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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Xuan Ma Ying Yan Qing Liu Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an City China

Aiming at the characteristics of strong constraints of multiple knapsack problem (MKP), a method based on multi-objective particle swarm optimization is proposed in this paper. We take constraint violation as an optimal objective to avoid the troublesome of dealing with unfeasible solutions unsatisfied constraints. For updating effectively the external archive, the degree of constraint violation and the crowding degree of non-dominated particle in Pareto front are taken as two criteria. To decrease the probability of algorithm converging to a pseudo Pareto front, a mutation operator is designed. The experimental results of solving a number of MKP instances demonstrate that the proposed algorithm performs better to the MKP instances with strong constraints.

关键词： Optimization Sociology Statistics Particle swarm optimization Heuristic algorithms complex systems control systems

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Modeling and simulation of MHD melt convection and heat transfer in CZ crystal growth

Modeling and simulation of MHD melt convection and heat tran...

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Chinese Automation Congress

作者： Ni Zhang Ding Liu Weichao Huang National and Local Joint Engineering Shaanxi key Laboratory of Complex System Control and Intelligent Information Processing Xi'an China

This paper presents an axisymmetric swirl incompressible thermal lattice Boltzmann model to study the magnetohydrodynamics (MHD) melt flow and heat transfer at high Grashof number and high Reynolds number under external magnetic field in Czochralski (CZ) crystal growth process. The model is built based on the double distribution function lattice Boltzmann equation (DDF-LBE) and is verified by benchmark problem. By using the proposed model, the evolution relationship of melt flow, temperature is constructed under the combined effects of Lorenz force, rotating inertia force and thermal buoyancy force. Furthermore, the flow patterns and the temperature distribution of silicon melt under different magnetic field strengths are simulated. The simulation results show that with the increase of magnetic field strength, the melt velocity is reduced, which suppresses the melt convection and is beneficial for suppressing the fluctuation of melt. And the temperature gradient below the crystal growth interface increases in the case of crystal rotation, which is helpful to increase the crystallization rate. The combination of Cusp magnetic field, crucible rotation and crystal rotation can be used as an effective method in the production of high-quality crystal.

关键词： Modeling Computer simulation Crystal growth Lattice boltzmann method

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Stochastic optimal control for sampled-data system under stochastic sampling

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IET control Theory & Applications 2018年第11期12卷 1553-1560页

作者： Haoyuan Sun Jie Chen Jian Sun School of Automation Beijing Institute of Technology Beijing 100081 People's Republic of China Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing 100081 People's Republic of China

In this study, the problem of designing a stochastic optimal controller for sampled-data systems whose sampling interval is subjected to a certain probability distribution is addressed. To design the controller, the Kronecker product operation and the Vandermonde matrix were introduced. A design method of the stochastic optimal controller is proposed. It is shown that the controller guarantee that the closed-loop system has exponentially mean square stability. Finally, the simulation results illustrate the effectiveness and practicability of the proposed method.

关键词： sampled data systems control system synthesis stochastic systems stochastic processes closed loop systems optimal control asymptotic stability sampling methods statistical distributions matrix algebra stochastic optimal control sampled-data system stochastic sampling sampling interval stochastic optimal controller design probability distribution Kronecker product operation Vandermonde matrix closed-loop system exponentially mean square stability

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Optimizing multi-criteria k-shortest paths in graph by a natural routing genotype-based genetic algorithm

Optimizing multi-criteria k-shortest paths in graph by a nat...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Yang Wang Qing Liu Haipeng Ren Xuan Ma Long Liu Wenqing Wang Jing Zhang Department of Information and Control Engineering Xi'an University of technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an China

k-shortest path problem (KSP) is a more general form of the classical shortest path problem in graph. Its task is no longer to find the shortest path between two vertices, but to find the shortest k paths. So far, the reported KSP-algorithms only considers finding shortest k paths with regard to a single criterion, while far more application scenarios of the KSP require to determine k paths by taking more criteria into account. This article formulates the KSP as a multi-objective optimization problem. In consideration of the problem's property and genetic algorithm's great success in multi-objective optimization, the genetic algorithm is utilized as the optimization tool. The genotype is directly represented as the form of natural routing. In order to make the related genetic operators risk-free, the conception of gene-bank is introduced. And the crossover and mutation are both implemented based on the introduced gene-bank. The proposed genetic algorithm is tested on an undirected graph of 9-vertex/65-edge. The testing result shows that, our proposed algorithm is valid and outperforms some other swarm intelligence-based algorithms in terms of the obtained paths' quality.

关键词： Genetic algorithms Optimization Genetics Routing Task analysis Encoding Sociology

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A maximum power point tracking controller for thermoelectric generators 36

A maximum power point tracking controller for thermoelectric...

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第36届中国控制会议

作者： Wei Xie Guangyue Huang Xiao Zhang Fang Deng School of Automation Beijing Institute of Technology Key Laboratory of Intelligent Control and Decision of Complex Systems China ZhenHua Oil Co

ISBN: (纸本)9781538629185

Thermoelectric generators（TEGs） directly convert heat energy into electricity for power-supplying sensors and other portable electronic *** this paper,a maximum power point tracking（MPPT） controller in the modified perturb and observe（P＆O） algorithm was designed on the basis of analyzing the output characteristics of the TEG *** MPPT controller consists of a voltage sensor,a current sensor,a microcontroller and a SEPIC converter,which keeps the operating point of the thermoelectric generator tracking the maximum power point（MPP）.The thermoelectric generator was heated to generate electricity,and a supercapacitor accumulated that *** results using the MPPT controller present that the thermoelectric generator’s output voltage achieves around 5V,the output power achieves upward of 2W when the temperature difference △T=75℃,and the operating point of the thermoelectric generator accurately tracks the MPP with a tracking efficiency of 99.8%;the charging power can be improved 89.65%.

关键词： Thermoelectric generators（TEGs） Modified P＆O algorithm Maximum power point tracking（MPPT） SEPIC converter

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Intersection Scan Model and Probability Inference for Vision Based Small-scale Urban Intersection Detection 28

Intersection Scan Model and Probability Inference for Vision...

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IEEE intelligent Vehicles Symposium

作者： Yang Yi Li Hao Zhu Hao Shang Songtian Lyu Ningyi Song Wenjie School of Automation and State Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology

ISBN: (纸本)9781509048052

Large-scale intersections stamped on maps have diverse visual features for detection, while small-scale urban intersections are hard to be identified especially when GPS signals are missing. In this paper, we propose a Hidden Markov Model (HMM) based small-scale intersection detection method utilizing monocular vision. We extract visual cues of road transformations and dynamic vehicles' tracks, and then design an Intersection Scan Model to obtain the potential traversable direction of the current road, which is the primary criterion of the intersection estimation. For better performances, we take the detections of consecutive frames into consideration and finally integrate them into HMM to estimate the probabilities of intersections. Results from KITTI datasets and real-world experiments have shown the functionality of the presented approach.

关键词： Hidden Markov models Roads Global Positioning system Vehicle dynamics Semantics Image segmentation Feature extraction

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Correction to: A method of VR‑EEG scene cognitive rehabilitation training

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Health information science and systems 2021年第1期9卷 7页

作者： Wenjun Tan Yang Xu Pan Liu Chunyan Liu Yujin Li Yanrui Du Chao Chen Yuping Wang Yanchun Zhang Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang 110189 China. Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou 510006 China. Department of Neurology Xuanwu Hospital Capital Medical University Beijing 100053 China. Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology Tianjin 300384 China. Institute for Sustainable Industries and Liveable Cities Victoria University Melbourne VIC 8001 Australia.

[This corrects the article DOI: 10.1007/s13755-020-00132-6.].

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Distributed Algorithm for Discrete-Time Lyapunov Equations

Distributed Algorithm for Discrete-Time Lyapunov Equations

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International Conference on control, Automation, Robotics and Vision

作者： Xia Jiang Xianlin Zeng Jian Sun Jie Chen Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China Ministry of Education Beijing Institute of Technology Beijing China

This paper investigates the problem of solving a unique solution to discrete-time Lyapunov equations (DTLE) using multi-agent networks. We propose a distributed algorithm where each agent only uses partial information of the matrices. The agents of the algorithm reach a consensus by exchanging information with their neighbors over an undirected connected graph. We provide convergence analysis and the convergence rate estimate for the proposed algorithm. Finally, convergence performance is verified by numerical simulations.

关键词： Distributed algorithms Mathematical model Convergence Optimization Radio frequency Convex functions Symmetric matrices

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Ensemble of Extreme Learning Machines for Regression

Ensemble of Extreme Learning Machines for Regression

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Data Driven control and Learning systems (DDCLS)

作者： Atmane Khellal Hongbin Ma Qing Fei School of Automation Beijing Institute of Technology Beijing China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology China

ISBN: (纸本)9781538626191

Regression, as a particular task of machine learning, performs a vital part in data-driven modeling, by finding the connections between the system state variables without any explicit knowledge about the system, using a collection of input-output data. To enhance the prediction performance and maximize the training speed, we propose a fully learnable ensemble of Extreme Learning Machines (ELMs) for regression. The developed approach learns the combination of different individual models, using the ELM algorithm, which is applied to minimize both the prediction error and the norm of the network parameters, which leads to higher generalization performance under Bartlett's theory. Moreover, the average based ELM ensemble may be viewed as a particular case of our model. Extensive experiments on many standard regression benchmark datasets have been carried out, and comparison with different models has been performed. The experimental findings confirm that the proposed ensemble can reach competitive results in term of the generalization performance, and the training speed. Furthermore, the influence of different hyper parameters on the performance, in term of the prediction error and the training speed, of the developed model has been investigated to provide a meaningful guideline to practical applications.

关键词： Computational modeling Training Mathematical model Predictive models Neural networks Prediction algorithms Approximation algorithms

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Stability of linear systems with sawtooth input delay and predictor-based controller

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Automatica 2020年 117卷

作者： Haoyuan Sun Jian Sun Jie Chen School of Automation Beijing Institute of Technology Beijing 100081 PR China Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing 100081 PR China School of Electronic and Information Engineering Tongji University Shanghai 200082 PR China

In this paper, the stability of linear systems with sawtooth input delay widely existing in networked systems and predictor-based controller is considered. Under the assumption that there exists an instant where the input delay is zero, a necessary and sufficient condition is obtained to guarantee the exponential stability of the closed-loop system, that is, the closed-loop system is stable if and only if the matrix A + B K is Hurwitz. Two simulation examples are given to confirm the validity of the obtained results.

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