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第三十八届中国控制会议

作者： Junchao Ren Ding Liu Yin Wan Ni Zhang National & Local Engineering Research Center of Crystal Growth Equipment and System Integration Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing

In view of the previous Czochralski(Cz) monocrystalline silicon growth control method based on mechanism model, it is difficult to accurately reflect the nonlinear and real-time variation characteristics of crystal growth process. This paper proposes a data-driven iterative learning control method based on support vector regression, which combines crystal diameter prediction model by support vector regression(SVR) off-line training with crystal growth mechanism model, and then control the crystal diameter and output key variable information in the framework of iterative learning algorithm. Simulation results for diameter control of crystal growth process and the analysis of key variable information show that the proposed method is effective and has a fast crystal diameter learning speed, and good performances of setpoint tracking and disturbance rejection, high crystal growth speed.

关键词： Data-Driven Czochralski Monocrystalline Silicon Support Vector Regression Iterative Learning control

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Development of Sheet Palletizing Manipulator Based on Multi-link Mechanism

Research Square

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Research Square 2020年

作者： Chen, Zhihua Wang, Shoukun Liu, Quan Wang, Junzheng Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Key Laboratory of Servo Motion System Drive and Control Ministry of Industry and Information Technology School of Automation Beijing Institute of Technology Beijing100081 China New Energy Laboratory College of Mechanical and Electrical Engineering Beijing Information Science and Technology University Beijing100192 China

In view of the disadvantages of Traditional tandem joint type thin-plate palletizing robots using push-down adsorption sorting, such as moving path complexity, large occupied space, joint error stack and low end control precision, a novel multi-link mechanism thin-plate palletizing robot is developed. The kinematics analysis is carried out based on the analysis of the overall structure for the manipulator, a genetic algorithm based on the stroke speed ratio K, the transmission angle γ and the four-bar size is proposed to realize the optimal design of the four-link rocker arm mechanism. Secondly, a finite element based overall structure analysis method is proposed, which obtains the most affected part of the vibration process of a manipulator and performs load verification to prevent early warning and reference of faults caused by mechanical resonance. To solve the control problem of nonlinear strongly coupled dynamic systems, an iterative learning fuzzy adaptive control algorithm is proposed to achieve the smooth running of the manipulator and the high-precision tracking of the end centroid trajectory. The experimental results verify the feasibility and effectiveness of the proposed multi-link controllable thin-plate palletizing manipulator's overall structural design and iterative learning control method, which is well applied in practical engineering. © 2020, CC BY.

关键词： Manipulators

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Water Supply Prediction Based on Initialized Attention Residual Network

Water Supply Prediction Based on Initialized Attention Resid...

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第三十九届中国控制会议

作者： Yuhao Long Yeming Zhang Jingcheng Wang Miaoshun Bai Dept of Automation Shanghai Jiao Tong UniversityKey Laboratory of System Control and Information ProcessingMinistry of Education of China Shanghai Municipal Engineering Design Institute(Group)Co. Ltd Autonomous Systems and Intelligent Control International Joint Research Center Xi'an Technological University

Real-time and accurate water supply forecast is crucial for water supply control. However, most existing methods are likely affected by factors such as weather and holidays, which lead to a decline in the reliability of water supply *** this paper, we address a generic artificial neural network, Initialized Attention Residual Network(IARN), to tackle the time series prediction problem in water supply domain. Specifically, instead of continuing to use the recurrent neural network(RNN)in time-series tasks, we try to build a convolution neural network(CNN) to recede the disturb from other factors and get a more credible results. Experiments show that our method achieves state-of-the-art performance on several datasets in terms of accuracy,robustness and generalization ability. The source code of our method will available at https://***/SJTU-L-dd/IARN.

关键词： Neural networks Prediction methods Water supply Time-series analysis Attention module Residual network

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A hybrid model based on stacking and multi-correction mechanisms for urban water demand prediction ⁎

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IFAC-PapersOnLine 2020年第2期53卷 16685-16690页

作者： Yang Lan Jingcheng Wang Miaoshun Bai Ibrahim Brahmia Haotian Xu Piao Hu Yuhao Long Yeming Zhang Dept. of Automation Shanghai Jiaotong University Key Laboratory of System Control and Information Processing Ministry of education of China Shanghai 200240 China. Autonomous Systems and Intelligent Control International Joint Research Center Xi’an Technological University Xi’an Shaanxi710021 China. Shanghai Municipal engineering Design Institute (Group) Co. Ltd.

Water demand prediction is the key link for the effective operation of urban intelligent water supply system. Since the non-linearity and complex variability of water consumption, it is difficult for traditional water demand prediction models to guarantee high accuracy for a long period. Different holiday types and even tiny changes in temperatures can affect urban water demand seriously. This paper proposes a Stacking-based hybrid model which integrates multi-correction mechanisms to address these problems. A better stacking model is proposed to minimize the generalization error. The stability and reliability of predictions are improved through the design of multi-correction mechanisms such as high temperature weather compensation feature, holiday-type correction model and water quantity fluctuation correction model. Comparing different models before and after stacking also before and after correcting, the prediction accuracy of the proposed hybrid model is much higher and the predictions are more stable and reliable.

关键词： Water supply system Prediction methods Hybrid models Stacking methods Multi-correction mechanisms Neural-network models Time-series analysis

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A New Method for Deriving Weights in Group Fuzzy Analytic Hierarchy Process and Evaluation Measures

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IFAC-PapersOnLine 2020年第2期53卷 7941-7946页

作者： Lin Che Yeming Zhang Jingcheng Wang Miaoshun Bai Dept. of Automation Shanghai Jiaotong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China Shanghai Municipal Engineering Design Institute (Group) Co. Ltd. China Autonomous Systems and Intelligent Control International Joint Research Center Xi’an Technological University Xi’an Shaanxi 710021 China

The paper discusses fuzzy comparison matrices, consistency check, weight prioritization methods and weight evaluation methods in fuzzy group analytic hierarchy process. There are various methods of weight prioritization, however, they are not critically evaluated. In the paper, two measures are introduced for the evaluation of the group weights. Then, a new method is proposed to improve the process of deriving weights and use it in an application compared to another common three methods. Our results show that the new method is a good method for deriving weights of indexes.

关键词： Group Fuzzy AHP Ari-Geo Method Assessment Measure Fuzzy Consistency Check Triangular Fuzzy Number

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Diameter Detection of Silicon Single Crystal Based on Brain Storm Optimization Algorithm Hough Transform

Diameter Detection of Silicon Single Crystal Based on Brain ...

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第三十八届中国控制会议

作者： Lu Kang Ding Liu National and Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing

In order to improve the speed and accuracy of silicon single crystal diameter detection, this paper proposes an ellipse detection method based on brain storm optimization algorithm Hough transform, this method uses brain storm optimization algorithm to search ellipse parameters that make the accumulator obtains the maximum value after using Hough transform, then the accurate measurement value of silicon single crystal diameter is obtained. The proposed algorithm is simulated on the complete ellipse with Gaussian white noise and silicon single crystal diameter detection engineering experiments, and the results are compared with other intelligent optimization algorithms. Experimental results show that the proposed algorithm can effectively improve the speed and accuracy of ellipse detection with Hough transform, thereby verify the feasibility and effectiveness of the proposed algorithm, and improve the speed and accuracy of silicon single crystal diameter detection.

关键词： Silicon single crystal Diameter detection Ellipse detection Brain storm optimization algorithm Hough transform

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Research on Algorithm for Model Predictive control Based on Robust Optimizition

Research on Algorithm for Model Predictive Control Based on ...

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第三十八届中国控制会议

作者： Xia Zhang Ding Liu Fei Yu National and Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Xian University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing

The practicality of model predictive control(MPC) is partially limited by its ability to solve optimization problems in real time. The robust model prediction algorithm based on linear matrix inequality is divided into online and offline parts. In order to improve the response speed and control precision of the system, the upper bound of the objective function obtained by the off-line algorithm is taken as a known quantity, and a series of large asymptotically stable invariant elliptic sets are obtained by re-optimization. The state variables can be determined-lying between two ellipsoid sets within the ellipsoid set sequence, on the basis of online measured state variables. By adding an adjacent small ellipse set, the state variables are precisely located by three ellipse sets optimization, and the system control quantity is obtained. The simulation results show that this algorithm is better than the traditional algorithm. Finally, this algorithm is applied to identify parameters of silicon single crystal thermal field temperature model and the simulation results prove its excellent ability of parameters identification.

关键词： Robust optimization Model predictive control Uncertainty Online optimization

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Mem Brain: An Easy-to-Use Online Webserver for Transmembrane Protein Structure Prediction

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Nano-Micro Letters 2018年第1期10卷 12-19页

作者： Xi Yin Jing Yang Feng Xiao Yang Yang Hong-Bin Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Department of Computer Science Shanghai Jiao Tong University Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering

Membrane proteins are an important kind of proteins embedded in the membranes of cells and play crucial roles in living organisms, such as ion channels,transporters, receptors. Because it is difficult to determinate the membrane protein's structure by wet-lab experiments,accurate and fast amino acid sequence-based computational methods are highly desired. In this paper, we report an online prediction tool called Mem Brain, whose input is the amino acid sequence. Mem Brain consists of specialized modules for predicting transmembrane helices, residue–residue contacts and relative accessible surface area of a-helical membrane proteins. Mem Brain achieves aprediction accuracy of 97.9% of ATMH, 87.1% of AP,3.2 ± 3.0 of N-score, 3.1 ± 2.8 of C-score. Mem BrainContact obtains 62%/64.1% prediction accuracy on training and independent dataset on top L/5 contact prediction,respectively. And Mem Brain-Rasa achieves Pearson correlation coefficient of 0.733 and its mean absolute error of13.593. These prediction results provide valuable hints for revealing the structure and function of membrane *** Brain web server is free for academic use and available at ***/bioinf/Mem Brain/.

关键词： Transmembrane a-helices Structure prediction Machine learning Contact map prediction Relative accessible surface area

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Multi-carrier Differential Chaos Shift keying Wireless Communication Based On Chaotic Shape-forming Filter

Multi-carrier Differential Chaos Shift Keying Wireless Commu...

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Chinese Automation Congress (CAC)

作者： Hai-Peng Ren Si-Long Guo Chao Bai Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an China School of Mechatronic Engineering Xi’an Technological University Xi’an China

In recent years, chaotic communication has shown advantages, such as used the simplest matched filter to make signal-to-noise ratio (SNR) maximization and improve the resistance to multi-path interference. The chaotic shape-forming filter (CSF) has been proposed to generate the chaotic baseband signal in the wireless communication, the additional advantage of the chaotic shape-forming filter is able to generate the chaotic signal as spread sequence in Differential Chaotic Shift key (DCSK), which encodes information in the sequence. In this paper, the chaotic signal generated by CSF is used in DCSK and combined with multi-carrier frequency to improve the bit transmission rate. Because of CSF used, it helps to improve both Bit Error Rate (BER) performance and provide an additional information flow in the spread sequence itself. Simulation results indicate that the proposed method is superior to other methods over the Addition White Gaussian Noise (AWGN) channel and wireless channel.

关键词： Chaotic communication Signal to noise ratio Wireless communication Simulation Demodulation Data communication

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Hyperchaos synchronization using univariate impulse control

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Physical Review E 2019年第5期100卷 052215-052215页

作者： Kun Tian Chao Bai Hai-Peng Ren Celso Grebogi Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an 710048 China School of Mechatronics Engineering Xi'an Technological University Xi'an 710048 China Institute for Complex System and Mathematical Biology SUPA University of Aberdeen AB24 3UE United Kingdom

Rössler and Chen systems with time delay are shown to be hyperchaotic, which exhibits a more complex dynamics, including multiple positive Lyapunov exponents and infinite dimension. The hyperchaos has better application potential where hyperchaos synchronization is concerned. Univariate impulse control requires smaller perturbation to the response system, thus promising better performance. However, synchronization of two hyperchaotic systems using this control method is a challenging task due to the difficulty to guarantee synchronization stability using a minimum number of manipulated variables. In this paper, a univariate impulse control method is proposed for the synchronization of two hyperchaotic dynamics generated by time delay. A theorem is developed and proved to provide the sufficient conditions for the synchronization of time delay systems using the univariate impulse control. The upper bound of the impulse interval is proved to guarantee the asymptotic synchronization. Simulation and circuit experiment show the correctness of the analysis and the feasibility of the proposed method.

关键词： control & applications of chaos Synchronization

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