Control Engineering of China

Based on Particle Swarm Algorithm and Differential Evolution Flower Pollination Algorithm for Reactive Power Optimization

MA Li-xin, WANG Li-ya , DONG Ang

2019, 26(4): 613-618.

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Reactive power optimization of the power system is an effective way to keep the power system safe and economical. The main purpose of reactive power optimization is that it can improve the voltage quality and reduce the active power loss of the power system. The reactive power optimization problem of the power system is a complex and nonlinear problem and it should adjust the variables including control variables and state variables. This paper establishes a differential evolution flower pollination algorithm based on particle swarm optimization (DFPA-PSO) in view of the shortcomings of the accuracy of the traditional particle swarm optimization algorithm. The DFPA-PSO combines global search, local search and mutation operations with the flower pollination algorithm. Not only can it widen the search space of particles, but also it increases the diversity of the particles. The DFPA-PSO is applied to the IEEE-14 bus system, which takes into account of loss minimization, voltage level best target and maximum static voltage stability margin. Compared with other algorithms, the results show that DFPA-PSO has stronger global searching ability, faster convergence rate, better robustness and the active power loss is also reduced, thus proving the superiority of DFPA-PSO.

Research on Short-Term Load Forecasting Method Based on Multi-Model Fusion Neural Network

XU Yan-lu, ZHANG Jian-sen, JI Xing, WANG Bin-bin, DENG Zhuo-fu

2019, 26(4): 619-624.

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Power industry requires accurate short-term load forecasting to provide precise load requirements for power system control and scheduling. In order to improve the accuracy of short-term power load forecasting, a method based on FFT optimized ResNet model is proposed. The model first defines power load forecasting as a time series problem, then introduces one-dimensional ResNet for power load regression prediction, and uses FFT to optimize ResNet. The FFT transform of a layer of convolution results gives the model the ability to extract periodic features in the data. Experiments show that the prediction accuracy of FFT-ResNet is better than several benchmark models in 6-hour power load forecasting, which indicates that this method has a good application prospect in power load forecasting.

The Temperature Control System of Vacuum Annealing Furnace Based on Fuzzy Control

LUO Dong-song, SUN Guan-qiong

2019, 26(4): 625-630.

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Aiming at the problems such as slow response, large overshoot quantity, poor anti-jamming ability and hysteresis in the temperature control process of vacuum annealing furnace, this paper puts forward a self-tuning fuzzy PID control strategy and designs a self-tuning fuzzy PID controller to achieve parameter detection of the vacuum annealing process and optimization control of the whole process. This article takes the production equipment and main technical parameters as a starting point, designs an industrial annealing furnace temperature control system which is based on PLC, intelligent instruments and fuzzy PID self-tuning technology. The simulation of the self-tuning fuzzy PID control algorithm is realized by MATLAB and the results are observed, finally the on-optimization of system parameters is realized. The actual operation shows that the system is reliable, the parameters are able to meet the requirements.

Probabilistic PLS Based on Re-extraction of Residuals and Its Application in Process Monitoring

LI Qing-hua, PAN Feng, ZHAO Zhong-gai

2019, 26(4): 631-637.

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The objective of the probabilistic partial least square (PPLS) algorithm is to maximize the correlation of scores of process variables and quality variables, and imposes no restriction on residuals, which will result in large information containing in the residuals. The paper proposes a PPLS algorithm based on re-extraction of residuals. After the development of the PPLS model, this algorithm performs further decomposition on residuals to obtain another set of scores and residuals. As a result, process variables and quality variables can be projected into the correlation score subspace, the score subspace for the quality-irrelevant process variables, the residual subspace for process variables, the score subspace for un-predicted quality variables, and the residual subspace for quality variables. To identify the parameters, the maximum-likelihood method along with the expectation-maximization (EM) algorithm is employed. Moreover, by constructing the monitoring statistics, this model is introduced into process monitoring, and its application in the numerical simulation case illustrates its validity.

Robust Controller Design of Fuzzy Singularly Perturbation Systems with Actuator Saturation

WANG Yang, YANG Yi-yong, SUN Fu-chun, YANG Hong-jiu, MA Xi

2019, 26(4): 638-644.

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This paper is concerned with the robust controller design of the continuous-time fuzzy singularly perturbation systems with actuator saturation. In order to solve the problem of the system with input saturation, a continuous-time singularly perturbation model subject to actuator saturation is given. A parallel distributed compensation (PDC) is used to design the controller and then the control system stability is proved. In addition, the ellipsoid and polyhedron contractive invariant conditions are derived with the help of the auxiliary feedback matrix. Then the stabilizing feedback controller gain to stabilize the system is obtained based on the linear matrix inequalities (LMIs). Furthermore, some necessary and sufficient conditions are given for single input systems. At last, a numerical example is given to illustrate the effectiveness of the designed protocol.

Roughness Measurement of Face Milling Surface Based on Hough Transform and GLCM

MIN Li, WANG Zhe, DONG Shuai

2019, 26(4): 645-651.

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A non-contact roughness measurement method of the face milling surface is studied in this paper，which is based on computer vision theory. The face milling surface image is obtained by the image collection system and is preprocessed. Using Hough transform, the image is rotated to ensure the texture direction is vertical downward. Therefore, we can compute GLCM texture parameters only on this single direction, thus the computing time is saved greatly. The four GLCM texture parameters are extracted as the roughness characteristics of face milling surface. BP neural network is used to model the relations between the GLCM texture parameters and the roughness Ra, and the roughness detection model of face milling surface is established. The experiment results showed that using GLCM based on Hough transform can extract the roughness texture parameters quickly. The BP network model can measure the roughness of face milling surface precisely.

Quality-Related Process Minitoring Based on Kernel Canonical Correlation Analysis

REN Wei, SUO Han-sheng, JIANG Bai-hua, JIA Gui-jin

2019, 26(4): 652-656.

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Aiming at a large number of nonlinear problems in the chemical process, the main existing method is the combination of the kernel algorithm and the partial least squares (KPLS) algorithm. Compared with KPLS algorithm, the method of combining kernel algorithm and canonical correlation analysis (KCCA) algorithm can maximize the correlation between the two groups of variables to achieve better detection results. However, the current KCCA method cannot accurately decompose the data space into parts that are related and unrelated to the key performance indicator (KPI), thereby it ignores the fact that the remaining space still involves some information related to the KPI. In this paper, an improved KCCA is proposed. The method performs singular value decomposition (SVD) on the calculable loadings of kernel matrix, a projection model is obtained in which the kernel matrix is appropriately decomposed into KPI -related and -unrelated parts, and then two statistics are accordingly designed for fault detection. Finally, the Eastman Eastman (TE) process was used to verify the effectiveness and superiority of the proposed method.

Flow and Pressure Fast Control of Water Flow Standard Facility

DANG Shi-zhong, SUN Li-jun, TANG Bing, ZHANG Tao

2019, 26(4): 657-663.

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Mutual coupling between the flow and pressure regulation reduces rapidity of the control system. To address this, taking water standard facility as an example, the coupling between flow and pressure regulation is analyzed. Regulating valve opening and inverter frequency are selected as the main control variable of the flow and the pressure respectively. And a quick flow and pressure control method based on back-propagation(BP) neural network and PID control is proposed. The established neural network model achieves a high predicting precision. The relative error of valve opening prediction is within ±5 % and that of inverter frequency is within ±0.6 %. The experimental results indicate that the control combining BP neural network and PID can achieve a faster flow and pressure regulation: compared with the serial PID control, the regulation time is decreased by 38.5 %~87.3 %, and compared with the parallel PID control, that is decreased by 25.4 %~83.7 %.

Research on the Control Room Design Based on Ergonomics

CHEN Yuan

2019, 26(4): 664-669.

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It aims to study the design method for the human-machine-environment of the control room, which can be used to improve the usage of the machine and the work efficiency and comfort of the operators. Based on ergonomics analysis of the requirement focused on human, the design elements of “machine” are divided into styling basic features, layout, size and control design. The design elements of “environment” are divided into physical environment, material environment, and emotional environment. It summarizes design rules and methods for these elements and makes them meet the requirement of human. The design rules and methods will offer effective reference and suggestion for the ergonomic design of the control room.

The Research on Oil Separation System Performance Evaluation

QU Bo, ZHAO Hong-ye, GAO Xiang, JIANG Bi-bo, ZHAO Deng-quan

2019, 26(4): 670-674.

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The control system as offshore oil platform connecting the core of the control aspects of the production.And PID controller as an important part of the control loop control system, often due to changes in working conditions and other causes varying parameters mutation occurs in the system and cause its performance may be degraded. In this paper, reliability of PID controller performance evaluation method based on minimum variance control for the PID controller. Construction of Offshore oil and gas separation system simulation test model with semi-physical simulation technology, and use this model to simulate offshore oil and gas separation process simulation study of the control system. Multi-bus technology to ensure communication with the computer simulation model, simulation system platform to achieve real-time monitoring parameters. Finally, the test results show that the model simulation of real-time evaluation PID algorithm can effectively improve the performance of the control system.

Traffic Control with Bus Priority Based on Cooperative Chaos PSO Algorithm

CAI Yan-guang, HUANG Bai-liang, CAO Hao, HUANG He-lie, QI Yuan-hang

2019, 26(4): 675-681.

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Focusing on the problem of cooperative control of public transport priority in multi-intersection of the regional road network, this paper proposes a degraded modeling strategy and establishes a multi-intersection traffic signal collaborative optimal control model for the goal of minimizing passenger per-capita delay based on the strategy. By introducing the chaos optimization strategy and multi-group cooperative search strategy, this paper presents a multi-group cooperative chaotic particle swarm optimization algorithm for solving the model. The experiments show that the proposed model and algorithm have good practicability. Compared with the Webster fixed timing scheme, the genetic algorithm and the standard particle swarm algorithm, the proposed algorithm can get better signal timing scheme which can effectively reduce the per-capita delay.

Research on Dynamic Prediction Method of Civil Aircraft Fuel Consumption

ZHANG Jun, YANG Gui-bin, PENG Xiao-feng, MU Xiao-yan

2019, 26(4): 682-687.

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A fuel consumption prediction method with the characteristics of aircraft age based on actual civil aircraft is presented. Through the analysis of the fuel consumption of aircraft operations at different stages, a dynamic prediction method is proposed based on the least squares support vector machine (LSSVM), and an improved particle swarm optimization method is proposed to optimize the LSSVM parameters (IPSO-LSSVM); In order to increase the prediction accuracy, a two-dimensional prediction model based on horizontal and vertical is put forward to make the fuel consumption prediction of aircraft operation more consistent with the actual situation. Finally, the effectiveness of the proposed method is validated by the practical data of aircraft operation, furthermore, the proposed method has better estimating performance than the traditional LSSVM method.

Multiple Model of Boiler NOx Emissions Based on Clustering and Weighted Connection

ZHU Yu-Sen, JIN Xiao-Ming, ZHANG Quan-Ling

2019, 26(4): 688-693.

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According to the characteristics of the boiler combustion process with nonlinear, multiple operating regions and multivariable coupling, a multiple-model modeling method based on fuzzy C means clustering and least squares support vector machines and weighted connections (FCM-LSSVM-WC) is proposed. The influence of inputs on outputs is employed to evaluate the difference between the samples. A BP neural network with "limited" processing is used to calculate the MIV. The membership weights are the MIVs which are used to realize classification and connect the multiple model. The proposed method is verified by taking the circulating fluidized bed boiler on a thermal power plant. Industrial applications show that compared with PLS, LSSVM, FCM-LSSVM, AP-LSSVM, the modeling method can ensure the generalization accuracy requirements, simultaneously possess better tracking ability in predicting NOx emissions of the boiler combustion process.

Study on Fault Diagnosis for Nonlinear Circuit Based on Lissajous Figures

LU Jing, CHEN Xin, LI Zhi-Hua

2019, 26(4): 694-699.

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A method based on the Lissajous figures is put forward in order to solve the defects of fault diagnosis of nonlinear circuits. A Lissajous figure is composed of excitation signals and measurable node signals in the circuit, which can be analyzed by using the curvature energy and feature points of curvature. Then we can get the useful information of Lisasajous figures which will be used to form the fault feature vector. This method has the advantage of combining the information of excitation signals and measurable nodes, and it doesn’t need to analyze the circuit topology. The simulation experiments show that the diagnosis result of the logarithmic amplifying circuit can reach 90 %, which proves that the method has good effect for nonlinear analog circuit fault diagnosis.

The SOH Estimation Of Lithium Battery Based on Feature Vectors Selected By Mutual Information

SUN Hao-hao, PAN Ting-long, WU Ding-hui

2019, 26(4): 700-707.

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Aiming at the small amount of data and nonlinear characteristics of the samples of lithium battery, a method based on mutual information (MI) that chooses SVR model’s input feature vectors is proposed. The error of SVR model’s output is influenced by two factors, including the presentation of input samples and the model’s parameters. Taking into account these two factors, MI method is determined to be used at input samples’ choice. Finally, the mean voltage and maximum and minimum temperature differences in the process of constant current and constant voltage charging are selected as the input feature vector of the model. In addition, the grid search algorithm is selected to optimize the model parameters. Experimental results show that the estimation accuracy and generalization ability of SOH estimation of lithium battery based on SVR of input feature vectors selected by mutual information is better than the BP neural network model.

Finite-frequency Model Reduction of Linear Switched Systems Via Balanced Truncation

DU Xin, WU Xing-cong, HU Zheng , LIU Yuan

2019, 26(4): 708-716.

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In order to improve the approximation performance over a pre-specified finite frequency interval for the model order reduction problem of linear continuous-time switched systems, this paper develops a parameterized frequency-dependent balanced truncation method. Firstly, the generalized finite-frequency performance index is introduced to describe the approximation performance over finite-frequency ranges. Then, the finite-frequency performance for both the continuous-time linear switched system and the corresponding parameterized frequency-dependent mapped discrete-time systems is established. Furthermore, the frequency-dependent balanced truncation algorithm is proposed to generate the desired reduced order model. Finally, a numerical example is given to show the effectiveness of the proposed approach.

A Novel Nonsingular Fast Terminal Sliding Mode Control with Adaptive Boundary Layer

ZHANG Bei-bei, ZHAO Dong-ya, GAO Shou-li, ZHANG Jia-shu

2019, 26(4): 717-723.

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In order to solve the problem that the conventional nonsingular fast terminal sliding mode (NFTSM) control converges slower than classical terminal sliding mode (TSM) control. A novel NFTSM is proposed for nonlinear uncertain systems and faster convergence rate is achieved especially in the neighbor of equilibrium points in comparison with the classical TSM control. Then, a novel NFTSM control with adaptive boundary layer is proposed so as to make systematic variables converge to a residual set in finite time. Furthermore, the application of adaptive boundary layers achieves higher precision and stronger robustness. The stability of closed loop systems is confirmed by Lyapunov theory. Finally, the simulation results verify that higher control precision and superior systematic robustness can be obtained simultaneously by the proposed approach for nonlinear uncertain systems.

Twelve-section Direct Thrust Force Control for Permanent Magnet Linear Synchronous Motor

ZHANG Hong-wei, WANG Xin-huan, CHEN Kai-bin

2019, 26(4): 729-734.

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To deal with the defect of thrust ripples of the traditional six section direct thrust control (DTC) for permanent magnet linear synchronous motor (PMLSM), a novel DTC strategy of PMLSM combined 12 stator flux linkage sectors and 13 voltage space vectors is proposed. A novel voltage selection table is designed. The way of three and two devices conducting interactively is applied to realize 13 voltage space vectors. The method is simulated through Matlab/Simulink. The results of simulation verify the better performance of the twelve-section control method, which can reduce the thrust and flux linkage ripple.

Novel Flux Sliding⁃mode Observer for Direct Torque Controlled Induction Motor Driving Systems#br#

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YU Jian-guo, XIAO Hai-feng, XU Yu-hao

2019, 26(4): 735-739.

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The flux estimation of the induction machine determines the performance of the drive system. The parameters of induction motors are prone to easy impaction with the aid of the conventional flux estimation. A new stator flux estimation is presented, which realizes accurately in the stationary frame of axes by the sliding-mode function. At the same time, the observer of the model contains a rotor-speed-independent term behavior, and estimates the rotor flux and the exact speed of the rotor indirectly. Theoretical analysis and MATALIB simulation results confirm the robustness, accuracy, quick response of the drive.

Design and Analyze a Window Cleaning Robot

CHEN Hai-chu, LI Qiang

2019, 26(4): 740-745.

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It designed a window cleaning robot (WCR) in this paper. In the bottom of the WCR, it designed two vacuum suckers and connected with a vacuum pump by hoses which exhausts the air in the sucker and creates vacuum in it. Then, negative pressure makes by atmosphere, which can make the WCR adhere to window glass. Two crawler wheels are designed to increase the contact area between the WCR and glass, which can improve the driving force of the WCR when it moves on glass. Also, it designed a negative pressure sensor and connected with the vacuum suckers by hoses, which can realize real-time monitoring the negative pressure of the WCR, and make the control system automatically adjust the PWM control parameters on the vacuum pump motor to change the negative pressure in the vacuum suckers of the WCR and make it adapt different glass with different friction coefficient and move freely. According to the designed structure parameters of the WCR, it set up the simple mechanical model, and then analyzed the negative pressure model, driving model of the WCR. At last, it made the WCR sample and tested the adherence ability and the moving ability of it. The experimental results prove the load capacity of the WCR is over 10kg when it adheres to smooth glass window, and can move freely on different glass with different friction coefficient.

Process Fault Diagnosis Using Kernel Canonical RF

CAO Yu-ping, LU Xiao, TIAN Xue-min, DENG Xiao-gang

2019, 26(4): 746-751.

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Traditional rotation forest based on principal component analysis does not consider time series correlation of features. Therefore, a fault diagnosis method based on kernel canonical rotation forest is proposed to improve fault diagnosis accuracy in nonlinear dynamic processes. For the proposed method, random forest features are projected to the high dimension linear reproducing kernel Hilbert space by using unknown nonlinear mapping. Canonical variate analysis is used to extract dynamic correlation information, and to produce irrelevant features. Kernel function is used to solve the unknown nonlinear mapping problem. In order to avoid kernel matrix singular problems in traditional kernel canonical variate analysis, canonical variables are extracted in kernel principal space, and used to train decision trees. The proposed method takes nonlinear correlation and dynamic correlation of random forest features into account. Meanwhile, the difference between decision trees is increased, which is helpful to improve the accuracy of fault diagnosis. The effectiveness of the proposed method is demonstrated through a case study of the Tennessee Eastman process.

Prediction of Aircraft Fuel Flow Based on QAR Data

CHEN Cong, SHI Li-zhong, GAO Jie, DONG Shi-yao, CAO Jin-jin

2019, 26(4): 752-758.

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At present, most of the domestic service B737NG aircraft using CFM56-7 type engine. As the research object, by decoding the vast data of quick access recorder (QAR), the denoising method with stationary wavelet Rigorous SURE, analyzing the engine speed, N1 speed, N2 speed, exhaust gas temperature (EGT)and other parameters, processing linear or nonlinear regression analysis, combined with the flight phase rational division and modeling, analysis of the main performance parameters and aircraft fuel flow (FF) relationship, full range of the FF prediction model is established. Through MATLAB-Simulink simulation analysis, 5 representative cases of the long flight, short-middle flight, take off-go around flight and complex flight are selected, and errors of the FF prediction model and the actual flow are compared to prove that the model is reasonable and universal.

Study on Early-warning of Coal and Gas Outburst Based on Wavelet Packet Entropy and Data Fusion

TU Nai-wei, YAN Xin

2019, 26(4): 759-764.

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By analyzing gas concentration change before coal and gas outburst, an early-warning method based on wavelet packet entropy and data fusion was proposed. The data fusion method based on the arithmetic mean and batch estimation is used to deal with the collected multi-sensor gas data so as to improve the precision and reliability of these data. The wavelet packet entropy feature is used to quantize the disordered degree of gas concentration change. An early-warning model for coal and gas outburst based on the wavelet packet entropy feature is used to carry out real-time early-warning. A selection method of the wavelet packet decomposition level based on restructure signal energy was given to scientifically obtain the wavelet packet decomposition level. The proposed method was validated using practical measured time-series data. The simulation example shows that the proposed method can monitor the risk for coal and gas outburst in the working face when the gas concentration change is abnormal before coal and gas outburst.

A Belief Rule Based Inference Method for Process Alarm Prognosis

ZHANG Ze-sheng, LI Hong-guang, YANG Bo, ZHANG Jing

2019, 26(4): 765-772.

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To better utilize historical alarm information of industrial processes, this paper introduces a belief rule base model based process alarm time series prognosing inference approach which is able to evaluate the process safety performance in the future. The belief rule base model involved is established using historical alarm data of process variables, while a particle swarm optimization algorithm is used for the model parameter learning. The online implementation of the model can help predict trends of the process alarm states in the future. A numerical simulation and industrial process alarm data are used to demonstrate the effectiveness of the approach with satisfying prognosis results.

Research on Multi-class Mixed Face Recognition Based with RBF Support Vector Machine

ZHU Shu-xian, LI Yun, ZHU Yong-jun, WU Zheng-tian

2019, 26(4): 773-776.

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RBF support vector machine (SVM) is widely used in pattern recognition and fault detection because of its stability and high recognition rate. Unlike other literatures, this paper applies RBF support vector machine to multi-class mixing face recognition. On the one hand, after multi-class mixing, the performance of RBF kernel function for support vector machine is examined whether investigated or not. On the other hand, this method has more practical value. Experiments show that the performance of multi-class mixing samples is slightly degraded compared with that of single-class RBF support vector machine, but the recognition rate is still very high, which verifies the effectiveness and practicability of the method.

Research on Position Sensorless Control of PMSM Based on Cubature Kalman Filter

WANG Di

2019, 26(4): 777-782.

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In order to overcome the problems of model inaccuracy and external disturbance led to the decrease of the kalman filtering accuracy, an improved cubature kalman filter is set up in permanent magnet motor position sensor less control algorithm. The state equation of Permanent magnet motor in the two-phase stationary coordinates is established, and gaussian process regression is used to identy the system state and measurement, and alternative cubature kalman filter in the system state equation and measurement equation. The identification accuracy of cubature KF is retained, the system robustness is improved with model inaccuracy and external disturbance. The experimental results show that the improved cubature kalman filtering algorithm identification accuracy, real-time, and robustness are better than kalman filter and extended kalman filtering and cubature kalman filtering algorithm, and it has a wider application prospect.

An Improved RFID Mutual Authentication Security Hardening Protocol

TAN Feng

2019, 26(4): 783-789.

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For the problem that the label information data is easily disclosed in LOT (Internet of Things), this paper proposes an improved RFID mutual authentication security hardening protocol. Unlike the traditional RFID authentication protocol, the proposed protocol authenticates the identity of members through authentication methods based on zero-knowledge proof, utilizes real-time information exchange between certifiers and verifiers to complete the zero-knowledge proof, and programs the participants’ identity security to their own identity key’s security. This protocol’ formal proof, including three aspects: secret proof, certification proof and label untraceability, shows that this protocol satisfies mutual authentication requirements of RFID.

Research on the Dephosphorization Ladle Scheduling algorithm of Steelmaking-refining-continuous Casting Process

LIU Wei, PANG Xin-fu, CHAI Tian-you

2019, 26(4): 790-798.

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At present, the steel plant adopts the manual method to carry out the ladle scheduling, the optimization results of the ladles selection are not good enough because of the uncertainty of ladle temperature, ladle life and ladle materials. Ladle scheduling is to select and distribute the ladles based on the heat plan, providing that the process time of heats and the process requirement of the dephosphorization for heats are given. The selection of dephosphorization ladle takes the performance index of maximum temperature, highest life, and the remaining online ladle life. Take the ladle temperature in the specified range, the ladle life within the specified range and the ladle maintenance end time as early as the furnace processing time as the constraints to determine the dephosphorization. This paper adopts consistency classification method to obtain the dephosphorization ladle rule, and use the rule-based reasoning to get dephosphorization ladle. The performance is tested by simulation experiments. The experimental results show that, compared with the manual scheduling, the algorithm enables the production work well and improve the production efficiency, and improve the economic efficiency of enterprises.

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