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2013 IEEE International Conference on Automation Science and engineering, CASE 2013

作者： La, Hung Manh Sheng, Weihua Chen, Jiming Rutgers University Piscataway NJ 08854 United States School of Electrical and Computer Engineering Oklahoma State University Stillwater OK 74078 United States State Key Lab. of Industrial Control Technology Department of Control Zhejiang University Hangzhou 310027 China

ISBN: (纸本)9781479915156

Scalar field mapping has many applications including environmental monitoring, search and rescue, etc. In such applications there is a need to achieve a certain level of confidence regarding the estimates at each location. In this paper, a cooperative and active sensing framework is developed to enable scalar field mapping using multiple mobile sensor nodes. The cooperative and active controller is designed via the real-time feedback of the sensing performance to steer the mobile sensors to new locations in order to improve the sensing quality. During the movement of the mobile sensors, the measurements from each sensor node and its neighbors are taken and fused with the corresponding confidences using distributed consensus filters. As a result an online map of the scalar field is built with a certain level of confidence of the estimates. We conducted computer simulations to validate and evaluate our proposed algorithms. © 2013 IEEE.

关键词： Sensor nodes

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The application of adaptive backstepping sliding mode for hybrid humanoid robot arm trajectory tracking control

The application of adaptive backstepping sliding mode for hy...

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International Conference on Advanced Mechatronic Systems (ICAMechS)

作者： Li Qin Lihuan Liang Fucai Liu Zhenlin Jin Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University YSU Qin-Huangdao China Department of Mechanical Engineering Yanshan University YSU Qin-Huangdao China

This paper presents a methodology of the dynamic analysis and control for a novel hybrid humanoid robot arm. The hybrid humanoid robot arm under consideration consists of a spherical parallel manipulator (SPM) connecting two revolute pairs in series form. The dynamic model of the hybrid humanoid robot arm has been set up based on the lie group and lie algebra combining with the principle of virtual work which can avoid processing of constraint reaction and the division of logic open-chains, as well as a great quantity of differential operation. Aiming at the parameter uncertainties and disturbances, an adaptive backstepping sliding mode controller is developed. Compared with PD control in trajectory tracking simulation, the results show the advanced of the controller.

关键词： Process control Trajectory Actuators Complexity theory TV

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Adaptive backstepping control of hydraulic servo system with input saturation for rolling mill based on multi-model switching

Adaptive backstepping control of hydraulic servo system with...

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Chinese control Conference (CCC)

作者： Yehong Li Yiming Fang Jianxiong Li Shengli Shi Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China National Engineering Research Center for Equipment and Technology of Cold Strip Rolling Qinhuangdao Qinhuangdao China

For the problems of parameters change abruptly and input saturation of the rolling mill hydraulic servo systems, an adaptive backstepping control method based on multi-model switching is presented in this paper. Firstly, multiple models of the rolling mill hydraulic servo systems under different operating modes are established. Secondly, an auxiliary system is introduced to reduce the effects of input saturation, and the adaptive backstepping controllers are designed for all subsystem. With the help of common Lyapunov theory, the proposed method can guarantee stability of the multi-model switched system under arbitrary switching strategy. Finally, the simulation results demonstrate the effectiveness of the proposed control method.

关键词： Servomotors Switches Backstepping Adaptive systems Valves Actuators

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Stochastic predictive control for energy-efficient cooperative wireless cellular networks

Stochastic predictive control for energy-efficient cooperati...

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IEEE International Conference on Communications (ICC)

作者： Shuhuan Wen F. Richard Yu Jinsong Wu Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China Department of Systems and Computer Engineering Carleton University Ottawa ON Canada Bell Laboratories Shanghai P.R. China

Energy efficiency has become an increasingly important aspect of wireless communications. Coordinated multipoint (CoMP) processing may be considered as a promising approach to improve the energy efficiency, extend the cell coverage, and increase the communication capacity in wireless cellular networks. However, the CoMP communication systems may require a large amount of channel state information (CSI) feedbacks which would be constrained by the wireless backhaul networks. This paper proposes a control theoretical approach to improve the energy efficiency in cooperative wireless cellular networks with CoMP communications. In particular, we propose a stochastic predictive control algorithm to achieve energy-efficient CoMP transmissions through optimal base station grouping. Moreover, since the channel state information in CoMP is often outdated/lost due to the feedback delays in the backhaul network, our proposed approach is based on a new discrete time predictive function control model to mitigate the impacts of packet delay or loss of channel state information. Simulation results show the effectiveness of the proposed scheme.

关键词： Base stations Wireless communication Channel state information Delays Predictive models Predictive control Prediction algorithms

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Reliability Optimization of Multi-state Hydraulic System Based on T-S Fault Tree and Extended PSO Algorithm

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IFAC Proceedings Volumes 2013年第5期46卷 463-468页

作者： Chengyu Yao Bin Wang Dongning Chen Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China (Tel: ***) Hebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control Yanshan University Qinhuangdao China Key Laboratory of Advanced Forging & Stamping Technology and Science (Yanshan University) Ministry of Education of China Qinhuangdao China

In order to overcome the difficulty of constructing the multi-state system's reliability optimization model, and the shortage of premature convergence of PSO (Particle Swarm Optimization) algorithm, a new reliability optimization method based on T-S fault tree and EPSO (Extended PSO) algorithm is proposed. According to the T-S fault tree and T-S rules of the events, the multi-state system reliability optimization model is constructed, at the same time, given constraint conditions on the system cost, weight and volume, and combined with the death penalty to construct the fitness function. Furthermore, EPSO algorithm based on the attraction and repulsion forces in the artificial physics is applied to the reliability optimization of multi-state hydraulic system. Comparing with PSO algorithm, the results of EPSO algorithm are in a better performance in obtaining global optimal solution.

关键词： Hydraulic system Reliability optimization Multi-state T-S fault tree EPSO algorithm

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Reliability Prediction Method of Hydraulic System by Fuzzy Theory

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IFAC Proceedings Volumes 2013年第5期46卷 457-462页

作者： Dongning Chen Chengyu Yao Zhongkui Feng Hebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control Yanshan University Qinhuangdao China (Tel: ***) Key Laboratory of Advanced Forging & Stamping Technology and Science (Yanshan University) Ministry of Education of China Qinhuangdao China Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China

The hydraulic system's reliability is a key factor to ensure the reliable operation of equipment and it is very important to evaluate reliability of hydraulic system. The paper describes a reliability prediction method that may be used to evaluate the reliability of hydraulic systems for industrial applications. The proposed method takes advantage of fuzzy set theory. The aim of this method is to minimize the deficiencies of the traditional reliability prediction methods treating a failure rate as fixed value due to insufficiency practice data. Triangular fuzzy number and Gaussian fuzzy number are used in the application of this prediction method, where environmental factor, derating factor, and other factors are considered. Finally, an example of type crane's hydraulic system is given to verify the feasibility and practicability of the method.

关键词： Type crane Hydraulic system Reliability prediction Fuzzy theory Reliability logic diagram

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The improved load balance adjustor of multi-motor driving system

The improved load balance adjustor of multi-motor driving sy...

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Chinese control Conference (CCC)

作者： Jing Ming Yang Bo Su Jin Lv Hai Jun Che Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao 066004 China Tian Jin Design and Research Institute of Electric Drive Tianjin 300180 China

Multi-motor coaxial connected are used to drive large mechanical load motor drive system. Multi-motor driver's main advantage is can reduce the rotational inertia of the load, increase the rapidity of system, but followed by many motor driver control difficulty increase. Based on one tandem mill motor drive as the background, analyzes the series motor drive a rack rolling work mode, aiming at the load problem of this way of driving motor, design a new kind of load balance adjustor. Using load observer to get the actual load as the load balance adjustor's input, the new control method of quadratic performance indicators single neuron adaptive PID, and joined the feed forward compensation to inhibit speed fluctuations. The results show that, this method can substantially ensure the roll motor load balance, while having a good control effect.

关键词： Observers Neurons Motor drives MATlab Design methodology Channel hot electron injection Digital control

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Application of artificial bee colony algorithm to select architecture of a optimal neural network for the prediction of rolling force in hot strip rolling process

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Journal of Chemical and Pharmaceutical Research 2013年第9期5卷 563-570页

作者： Zhao, Zhiwei Yang, Jingming Che, Haijun Sun, Hao Yang, He Key Lab of Industrial Computer Control Engineering of Hebei Province Institution of Electrical Engineering Yanshan University Qinhuangdao China National Engineering Research Center for Equipment and Technology of Cold Strip Rolling Yanshan University Qinhuangdao China Department of Computer Science and Technology Tangshan College Tangshan China

In the face of global competition, the requirement for dimensional accuracy, mechanical properties and surface properties has become a major challenge on aluminum manufacturing industries. Conventional rolling force formulas, however, provide not more than reasonably exact approximations. The mathematical modeling of the hot rolling process has long been recognized to be a desirable approach to aid in rolling operating practice and the design of mill equipment to improve productivity and quality. However, many factors make the theoretical analysis of the rolling process very complex and time-consuming. This paper presents a prediction method based on the Artificial Bee Colony algorithm and BP neural network, which was developed in order to improve the prediction of rolling force in hot strip rolling process. The architecture of BP neural network is optimized by Artificial Bee Colony algorithm. Comparing with the Sims mathematical model and BP neural network, the experimental results show that the prediction accuracy and error of rolling force is superior to the other two methods, and the predicted rolling force is very close to the practical rolling force.

关键词： Neural networks

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Tension control Study in the Finishing Mill of Aluminum Hot Rolling Mills based on PSO+CMAC

Tension Control Study in the Finishing Mill of Aluminum Hot ...

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2013 3rd International Conference on Advanced Measurement and Test(AMT 2013)

作者： YANG Jing-ming CHEN Li Wang Wan-hong CHE Hai-jun Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University National Engineering Research Center for Equipment and Technology of C.S.R Yanshan University Henan Mingtai Al.Industrial Co.Ltd of

Aimed at decreasing the tension variation during transforming process of "1+4" aluminum hot rolling mills unit in one factory of Henan,we analyzed the factors affecting tension variation and improved the tension formula of hot rolling mills process considering the influence of tension on forward slip *** tension forming process of hot rolling mills from stand 1 to stand 4 is simulated,and the tension structure diagrams of the four-stands are *** to nonlinear characteristics of the aluminum hot rolling mills tension system,conventional PID controller could not meet the requirement of the *** we proposed the mixed control of Cerebellar Model Articulation controller(CMAC)coupled with PID to solve the problem of online parameters control and we used Particle Swarm Optimization(PSO)algorithm to optimize PID controller *** simulation,it could be found that the using of PSO+CMAC significantly reduced surplus adjustment and transition time,and strip tension coupling degree among stands was also reduced.

关键词： aluminum hot rolling mills tension model tension influence coefficient PSO CMAC

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Design of Robotic Visual Servo control Based on Neural Network and Genetic Algorithm

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International Journal of Automation and computing 2012年第1期9卷 24-29页

作者： Hong-Bin Wang Mian Liu Key Lab of Industrial Computer Control Engineering of Hebei Province Institute of Electrical EngineeringYanshan UniversityQinhuangdao 066004PRC

A new visual servo control scheme for a robotic manipulator is presented in this paper, where a back propagation （BP） neural network is used to make a direct transition from image feature to joint angles without requiring robot kinematics and camera calibration. To speed up the convergence and avoid local minimum of the neural network, this paper uses a genetic algorithm to find the optimal initial weights and thresholds and then uses the BP Mgorithm to train the neural network according to the data given. The proposed method can effectively combine the good global searching ability of genetic algorithms with the accurate local searching feature of BP neural network. The Simulink model for PUMA560 robot visual servo system based on the improved BP neural network is built with the Robotics Toolbox of Matlab. The simulation results indicate that the proposed method can accelerate convergence of the image errors and provide a simple and effective way of robot control.

关键词： Visual servo image Jacobian back propagation （BP） neural network genetic algorithm robot control

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