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11th International Conference on Natural Computation, ICNC 2015

作者： Li, Xia Niu, Peifeng Li, Guoqiang Liu, Jianping Hui, Huihui Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao066000 China College of Mathematics and Information Technology Hebei Normal University of Science and Technology Qinhuangdao066000 China

ISBN: (纸本)9781467376792

The Teaching-Learning-Based Optimization(TLBO) algorithm does not require special parameters setting for working the algorithm, but there are some shortcomings such as slow convergence speed and long running time. Therefore, some improvements have been done on the TLBO algorithm in the paper. Firstly, the population initialization of the TLBO algorithm is random, which does not ensure the uniform distribution of initial solutions in the solution space, and then it will affect the algorithm's efficiency to some extent. Therefore, the paper proposes opposing-based learning to initialize and renewal the population of the TLBO algorithm. Secondly, to efficiently speed up the convergence speed of the algorithm, a linear decreasing inertia weight (DIW) strategy and two nonlinear DIW strategies (a parabola opening upwards and a parabola opening downwards curve) are combined with the TLBO respectively. Finally, improved TLBO algorithms have been evaluated by 13 benchmark functions. The experimental results show that improved TLBO algorithms have much better optimization performances than the TLBO algorithm on most benchmark functions. © 2015 IEEE.

关键词： Learning algorithms

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Direct Fuzzy Adaptive Inverse control Based on Particle Swarm Optimization Algorithm

Direct Fuzzy Adaptive Inverse Control Based on Particle Swar...

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第26届中国过程控制会议（CPCC2015）

作者： LIU Fu-cai LIU Nan DOU Jin-mei Key Lab of Industrial Computer Control Engineering of Hebei Yanshan University

T-S fuzzy inverse model identification based on particle swarm optimization(PSO) algorithm is proposed for a class of nonlinear process, and it is applied to direct adaptive inverse control of nonlinear system. In the first, the fuzzy space is divided by fuzzy grid diagonal method and the Gaussian function with uncertain center and width is used as the membership function, the fuzzy inverse model is obtained by using PSO algorithm to optimize both of the antecedent and consequent parameters in the process of inverse modeling in off-line manner. Afterwards the initial inverse model is connected to the plant in series and the consequent parameters of the inverse model are tuned by the variable-step least mean square(VSLMS) algorithm in on-line manner while its copy is connected to the plant as system controller. This approach realizes the adaptive inverse control of nonlinear system based on PSO algorithm. Simulation results on a hybrid nonlinear system and level control of a spherical tank show the validity of this method.

关键词： particle swarm optimization T-S fuzzy inverse model adaptive inverse control variable-step least mean square nonlinear system

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Distributed containment control with dynamic leaders based on binocular vision

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Journal of Information and Computational Science 2015年第4期12卷 1319-1327页

作者： Shao, Nuan Li, Huiguang Liu, Le Li, Guoyou Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China

In this paper, we study the distributed containment control problem for multi-robot system with dynamic leaders in the presence of parametric uncertainties. The binocular vision system is used as the sensing device to provide real-time status information for the followers, so firstly, a new binocular visual model for dynamic target is established. Then RBF neural networks are employed to approximate the unknown nonlinear functions in the system model of followers, and the approximated values are introduced into the designed sliding mode controllers to make compensation. Finally, simulation results are presented to show the validity of the proposed control strategy. ©, 2015, Binary Information Press

关键词： Multipurpose robots

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Cooperative Spectrum Sensing Based on Double Thresholds Energy Detection over Imperfect Reporting Channels

Cooperative Spectrum Sensing Based on Double Thresholds Ener...

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International Conference on Instrumentation, Measurement, computer, Communication and control (IMCCC)

作者： Zi Zhang Xinbin Li Zhixin Liu Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Hebei China

ISBN: (纸本)9781467377249

In this paper, the problem of hierarchical cooperative spectrum sensing using double thresholds energy detector over imperfect reporting channels will be investigated. When taking error effect on reporting decision into consideration, the sensing performance will be affected. In the proposed scheme, we analyze the performance of hierarchical cooperative spectrum sensing over imperfect reporting channels and the "majority-decision-aided" weighting rule will be used in the fusion center. The analysis and simulation results show that there is a significant performance loss when the decisions based on hierarchical spectrum sensing are transmitted over imperfect reporting channels and the sensing performance is improved by employing the new weighting rule.

关键词： Sensors Cognitive radio Signal to noise ratio computers Propagation losses Reliability Fading channels

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Fuzzy adaptive robust control for space robot considering the effect of the gravity

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Chinese Journal of Aeronautics 2014年第6期27卷 1562-1570页

作者： Qin Li Liu Fucai Liang Lihuan Gao Jingfang Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University

Space robot is assembled and tested in gravity environment, and completes on-orbit service（OOS） in microgravity environment. The kinematic and dynamic characteristic of the robot will change with the variations of gravity in different working condition. Fully considering the change of kinematic and dynamic models caused by the change of gravity environment, a fuzzy adaptive robust control（FARC） strategy which is adaptive to these model variations is put forward for trajectory tracking control of space robot. A fuzzy algorithm is employed to approximate the nonlinear uncertainties in the model, adaptive laws of the parameters are constructed, and the approximation error is compensated by using a robust control algorithm. The stability of the control system is guaranteed based on the Lyapunov theory and the trajectory tracking control simulation is performed. The simulation results are compared with the proportional plus derivative（PD） controller, and the effectiveness to achieve better trajectory tracking performance under different gravity environment without changing the control parameters and the advantage of the proposed controller are verified.

关键词： Fuzzy adaptive Microgravity Robustness Space robot Trajectory tracking control

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Multi-objective power dispatch based on Q-learning algorithm considering reliability of ICPR

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Journal of Computational Information Systems 2015年第13期11卷 4665-4672页

作者： Wu, Zhongqiang Zhao, Liru Jia, Wenjing Wu, Changhan Wu, Lina Key Lab of Industrial Computer Control Engineering of Hebei Province College of Electric Engineering Yanshan University Qinhuangdao China

To solve the power dispatch problem with intermittent clear power resource (ICPR), the optimal power model which contains wind power, photovoltaic power, coal-fired power and variation load is built. The model allows the most utilization about ICPR, and minimizes the emission of coal-fired plants. On the hand of algorithm, Q-learning algorithm is used to solve this multi objectives power dispatch (MOPD) problem. The optimal solution is obtained by the fuzzy-based definition algorithm from the Pareto-front got by Q-learning algorithm. Simulation on IEEE30 system is curried out to illustrate the efficiency of proposed model and algorithm. ©, 2015, Journal of Computational Information Systems. All right reserved.

关键词： Wind power

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controlling velocity of armature based on the multi-pulse forming network

Controlling velocity of armature based on the multi-pulse fo...

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International Conference on Pulsed Power

作者： Zhenchun Wang Zhiyong Bao Fucai Liu Lijie Jia Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University QinHuangDao China

Given the accuracy of the muzzle velocity of railgun, based on the capacitive energy-stored type of pulse forming network, electromechanical model of railgun is established, a multistage multi-module velocity control strategy based on velocity closed-loop control is proposed. Through the simulate analyze of the multiple of PFU by using electromechanical model of railgun, the best PFU electrical parameters and the best multi-stage control strategy are obtained, test shows that the system model and the control strategy can be applied to the actual system experiment, and achieve good results.

关键词： Interference Load modeling Accuracy Velocity control Electromagnetics Capacitors

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Robust Iterative Learning control of Single-phase Grid-connected Inverter

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International Journal of Automation and computing 2014年第4期11卷 404-411页

作者： Zhong-Qiang Wu Chun-Hua Xu Yang Yang Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University

For the single phase inductance-capacitance-inductance(LCL) grid-connected inverter in micro-grid, a kind of robust iterative learning controller is designed. Based on the output power droop characteristics of inverter, the current sharing among the inverters is achieved. Iterative learning strategy is suitable for repeated tracking control and inhibiting periodic disturbance, and is designed using robust performance index, so that it has the ability to overcome the uncertainty of system parameters. Compared with the repetitive control, the robust iterative learning control can get high precision output waveform, and enhance the tracking ability for waveform, and the distortion problem of the output signal can be solved effectively.

关键词： Inductance-capacitance-inductance (LCL) filter iterative learning robust control power droop-characteristics repetitive control

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Hydraulic System Reliability Modelling and Analysis Based on Convex Model Bayesian Network

Hydraulic System Reliability Modelling and Analysis Based on...

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International Conference of Fluid Power and Mechatronics

作者： Dongning Chen Yuliang Zhang Chengyu Yao Shijun Lv Key Lab of Heavy Machinery Fluid Power Transmission and Control of Hebei Province Yanshan University Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University

ISBN: (纸本)9781479987719

Compared with the probabilistic method, the non-probabilistic convex model only requires a small number of samples and experiences to obtain the variation bounds of the ambiguous and imprecise parameters, and whereby makes the reliability analysis very convenient. In this paper, hydraulic lifting system reliability model is established and the reliability of the system is analyzed by convex model Bayesian network, then the reliability indices are described in the form of interval parameters. Using ellipsoid model to constrain interval model, the more accurate reliability indices are obtained. Finally the convex model Bayesian network is compared with traditional Bayesian network. All the reliability indices obtained by convex model Bayesian network provide the basis for condition-based maintenance, fault diagnosis and reliability design.

关键词： Reliability analysis Hydraulic system Bayesian network Convex model

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Hydraulic System Reliability Modelling and Analysis Based on Convex Model Bayesian Network

Hydraulic System Reliability Modelling and Analysis Based on...

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2015 International Conference on Fluid Power and Mechatronics

Compared with the probabilistic method, the nonprobabilistic convex model only requires a small number of samples and experiences to obtain the variation bounds of the ambiguous and imprecise parameters, and whereby makes the reliability analysis very convenient. In this paper, hydraulic lifting system reliability model is established and the reliability of the system is analyzed by convex model Bayesian network, then the reliability indices are described in the form of interval parameters. Using ellipsoid model to constrain interval model, the more accurate reliability indices are obtained. Finally the convex model Bayesian network is compared with traditional Bayesian network. All the reliability indices obtained by convex model Bayesian network provide the basis for condition-based maintenance, fault diagnosis and reliability design.

关键词： reliability analysis hydraulic system Bayesian network convex model

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