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第37届中国控制会议

作者： Fang Wang Xin Chen Chang Tan Jinbin Li Yaodong Zhang School of Automation China University of Geoscience Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems State Grid Hubei Electric Power Research Institute

Traditional artificial live work asks for a high standard with operator’s mental state and technical level in high-voltage and strong-magnetic environment,which not only reduces the efficiency of modern production and living but also causes potential threats to the safety of ***,developing robot live work has great significance for both academic research and industrial applications.A key problem of the live work for an industrial robot is recognition and positioning of the hexagonshaped screw in substation with high *** solve this problem,this article designs a hexagon-shaped screw recognition and positioning system based on binocular vision in transformer substation.A method for stereo matching including 3 steps is proposed:edge noise of the target is reduced by median filter at the beginning;then the target can be obtained through the image segmentation method of adjusting threshold;further moments are used to express the centers of hexagons in 2 images which work as stereo matching *** depth information can be obtained due to visual geometric relationships based on stereo model and camera calibration parameters based on camera *** experiment results show that the binocular system designed in this article can accurately identify the target with good stability,and the measurement error is controlled in 4%.

关键词： Binocular stereo vision Image segmentation Stereo matching Depth information

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Multi-targets tracking based on SB/MHT theory

Multi-targets tracking based on SB/MHT theory

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作者： Ru, Qi Peng, Zhihong Zhang, Juan Chen, Xun School of Automation Beijing Institute of Technology Beijing 100081 China Education Ministry Key Laboratory of Intelligent Control of Complex Systems and Decision Beijing 100081 China

ISBN: (纸本)9787894631046

Multi-targets video tracking theory is introduced briefly, and the structured Branching MHT algorithm is applied in Multi-targets video tracking system. Connected with Kalman filter, hypotheses are built by SB/MHT using measures which are stretched by Bhattacharyya coefficient. In this way, Multi-targets video tracking can carry out. Feasibility of this method is validated by simulation.

关键词： Kalman filters

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Model-Based and Data-Driven control of Event- and Self-Triggered Discrete-Time LTI systems

arXiv

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arXiv 2022年

作者： Wang, Xin Berberich, Julian Sun, Jian Wang, Gang Allgöwer, Frank Chen, Jie The Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing10081 China The Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China The University of Stuttgart Institute for Systems Theory and Automatic Control Stuttgart70550 Germany The Department of Control Science and Engineering Tongji University Shanghai201804 China The State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China

The present paper considers the model-based and data-driven control of unknown linear time-invariant discretetime systems under event-triggering and self-triggering transmission schemes. To this end, we begin by presenting a dynamic event-triggering scheme (ETS) based on periodic sampling, and a discrete-time looped-functional approach, through which a model-based stability condition is derived. Combining the model-based condition with a recent data-based system representation, a data-driven stability criterion in the form of linear matrix inequalities (LMIs) is established, which also offers a way of co-designing the ETS matrix and the controller. To further alleviate the sampling burden of ETS due to its continuous/periodic detection, a self-triggering scheme (STS) is developed. Leveraging pre-collected input-state data, an algorithm for predicting the next transmission instant is given, while achieving system stability. Finally, numerical simulations showcase the efficacy of ETS and STS in reducing data transmissions as well as of the proposed co-design methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear matrix inequalities

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EMOST: A dual-branch hybrid network for medical image fusion via efficient model module and sparse transformer

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Computers in Biology and Medicine 2024年 179卷 108771-108771页

作者： Wang, Wenqing He, Ji Liu, Han School of Automation and Information Engineering Xi'an University of Technology Xi'an710048 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an710048 China

Multimodal medical image fusion fuses images with different modalities and provides more comprehensive and integrated diagnostic information. However, current multimodal image fusion methods cannot effectively model non-local contextual feature relationships, and due to direct aggregation of the extracted features, they introduce unnecessary implicit noise into the fused images. To solve the above problems, this paper proposes a novel dual-branch hybrid fusion network called EMOST for medical image fusion that combines a convolutional neural network (CNN) and a transformer. First, to extract more comprehensive feature information, an effective feature extraction module is proposed, which consists of an efficient dense block (EDB), an attention module (AM), a multiscale convolution block (MCB), and three sparse transformer blocks (STB). Meanwhile, a lightweight efficient model (EMO) is used in the feature extraction module to exploit the efficiency of the CNN with the dynamic modeling capability of the transformer. Additionally, the STB is incorporated to adaptively maintain the most useful self-attention values and remove as much redundant noise as possible by developing the top-k selection operator. Moreover, a novel feature fusion rule is designed to efficiently integrate the features. Experiments are conducted on four types of multimodal medical images. The proposed method shows higher performance than the art-of-the-state methods in terms of quantitative and qualitative evaluations. The code of the proposed method is available at https://***/XUTauto/EMOST. © 2024 Elsevier Ltd

关键词： Feature extraction

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Robust adaptive neural network consensus tracking control of multi-robot systems

Robust adaptive neural network consensus tracking control of...

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第33届中国控制与决策会议

作者： Huijun Guo Jintao Liang School of Automation and Information Engineering Xi'an University of Technology Key Laboratory of Shaanxi Province for Complex System Control and Intelligent Information Processing Guilin University of Electronic Technology

This paper studies the problem of distributed consensus tracking control for multi-robot systems with different friction coefficients and external disturbances based on the multi-agent *** the case where the communication topology is a weighted directed graph and leader node is the neighbor of a small portion of the follower *** follower nodes are modeled as two second-order nonlinear dynamical system with coupling parts.A distributed robust adaptive control law based on the neural networks(NN) is designed for each follower node,where the controller requires only relative state information from its adjacent *** such control scheme,the states of all follower nodes ultimately synchronize to the leader node with bounded residual *** results are provided to validate the effectiveness of the algorithm.

关键词： Consensus control Multi-robot systems Agent systems Neural networks Sliding mode control

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Event-triggered consensus for linear continuous-time multi-agent systems based on a predictor

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Information Sciences 2018年 459卷 278-289页

作者： Jian Sun Qiuling Yang Xiaoyu Liu Jie Chen School of Automation Beijing Institute of Technology Beijing 100081 PR China Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing 100081 PR China China North Industrial Group Corporation Limited China Ordnance Industry Navigation and Control Technology Institute Beijing 100089 PR China

In this paper, the problem of an event-triggered consensus for a linear continuous-time multi-agent system is investigated. A new event-triggered consensus protocol based on a predictor is proposed to achieve consensus while not requiring continuous communication among agents. The predictor utilizes an artificial closed-loop system to predict the future state of each agent. With the proposed consensus protocol, each agent only needs to monitor its own states to determine its event-triggered instants. When an event of an agent is triggered, the agent immediately updates its consensus protocol and sends its state information to its neighbors. When an agent receives state information from its neighbors, the agent immediately updates its consensus protocol and predictor. A necessary and sufficient condition that solves the consensus problem is derived. Moreover, it is proved that Zeno behaviors are excluded. Finally, some numerical examples are given to illustrate that, with the proposed protocol, a multi-agent system can achieve consensus while greatly reducing event-triggered times.

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Mobile robot's electronic compass calibration based on modified Fourier Neural Network

Mobile robot's electronic compass calibration based on modif...

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IEEE Conference on Robotics, automation and Mechatronics

作者： Gong Kun Deng Fang Ma Tao Gong Kun is with School of Automation Beijing Institute of Technology and Key Laboratory of Advanced Control of Iron and Steel Process (Ministry of Education) Beijing China Deng Fang is with School of Automation Beijing Institute of Technology and Key Laboratory of Advanced Control of Iron and Steel Process (Ministry of Education) Beijing China Ma Tao is with School of Automation Beijing Institute of Technology and Key Laboratory of Complex System Intelligent Control and Decision Ministry of Education Beijing China

In order to improve the precision of the azimuth measured by mobile robot's electronic compass, this paper proposes a new calibration method based on Fourier Neural Network trained by Modified Particle Swarm Optimization (MPSO-FNN). This method makes use of Fourier Neural Network (FNN) to establish the error compensation model of electronic compass's azimuth, and introduces Modified Particle Swarm Optimization (MPSO) algorithm to optimize the weights of neural network. Thus the comparatively accurate error model of azimuth is obtained to compensate the output of electronic compass. This method not only has strong nonlinear approximation capability, but also overcomes the neural networks' shortcomings which are too slow convergence speed, oscillation, and easy to fall into local optimum and sensitive to the initial values. Experimental results demonstrate that after calibrated by this method, the range of azimuth error reduces to -0.35°~0.70° from -3.4°~25.2°, and the average value of absolute error is only 0.30°.

关键词： Compass Azimuth Biological neural networks Particle swarm optimization Training Accuracy automation

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Adaptive Back-stepping Based control for Active Suspension systems with Prescribed Performance

Adaptive Back-stepping Based Control for Active Suspension S...

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第40届中国控制会议

This paper proposes an adaptive back-stepping tracking control with prescribed performance function for vehicle suspension systems with uncertain parameters. The prescribed performance function can lead the tracking error from a large set to an arbitrarily small residual set. Firstly, the constrained tracking error system incorporating the prescribed performance functions is transformed into unconstrained form. Then an adaptive updating scheme based on the projection algorithm is designed to estimate the uncertain parameter. Following that an adaptive nonlinear controller is designed using the back-stepping technique, and the stability of whole controlled systems is proved by the Lyapunov theory. Meanwhile, the state convergence of the internal dynamical system is also guaranteed. Finally, simulation results are provided to verify the proposed approach.

关键词： Active Suspension system Adaptive control Back-stepping technique prescribed performance

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A 3D Display Parallel system: Light Field Re-rendering and Depth Sense Optimization

A 3D Display Parallel System: Light Field Re-rendering and D...

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International Conference on Display Technology, ICDT 2018

作者： Pei, Renjing Ma, Kui Wang, Feiyue State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences East Zhongguanchun Road No. 95 Haidian District Beijing China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China Parallel Optics Technology Innovation Center Qingdao Academy of Intelligent Industries Qingdao China

ISBN: (纸本)9781510867659

The main benefit of 3D display over 2D display is the obvious ability to create a more lifelike character with high depth sense. However, the limitation of human eye’s visual mechanism, unartful 3D scene structure design, or bad viewing condition always emerges poor depth perception experience or even physiological discomfort during the watching time, which is often sub-optimal for mass high-quality 3D display productions. To solve this problem, we propose a novel 3D display parallel system for depth sense optimization and it empirically guides how the light field should be re-rendered. Structurally, the parallel system consists of an artificial perception measurement system, a display evaluation model and a light field display rendering system, which includes the display calibration, scene capture, light field data processing and display. Particularly, the system can systematically analyze and model various factors affecting the depth sense which learned through the measurement system, like scene structure, objects’ speeds in 3D video and so on. And those sense factors can be personally modified or increased according to the viewer’s demands or technical improvement. Moreover, the light field could be real-time re-rendered, based on some image processing technology, optical flow analysis and object segmentation (or tracking) (especially the one-shot video segmentation). Theory and algorithms are developed and experimental validation results show a superior performance. © 2018 SID.

关键词： Three dimensional displays

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Local entropy difference-fast marching method for leak diagnosis and localization

Local entropy difference-fast marching method for leak diagn...

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IEEE/ASME (AIM) International Conference on Advanced intelligent Mechatronics

作者： Ling Zhao Tao Wang Wei Fan Nan Ge Tong Zhao Guangzheng Peng School of Automation Complicated System of Intelligent Control and Decision Key Laboratory of Education Ministry Beijing Institute of Technology Beijing China Department of Engineering Physics Tsinghua University Beijing China

In this paper, an effective technique for leak diagnosis and localization which couples the local entropy difference method (LEDM) to the fast marching method (FMM) is proposed. A computer-based system is described to combine a specific pneumatic circuit with an on-line infrared camera which is used to test the air-tightness of a test vessel. The test object is charged with cryogenic compressed air and its temperature field is acquired by infrared thermography in this system. Then, both the leakage point and its edge can be identified by means of the designed LED-FM method. An experiment is conducted that confirms that the leak diagnosis and localization method presented in this paper is effective in diagnosing leakage points and in extracting accurate leakage edges, in spite of the presence of surface defects.

关键词： Entropy Pixel Image edge detection Level set Cameras Equations Probes

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