduced to further improve the accuracy of the model. The simulation results show that DBSO-SSAE greatly reduces the complexity of manual tuning and increases the adaptability of the network model.

关键词： intrusion detection feature extraction SSAE deep learning difference brain storm optimization

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Preface——Special Issue on New Directions in Nonlinear and Distributed control

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Journal of systems Science & complexity 2018年第1期31卷 1-3页

作者： LIU Tengfei XIN Bin JIANG Zhong-Ping State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang 110819 China School of Automation State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing 100081 China Tandon School of Engineering New York University Brooklyn NY 11201 USA

Nonlinearity is ubiquitous in engineering and natural *** development of nonlinear control can be traced back to decades *** date,the research has reached the stage that emphasizes developing methodologies that can ha... 详细信息

关键词：控制问题非线性分布式工程应用程序电子物理自然系统生产线相互作用

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Aeromagnetic Compensation Based on Tikhonov Regularization with Limited L-Curve Parameter-Choice Algorithm 37

Aeromagnetic Compensation Based on Tikhonov Regularization w...

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37th Chinese control Conference, CCC 2018

作者： Mengyin, Fu Jie, Li Tailin, Wu Tong, Liu Meiling, Wang Kai, Wang Jiapeng, Kang Beijing Institute of Technology Beijing100081 China Key Laboratory of Complex System Intelligent Control and Decision Beijing100081 China Nanjing University of Science and Technology Nanjing210094 China

ISBN: (纸本)9789881563941

Magnetic anomaly detection has great application value to national economic development and national defense construction, while the presence of various interference magnetic fields on the aircraft will affect the detection of magnetic anomaly. Therefore, it is very important to compensate the aircraft interference magnetic field. In order to solve the problem that the accuracy of the parameters is poor by using ridge estimation in the process of solving the parameters of aircraft interference magnetic field model, we propose a method based on Tikhonov Regularization (TR). Firstly, the optimal regularization parameter is obtained by the limited L-curve method in our work. Secondly, the aircraft interference magnetic field model parameters are obtained by using the TR method. Finally, we use the model parameters to calculate the aircraft interference magnetic field in the real flight, and complete the compensation. The effectiveness of this method is validated by simulation experiments. The results show that the method proposed in this paper has the advantages of high precision and convenient application, so it can be used to compensate the aircraft interference magnetic field effectively. © 2018 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Parameter estimation

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Hierarchical Task Impedance control of a Serial Manipulator for Minimally Invasive Surgery

Hierarchical Task Impedance Control of a Serial Manipulator ...

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IEEE International Conference on Human-Machine systems

作者： Hang Su Chenguang Yang Jiehao Li Yiming Jiang Giancarlo Ferrigno Elena De Momi Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan Italy Bristol Robotics Laboratory University of the West of England Bristol United Kingdom State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China National Engineering Laboratory for Robot Visual Perception and Control Hunan University Changsha China Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milano Italy

ISBN: (数字)9781728158716

ISBN: (纸本)9781728158723

Flexibility and robustness have become key points in the development of surgical robot controller for physical interactions. However, the conventional impedance control schemes unaware of the actual surgical scenario, including complex physical interaction on the robot arm, lead to the loss of accuracy. In this paper, a hierarchical task impedance control scheme is proposed for Minimally Invasive Surgery (MIS) based on an operational space formulation of a 7 DoFs redundant robot. Its redundancy is exploited to guarantee a remote center of motion (RCM) constraint and to provide a flexible workspace for the medical staff to assist physicians. In addition to the achievement of the classical whole-body impedance control, the issue of uncertain disturbances will be addressed by a decoupled adaptive approximation based on a radial basis function neural network (RBFNN) within the control framework. Task performances under the hierarchical task impedance controller were validated and compared with previous work in the literature. Experimental results showed its improved performance in terms of positional error and RCM constraint, regardless of the existing uncertain physical interaction.

关键词： Task analysis Impedance Manipulators Tools Surgery Jacobian matrices

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