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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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Two-Stage Force Particle Swarm Optimization Algorithm and Application in Hydraulic System Reliability Optimization

Two-Stage Force Particle Swarm Optimization Algorithm and Ap...

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2015 International Conference on Fluid Power and Mechatronics

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

In view of the particle swarm optimization(PSO) algorithm's shortages of easily trapping into local optimum and premature convergence, an improvement research from the perspective of interaction mechanism among particles is made in this paper. Consider advantages and disadvantages of the current improved PSO algorithms from the aspect of force, the two-stage force particle swarm optimization(TFPSO) algorithm is proposed. The proposed algorithm, which employs staged search strategy to achieve a trade-off between global exploration and local exploitation abilities, divides search process into two stages and constructs corresponding force rules combining with the idea of attractive and repulsive forces in artificial physics. In order to demonstrate the performance of the proposed algorithm in solving optimization problems, TFPSO algorithm is compared with some well-known PSO algorithms in reliability optimization for hydraulic system. Comparison results show that TFPSO algorithm obtains the best optimization results and enhances the performance of PSO in terms of accuracy of the optimal solution and local search ability.

关键词： PSO algorithm force rule TFPSO algorithm reliability optimization hydraulic system

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Fuzzy PID control of Space Manipulator for Both Ground Alignment and Space Applications

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International Journal of Automation and computing 2014年第4期11卷 353-360页

作者： Fu-Cai Liu Li-Huan Liang Juan-Juan Gao Key Lab of Industrial Computer Control of Hebei Province Yanshan University

Considering gravity change from ground alignment to space applications, a fuzzy proportional-integral-differential(PID)control strategy is proposed to make the space manipulator track the desired trajectories in different gravity environments. The fuzzy PID controller is developed by combining the fuzzy approach with the PID control method, and the parameters of the PID controller can be adjusted on line based on the ability of the fuzzy controller. Simulations using the dynamic model of the space manipulator have shown the effectiveness of the algorithm in the trajectory tracking problem. Compared with the results of conventional PID control,the control performance of the fuzzy PID is more effective for manipulator trajectory control.

关键词： Space manipulator trajectory tracking ground alignment space applications fuzzy proportional-integral-differential (PID) gravity environment

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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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Two-stage force particle swarm optimization algorithm and application in hydraulic system reliability optimization

Two-stage force particle swarm optimization algorithm and ap...

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International Conference on Fluid Power and Mechatronics (FPM)

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

ISBN: (纸本)9781479987719

In view of the particle swarm optimization (PSO) algorithm's shortages of easily trapping into local optimum and premature convergence, an improvement research from the perspective of interaction mechanism among particles is made in this paper. Consider advantages and disadvantages of the current improved PSO algorithms from the aspect of force, the two-stage force particle swarm optimization (TFPSO) algorithm is proposed. The proposed algorithm, which employs staged search strategy to achieve a trade-off between global exploration and local exploitation abilities, divides search process into two stages and constructs corresponding force rules combining with the idea of attractive and repulsive forces in artificial physics. In order to demonstrate the performance of the proposed algorithm in solving optimization problems, TFPSO algorithm is compared with some well-known PSO algorithms in reliability optimization for hydraulic system. Comparison results show that TFPSO algorithm obtains the best optimization results and enhances the performance of PSO in terms of accuracy of the optimal solution and local search ability.

关键词： Force Reliability Optimization Particle swarm optimization Hydraulic systems Convergence Fault trees

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Prediction of chaotic time series based on interval type-2 T-S fuzzy system

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Journal of Computational Information Systems 2014年第13期10卷 5403-5412页

作者： Wang, Shu'en Dou, Jinmei Liu, Yan Liu, Fucai Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China

Fuzzy model based chaotic time series prediction has been extensively studied. However, traditional type-1 fuzzy system, whose membership functions are type-1 fuzzy set, has its limitation in handling uncertainties. Type-2 fuzzy system, whose membership functions are type-2 fuzzy set, has more adaptable parameters and design degree of freedom, making it more powerful in dealing with nonlinear and uncertain problems. In this paper, the interval type-2 fuzzy system, whose fuzzy space is divided by mesh diagonal method and consequent parameters are adapted by the forgetting factor recursive least square method while previous parameters remain unchanged, is applied to modeling and prediction of chaotic time series. Finally, the proposed scheme is tested by prediction of Mackey-Glass chaotic time series in case of noise free and noise condition, the simulation results show that the proposed scheme has a higher prediction accuracy and is powerful in handling uncertainties. © 2014 by Binary Information Press

关键词： Forecasting

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Robust H-infinity space vector model of permanent magnet synchronous motor based on genetic algorithm

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Journal of Computational Information Systems 2014年第14期10卷 5897-5905页

作者： Zang, Huaiquan Qin, Zhe Dai, Yan Key Lab of Industrial Computer Control Engineering of Hebei Province Institute of Electrical and Engineering Yanshan University Qinhuangdao China

A robust H-infinity controller based on genetic algorithm optimization is designed for space vector control model of permanent magnet synchronous motor to improve the speed of tracking performance. A space vector control model of the system has been formed, and a quadrature-axis current loop transfer function based on a dynamic mathematical model of motor combined with decoupling technology is established. In order to obtain a good dynamic performance, the selection of the weighting function uses genetic algorithm to optimize in the process of designing the robust H-infinity. Simulation and analysis results show that the robust controller that based on genetic algorithm effectively improves the tracking performance and robustness of the system. © 2014 by Binary Information Press

关键词： Genetic algorithms

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Distributed edge control for multi-agent based on binocular vision system

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Journal of Computational Information Systems 2014年第7期10卷 2907-2915页

作者： Shao, Nuan Li, Huiguang Zou, Liying Liu, Le Key Lab. of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao 066004 China

For multiple Lagrange systems, the objective of distributed edge control problem is to drive all the followers converge to the edge of the convex hull spanned by the leaders. In this approach, we choose binocular vision system as sensing devices. Firstly, a pseudo-stereo visual model is proposed based on the concept of depth information sensitivity which can be used to provide the generalized coordinates of the leaders for all followers in the image coordinate system. Then, based on this new model, a distributed adaptive control algorithm is designed to compensate for the parametric uncertainties under a directed graph. Finally, the proposed control algorithm is applied to the multi-manipulators grasping control, simulation examples are provided to show the effectiveness of the controller. © 2014 Binary Information Press.

关键词： Multi agent systems

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Disturbance observer based robust backstepping control for electro-hydraulic servo system

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ICIC Express Letters 2015年第9期9卷 2467-2473页

作者： Shi, Shengli Li, Jianxiong Fang, Yiming Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University No. 438 Hebei Avenue Qinhuangdao China College of Science Yanshan University No. 438 Hebei Avenue Qinhuangdao China

The position tracking control scheme is investigated in this paper for electrohydraulic servo system with parameter uncertainties, external disturbances and actuator saturation. Disturbance observer is employed to estimate the compound disturbance, and robust backstepping control law based on the disturbance observer is designed to ensure the system output tracks the reference signal. A second-order integral filter is proposed to avoid the repetitive differentiation of the virtual control. The robustness of the controller is guaranteed by robust items, and the tracking performance can be achieved by the proposed method. Simulation results are presented to verify that the control strategy can realize good position tracking. © ICIC International 2015.

关键词： Tracking (position)

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Design and analysis of an adaptive RBF neural network controller for two-eye robot visual system

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Journal of Computational Information Systems 2014年第17期10卷 7517-7527页

作者： Li, Guoyou Li, Hengrui Zhang, Boqiang Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China

Aiming at solving the problem of control performance due to the inaccuracy in the two-eye robot visual servoing system modeling, an RBF neural network based on the adaptive algorithm is proposed, which is used to identify the uncertainties of the modeling. A dynamic control law is put forward for compensating the uncertainties of the modeling, an adaptive law is put forward to ensure the boundness of the neural network's weights and a kinematic control law is put forward which takes the errors between the actual position and the target position of the manipulators as input. The stability of the two control laws is demonstrated using the Lyapunov stability theorems, and the stability of the manipulator system which is under the proposed control laws is illustrated. The results, which are obtained from the Matlab simulation, verify the high performance of the control strategy.

关键词： Kinematics

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