Proceedings of the 2001 Winter Simulation Conference

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印度月球初航航天器即将发射

印度月球初航航天器即将发射

图4 EKF 仿真结果Fig.4 Monte Car lo simula tion r esults o fEKF图5 UKF 仿真结果Fig.5 Monte Car lo simulat ion r esults of UKF 参考文献[1] 罗建军,袁建平.利用GP S 进行航天器姿态确定的EKF 方法[J ].航天控制,1999,17(2):25229.[2] J UL IER S J ,U HL MAN J K ,HU GH F.A newmethod for the nonlinear tra nsfo rmation of means and cova riance s in filter s a nd e stimation [J ].IEEE Trans 2actions on Automatic Cont rol ,2000,45(3):4772482.[3] J UL IER S J ,U HLMAN J K .Unscente d f iltering andnonlinear estimation [J ].Proceedings of the I EEE ,2004,92(3):4012422.[4] MA G F ,J IANG X Y.Unsce nted K alman filte r fo rspacecraft at titude e stima tio n and calibration usingmagnetometer mea surement s [J ].G ua ngzhou :Pro 2ceedings of the 4th Con f ere nce on Machine Learing and Cyber netic s ,2005.[5] 张红梅,邓正隆,高玉凯.U KF 在基于修正罗得里格参数的飞行器姿态确定中的应用[J ].宇航学报,2005,26(2):1642167.[6] SHARMA R S ,TEWAR I A T.Optimal nonlineart racking of spacecraft attitude maneuver s [J ].I EEE Transactions on Control Systems Techn ology ,2004,12(5):6772682.[7] 张贵明,黄顺吉.SAR 卫星GPS 轨道和姿态测量技术研究[D ].电子科技大学博士学位论文,2001.印度月球初航航天器即将发射印度太空研究组织的增强型极轨卫星运载火箭(PSLV)的装配已经开始,准备在今年9月从印度东海岸Sati sh Dhawan 航天中心发射印度的月球初航(Chandaryaan 21)任务。

数学类SCI分区查询

数学类SCI分区查询

SIAM REV SIAM REVIEWJ AM MATH SOC JOURNAL OF THE AMERICAN MATHEMATICAL SOCIETYANN MATH ANNALS OF MATHEMATICSB AM MATH SOC BULLETIN OF THE AMERICAN MATHEMATICAL SOCIETYJ R STAT SOC B JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATIS J AM STAT ASSOC JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION STRUCT EQU MODELING STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL MULTIVAR BEHAV RES MULTIVARIATE BEHAVIORAL RESEARCHINT J INFECT DIS DYNAMICS OF CONTINUOUS DISCRETE AND IMPULSIVE SYSTEMS-SE COMMUN PUR APPL MATH COMMUNICATIONS ON PURE AND APPLIED MATHEMATICSRISK ANAL RISK ANALYSISANN STAT ANNALS OF STATISTICSSIAM J SCI COMPUT SIAM JOURNAL ON SCIENTIFIC COMPUTINGMATH MOD METH APPL S MATHEMATICAL MODELS & METHODS IN APPLIED SCIENCESSIAM J MATRIX ANAL A SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS MULTISCALE MODEL SIM MULTISCALE MODELING & SIMULATIONINVENT MATH INVENTIONES MATHEMATICAEJ R STAT SOC A STAT JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATIS STAT SCI 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5.3326672922245921782519.66667 0894-0347数学 2.552 2.3 2.581 2.4853331457123011041263.66667 0003-486X数学 2.4262 1.845 2.0933336285529654555678.66667 0273-0979数学 2.385 1.8 2.962 2.3823332304194919862079.66667 1369-7412数学 2.3152 2.691 2.3223337168629556426368.33333 0162-1459数学 2.171 1.7 1.978 1.95314510131941272513476.3333 1070-5511数学 2.143 1.2 1.919 1.7693332549209317812141 0027-3171数学 2.095 1.20.952 1.4033331394124610551231.66667 1201-9712数学 2.0620.20.0860.7943335114920193.333333 0010-3640数学 2.031 1.8 1.694 1.8553334407390038584055 0272-4332数学 1.938 1.5 1.321 1.5896672521204419772180.66667 0090-5364数学 1.902 1.7 1.625 1.7347253631061186560.33333 1064-8275数学 1.824 1.5 1.231 1.5213334360367731623733 0218-2025数学 1.805 1.2 1.31 1.454333894768674778.666667 0895-4798数学 1.798 1.10.727 1.2243331658149711341429.66667 1540-3459数学 1.723 1.7 1.135 1.52966727814955160.666667 0020-9910数学 1.659 1.7 1.926 1.7456675025443846424701.66667 0964-1998数学 1.547 1.10.796 1.1393331296119310991196 0883-4237数学 1.531 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JCR期刊影响因子及分区情况 中科院SCI期刊分区表 工程技术类

JCR期刊影响因子及分区情况 中科院SCI期刊分区表 工程技术类

阈值期刊数期刊数3.4585225序号12345678910111213141516171819 2021 222324 25 26 2728 2930 31 32BIOTECCUBIOTECHNOLOGYAnnualBiomolecular EngineePROGRESSCHARACTERIZATION OF MACM CCHEMINTERNATIOPROCJOU377分区ADVANNUALRESEARCHNANaNATUEnergy EducPROGRESMATERIALSR-REPORTSPROGCOMBUSTION SCIENANNUALENGINEERINGPROGRETRENDADVANCEDNanomedicand MedicinePROGRE111111111111111111111111111111111062-7995PROG PHOTOVOLTAICS0018-9219P IEEE0021-9517J CATAL0950-6608INT MATER REV1549-9634NANOMED-NANOTECHNOL1947-5438ANNU REV CHEM BIOMOL1613-6810SMALL0960-8974PROG CRYST GROWTH CH1473-0197LAB CHIP0360-0300ACM COMPUT SURV1558-3724POLYM REV0897-4756CHEM MATER1369-7021MATER TODAY0142-9612BIOMATERIALS1531-7331ANNU REV MATER RES1936-0851ACS NANO0079-6816PROG SURF SCI0958-1669CURR OPIN BIOTECH0167-7799TRENDS BIOTECHNOL1616-301X ADV FUNCT MATER0734-9750BIOTECHNOL ADV0935-9648ADV MATER0927-796X MAT SCI ENG R1748-0132NANO TODAY0360-1285PROG ENERG COMBUST1530-6984NANO LETT1523-9829ANNU REV BIOMED ENG1748-3387NAT NANOTECHNOL1087-0156NAT 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B-CHEM0570-4928APPL SPECTROSC REV1557-1955PLASMONICS1077-260X IEEE J SEL TOP QUANT1063-6706IEEE T FUZZY SYST0308-8146FOOD CHEM0957-4484NANOTECHNOLOGY0730-0301ACM T GRAPHIC0306-2619APPL ENERG1944-8244ACS APPL MATER INTER0006-3592BIOTECHNOL BIOENG1941-1413ANNU REV FOOD SCI T0885-8993IEEE T POWER ELECTR0961-9534BIOMASS BIOENERG0013-4686ELECTROCHIM ACTA1566-1199ORG ELECTRON1359-6454ACTA MATER0304-3894J HAZARD MATER1361-8415MED IMAGE ANAL0737-0024HUM-COMPUT INTERACT0733-8716IEEE J SEL AREA COMM1475-2859MICROB CELL FACT0129-0657INT J NEURAL SYST0924-2244TRENDS FOOD SCI TECH0360-3199INT J HYDROGEN ENERG9798 99 100 101 102 103 104 105106 107108 109 110 111 112 113 114 115116 117 118 119120 121 122 123 124125 126IEEE TRAMAN AND CYBERNETICSCYBERNETICSInternationScienceJOURNAIEEE CompuJournaManagementIEEE TRPROCESSINGCOMICROSCJournal ofBiomedical MaterialCOMINTERNROBOTICS RESEARENVIROSOFTWAREDECOMPTECHNOLOGYIEEE COMJOURNRESEARCHMARIJournINFOInternationCHEMICASCIENCADVANCED MATERIAFOOIEEE JOCIRCUITSINTERNATMICROBIOLOGYBIOMEAPPLIEBIOTECHNOLOGYCBiomeMechanobiology2222222222222222222222222222220266-3538COMPOS SCI TECHNOL0168-1656J BIOTECHNOL1556-603X IEEE COMPUT INTELL M1452-3981INT J ELECTROCHEM SC1392-3730J CIV ENG MANAG1057-7149IEEE T IMAGE PROCESS1083-4419IEEE T SYST MAN CY B0020-0255INFORM SCIENCES0109-5641DENT MATER0278-3649INT J ROBOT RES1364-8152ENVIRON MODELL SOFTW0163-6804IEEE COMMUN MAG1548-7660J STAT SOFTW1615-6846FUEL CELLS1388-0764J NANOPART RES1436-2228MAR BIOTECHNOL1751-6161J MECH BEHAV BIOMED0010-2180COMBUST FLAME0168-1605INT J FOOD MICROBIOL1178-2013INT J NANOMED1385-8947CHEM ENG J1431-9276MICROSC MICROANAL1468-6996SCI TECHNOL ADV MAT0268-005X FOOD HYDROCOLLOID0010-938X CORROS SCI1617-7959BIOMECH MODEL MECHAN0018-9200IEEE J SOLID-ST CIRC1570-1646CURR PROTEOMICS1387-2176BIOMED MICRODEVICES0175-7598APPL MICROBIOL BIOT127128 129130131 132133 134 135 136 137 138 139 140 141 142143 144145 146 147 148 149150 151 152 153 154155 156NanoIEEE ELEIEEEINFORMATION 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29.Emergency departments I the use of simulation and design of experiments for estimating maximum c

29.Emergency departments I  the use of simulation and design of experiments for estimating maximum c

Proceedings of the 2003 Winter Simulation ConferenceS. Chick, P. J. Sánchez, D. Ferrin, and D. J. Morrice, eds.THE USE OF SIMULATION AND DESIGN OF EXPERIMENTS FOR ESTIMATINGMAXIMUM CAPACITY IN AN EMERGENCY ROOMFelipe F. BaeslerHector E. Jahnsen Departamento de Ingeniería Industrial Universidad del Bío-BíoAv. Collao 1202, Casilla 5-CConcepción, CHILE MahalDaCostaFacultad de MedicinaUniversidad de ConcepciónAv. Roosevelt 1550Concepción, CHILEABSTRACTThis work presents the results obtained after using a simu-lation model for estimating the maximum possible demand increment in an emergency room of a private hospital in Chile. To achieve this objective the first step was to create a simulation model of the system under study. This model was used to create a curve for predicting the behavior of the variable patient’s time in system and estimate the maximum possible demand that the system can absorb. Fi-nally, a design of experiments was conducted in order to define the minimum number of physical and human re-sources required to serve this demand.1 INTRODUCTIONThe Hospital del Trabajador in Concepción city in Chile is an institution that offers a wide variety of healthcare ser-vices. The hospital is mainly oriented to serve patients that are workers in local companies who have had work acci-dents or diseases developed from their professional activi-ties. The companies have contracts with the hospital in or-der to get treatment for their workers. For this reason the most important part of the demand is controlled by the hospital based on the number of companies affiliated to them. In other words, if more companies were affiliated to the hospital it could be said that they were incrementing their demand. The hospital interest is to estimate the amount of extra demand that they are able to absorb con-sidering two main issues, maintain the patients’ waiting time standard and to consider some physical and human resources limitations.2 BACKGROUNDSimulation is an excellent and flexible tool to model dif-ferent types of environments. It is possible to find in the literature several simulation experiences in healthcare. For example, in the area of emergency rooms simulation it is possible to highlight Garcia et al. (1995). They present a simulation model focused on reduction of waiting time in the emergency room of Mercy Hospital in Miami. A simi-lar application is presented in Baesler et al. (1998) where important issues that have to be considered when interact-ing with healthcare practitioners during a simulation pro-ject are presented. Other cases not related to emergency rooms can be found in Pitt (1997). They present a simula-tion system to support strategic resource planning in healthcare. Lowery (1996) presents an introduction to simulation in healthcare showing very important considera-tions and barriers in a simulation project. Sepulveda et al. (1999) shows how simulation is used to understand and improve patient flow in an ambulatory cancer treatment center. This same study is complemented in Baesler & Se-pulveda (2001) where a multi-objective optimization analysis is performed.3 SYSTEM DESCRIPTIONThe emergency department of the hospital is open 24 hours a day and receives an average of 1560 patients a year. Be-sides their internal capacity the emergency department shares resources with other hospital services such as, X rays, Scanner, MRI, clinical laboratory, blood bank, phar-macy, and surgery. The human resources work in shifts, but at every moment three physicians are available, one nurse, and two or three paramedics depending on the time of the day. The patients get their examination and general treatment in five rooms, three of them for general use, the other two for specific cases. The general patient´s process is presented in Figure 1.When the patient arrives to the hospital, a receptionist collects their personal information. After this, the patient waits for availability of a treatment room and a paramedic. When this occurs the patient is walked to the room whereFigure 1: Patient Flowthe paramedic takes their vital signs. Then the physician is informed that a patient is waiting for treatment. If the phy-sician is available goes to see the patient and performs the examination. After the physician evaluates the patient he could conclude that additional exams are required. In this case the patient is transported to the corresponding test area, such as X rays, scanner, MRI, etc. Finally the patient returns to the exam room and the physician concludes the treatment and the patient is sent home.4 THE SIMULATION MODELThe simulation model was constructed using the simulation package Arena 4.0. The information required as input for the model was collected from the hospital databases, such as inter arrival rates, type of diagnosis, type and duration of treatments. A replication/deletion approach was used in or-der to run the model for a length of 1 month and a warm-up period of 4 days. A total of 57 replication were neces-sary in order to obtain the statistical precision required. The results obtained after running the as-is scenario were validated using hospital data.The objective of this project was to predict the maxi-mum demand that the emergency room is able to afford without increasing the waiting time over an acceptable level. The response variable “time in system”, that repre-sents the total time a patient spends inside the emergency room, was used as a service level parameter. Currently, pa-tients spend an average of approximately 70 minutes inside the system. The management is willing to increase the time up to 100 minutes in order to increment their demand.At the same time they are willing to expand their re-sources within a range that offers feasibility to this project, this means, add one receptionist, two physicians, two paramedics and build one extra room. The question that arises is “which is the maximum demand that the emer-gency room can afford without going over 100 minutes of patient average time in system with this new configuration of resources. In order to answer this question it was neces-sary to understand the behavior of the variable time in sys-tem versus changes in demand. This was done running the simulation model with the new configuration of resources in 5 different levels of demand. Table 1 shows the percent-age of demand increased and the number of patients asso-ciated to that level of demand.Table 1: Changes in Demand% DemandIncreasePatientsper dayPatientsper monthAs-Is 52 156021 63 189044 75 225070 88 2640100 106 3180150 131 3930 The results obtained after running these five scenarios as well as a polynomial curve that fits the behavior of the time in system is presented in Figure 2.Interpolating this curve it is possible to estimate that the level of demand that generates an average time in sys-tem of 100 minutes corresponds to a 130% increase of de-mand. The question now is to determine the minimum number of resources required to achieve this level of de-mand. The simulation scenarios were carried out using the maximum feasible hospital capacity, but it could be possi-ble that one or more resources of this configuration wereFigure 2 : Demand Curveunder utilized. In this case the same level of demand could be satisfied using less resources. To do this it is necessary to determine which resources could be decreased without altering the system’s performance. In order to answer this question it was decided to perform an experimental design analysis. 5DESIGN OF EXPERIMENTSIn order to determine the significance of the resources in the system’s behavior, a design of experiments was per-formed. The experiments considered a fixed level of de-mand (130%) and four factors, physicians, paramedics, exam rooms and receptionists. Table 2 shows the settings of this experiment.Table 2: Factor LevelsLevel Receptionist Physician Paramedic Room - 1 3 3 5 + 2 5 5 6A fractional factorial design with resolution IV was con-ducted. This requires a total of 24-1 = 8 simulation scenar-ios. With this resolution it is possible to determine the significance of the main effects, but the two-way interac-tions are confounded. The results obtained after perform-ing the experiments are presented in the pareto chart shown in Figure 3.This chart shows that the main effects receptionist and paramedics as well as the confounded interactions AC+BDare significant. Since it is not possible to determine which one of the interactions AC or BD is the significant one, it is necessary to conduct additional experiments that permit to understand the significance of the interaction AC (Physi-cians- Rooms). The design selected was a full factorialdesign considering two factors, Physicians and Rooms. Since the main factors receptionists and paramedics re-sulted to be significant from the previous experiment, it was decided to fix these factors in the high level, this means, two receptionists and five paramedics and a level of demand of 130% increase. The experiments were performed and it was concluded that the two factors were significant, so they have to be set in a high level. Figure 4 presents a response surface plot of the two factors.The response surface plot indicates that in order to de-crease the time in system it is necessary to set the two fac-tors in a high level, six rooms and five physicians. Even though it is clear that the two factors are significant, the plot shows that the maximum time in system allowed (100 minutes) is reached before the level of five physicians. A contour map can explained better this issue and it is pre-sented in Figure 5.Figure 5: Contour MapThe contour line highlighted with the two arrows represents the level of resources required to reach a time in system of 102 minutes, very close to 100 minutes. It can be concluded that fixing the factor physicians in a level of 4.5, it is possible to maintain the time in system in 100 minutes. This interesting result could be interpreted as a requirement of four fulltime physicians plus one halftime physician.6 CONCLUSIONSThis study showed how simulation could be used to esti-mate the maximum level of demand that an emergency room is able to absorb and which is the configuration of resources required to maintain a quality of service. The re-sults showed that the resources required to reach this level of demand are close to the feasible maximum level. For example, the hospital layout permits to build just one extra exam room. Probably the most important conclusion of this study is that 4.5 physician are required (four fulltime and one halftime). Of course, this means important saving to the hospital.REFERENCESBaesler, F., Sepúlveda, J.A., Thompson, W., Kotnour, T.(1998), Working with Healthcare Practitioners to Im-prove Hospital Operations with Simulation, in Pro-ceedings of Arena Sphere ’98, 122-130.Baesler, F., Sepúlveda, J., (2001) “Multi-Objective Simula-tion Optimization for a Cancer Treatment Center” in Proceedings of Winter Simulation Conference 2001, Virginia, USA. B. A. Peters, J. S. Smith, D. J.Medeiros, and M. W. Rohrer, (eds.) 1405-1411. Garcia, M.L., Centeno M.A., Rivera, C., DeCario N.(1995), Reducing Time in an Emergency Room Via a Fast-Track, in Proceedings of the 1995 Winter Simula-tion Conference, Alexopoulus, Kang, Lilegdon & Goldman (eds.), 1048-1053.Pitt, M. (1997), A Generalised Simulation System to Sup-port Strategic Resource Planning in Healthcare, Pro-ceedings of the 1997 Winter Simulation Conference, S.Andradóttir, K. J. Healy, D. H. Withers, and B. L.Nelson (eds), 1155-1162.Lowery, J. C. (1996), Introduction to Simulation in Health Care, in Proceedings of the 1996 Winter Simulation Conference, J. M. Charnes, D.J. Morrice, D. T. Brun-ner, and J. J. Swain (eds), 78-84.Sepúlveda, J.A.,., Thompson, W., Baesler, F., Alvarez, M.(1999), “The Use of Simulation for Process Improve-ment in a Cancer Treatment Center”, Proceedingsof the1999 Winter Simulation Conference, Phoenix, Ari-zona, USA, 1551-1548.BIOGRAPHIESFELIPE F. BAESLER is an Assistant Professor of Indus-trial Engineering at Universidad del Bio-Bio in Concep-ción Chile. He received his Ph.D. from University of Cen-tral Florida in 2000. His research interest are in Simulation Optimization and Artificial Intelligence. His email is <fbaesler@ubiobio.cl>.HECTOR E. JAHNSEN is a graduate student in the De-partment of Industrial Engineering at the University of Bio-Bio. He works as a research assistant in projects re-lated to industrial and healthcare simulation. His e-mail is <hjahnsen@alumnos.ubiobio.cl>.MAHAL DACOSTA is an assistant professor at the col-lege of medicine at the Universidad de Concepción in Chile. She has a Doctorate degree in Bio-ethics and a Mas-ter degree in public health. Her research interests are in the field of public health management. Her email is <gdacosta@udec.cl>.。

管外翅片强化传热途径与研究进展

管外翅片强化传热途径与研究进展

技术综述收稿日期:2004 04 16作者简介:徐百平(1969 ),男,吉林公主岭人,博士,从事高分子材料加工动力学模拟仿真、化工过程强化传热与节能以及传热过程的热力学效能评价方面的工作。

文章编号:1000 7466(2004)05 0041 04管外翅片强化传热途径与研究进展徐百平1,2,朱冬生2,黄晓峰1,顾雏军1(1 华南理工大学,广东广州 510640; 2.广东科龙电器股份有限公司博士后工作站,广东佛山 528303)摘要:介绍了管翅式换热器管外翅片强化传热的措施及其最新研究进展,总结了不同翅片形式强化传热的机理及翅片参数对传热与流阻的影响规律。

提出了翅片尺度的新概念,并指出了今后的研究方向。

关 键 词:换热器;翅片;强化传热中图分类号:TQ 051 501 文献标识码:AThe measurements and study advances for the heat transfer enhancement of outer fins of tubeXU Bai pi ng 1,2,ZHU Dong sheng 2,HUANG Xiao feng 1,GU Chu jun 1(1 College of Industrial Eq uipment and Control Eng ,SouthChina University of Technology,Guangzhou 510640,Chi na;2 Guangdong Kelon Electrical Holding Co Ltd ,Foshan 528303,China)Abstract :The measurements and up to datestudy advances for the heat transfer enhancement of ou ter fins in tube fin heatexchangers are reviewed,the mechanism of heat transfer enhancement and effectof fin parameters on heat transfer and flow resistance are sum marized Meanwhile,the novel concept of fin scale is proposed and further research direction is g i venKey words :heat ex changer;fin;heat transfer enhancement 管翅式换热器是空调中最常用的换热器结构形式,冷、热流体间壁错流换热,管内走冷媒,管外为空气。

基于延时滤波算法的阵列信号合成技术

基于延时滤波算法的阵列信号合成技术

第30卷 第6期2007年12月电子器件Ch inese Jou r nal Of Elect ro n DevicesVol.30 No.6D ec.2007A r r ay Signa l Synthesizer TechnologyB ased on Dela y Filter Ar ithmet icH U A N G Fei ,Q I AO Ch un 2j ie ,W A N G Yue 2ke ,W A N G Ga n g(S chool of Mechat roni cs En gineeri ng and Aut omat ion ,Nat ional Uni v.of Def ense Tech nol ogy ,Changsh a 410073,Chi na)Abstract :Accurat el y delayi ng t he reference signal wit h hi gh resol ut ion is t he key point i n array si gnal syn 2t hesizer.First ly ,t he ba si s p ri nciple and met hod of array signal synt hesizer are int roduced.According t o t he charact erist ic of array si gnal ,t he synt he sizi ng t echnology ba se d o n delay filt er arit hmet ic i s di scussed.The advantage of t he met hod i s t hat we ca n obt ai n t he acc urate dela y wi t h high resol ution even t ho ugh t he sample frequency of D/A is low.It shows by si mulation t hat t he met hod is simple ,low 2calculat ion ,hi gh 2accurate a nd very sui table to use i n array si gnal synt hesizer.K ey w or ds :Signal 2Synt he sizi ng;Beam 2Forming ;Delay Filt er ;Array Si gnal ;Direct Di gi tal Synt hesizer EEACC :7220基于延时滤波算法的阵列信号合成技术黄 飞,乔纯捷,王跃科,王 刚(国防科技大学机电工程与自动化学院仪器系,长沙410073)收稿日期226作者简介黄 飞(2),男,博士研究生,研究方向为主要从事数字化测试技术方面的研究,@63摘 要:阵列信号合成的关键是对一个参考信号如何实现高分辨率的延时.介绍了阵列信号合成的基本原理与方法,根据阵列信号的特性,深入研究了一种基于延时滤波算法的阵列信号合成技术.该方法的优点是在较低的D/A 采样频率的条件下,达到了高分辨率的延时,实现了阵列信号合成.仿真结果证明,该算法结构简单,计算量小,延时精度高,工程应用前景好.关键词:信号合成;波束成形;延时滤波;阵列信号;直接数字频率合成中图分类号:TP391 文献标识码:A 文章编号:100529490(2007)0622218204 阵列信号源是自导系统的重要组成部分,在自导系统工作过程中,阵列信号源按照自导要求产生信号,同时控制发射机工作,驱动声呐矩阵,发出“阵列波”搜索目标.阵列信号源是单亮点[1]的,它通过由多个阵元组成的声呐基阵输出一个单亮点的波束.各个阵元输出信号类型相同、幅值相同但相位各异的一列球面波,所有阵元输出波列叠加成为一个“平面波束”.在自导系统工作时,只要控制各个阵元之间的相位关系就可以改变发射平面波束的方向,从而实现“目标对准”.作为一种阵列信号源,要求各个通道之间有严格的相位关系,从而实现阵列波束向某一特定的方向发射,即“波束成形”.波束成形就是产生一个特定方向的阵列波束.根据各个阵元的空间位置和发射波束的方向角,将每个阵元发出的信号相对于参考点阵元作一定的延时,最终,声呐基阵的所有阵元发出的信号会在空间叠加,从而形成一个“阵列波束”.由此可见,增强自导系统探测目标、识别目标的能力,关键是提高阵列波束的“目标对准”精度,即要求阵列信号源各个阵元的波束相对参考点阵元的波束达到高分辨率的延时,这正是阵列波束成形的关键.本文根据阵列信号源的特性和要求,研究了一种基于延时滤波算法的阵列波束成形技术.在数字域采用了多采样率信号处理技术(插值、滤波和选抽),从而在较低的D/A 采样频率的条件下,达到了高分辨率的延时,实现了阵列信号合成.最后对这种算法进行了仿真,结果表明这种方法切实可行,满足自导系统对阵列信号源延时精度的要求.8:2007010:1979ice mar shal 1.co m.1 阵列信号的描述及实现方法1.1 阵列信号的描述阵列波(信号)是指发射声呐基阵发出或者接收声呐基阵接收到的波束.声呐基阵是由多个声呐传感器按照一定规律排列组成的阵列,每个声呐传感器称为声呐基阵的一个阵元.阵列波包括单亮点阵列波和多亮点阵列波.单亮点阵列波可以看作一个质点组成的目标发出的信号,而多亮点阵列波是多个质点组成的目标发出的信号.单亮点阵列波发出的是只有一个亮点的信号.对于发射声呐基阵,理想上认为每个阵元都发出一列球面波,这样声呐基阵的所有阵元发出的球面波会在空间叠加,就得到了一个平面波束.多亮点阵列波发出的是含有多亮点信息的信号.声呐基阵发送的是多个平面波束的叠加,每个阵元都发送多列波的叠加信号.多亮点阵列波也可以看作多个单亮点阵列波的叠加.接收阵列波是发射阵列波的逆过程,从基本原理上讲,合成这两种阵列波没有本质区别.这里从一维发射基阵发射的单亮点阵列波入手,分析阵列信号的描述方法.假定一维发射声呐基阵由N个阵元组成,则其发射的单亮点阵列波如图1所示.图1 单亮点阵列波示意图从图1可以看出,基阵发射的为一束平面波,其出射角为θ.以基阵中的一个阵元作为参考点,并设此参考点发射的信号为S0(t),则第i个阵元发射的单亮点信号S i(t),可由式(1)描述如下:S i(t)=S0(t-τi(θ))(1)其中,τi(θ)为出射角为θ时第i个阵元发射相对于参考点阵元的延时,显然有τ0(θ)=0.在实际应用中,声呐基阵是由N个阵元组成的二维基阵.所以第i个阵元发射的单亮点信号S i(t)应改用公式(2)描述:S i=S0(t-τi(θx,θy))(2)式(2)中,θx表示平面波对于声呐基阵平面x 轴的出射角,θy表示平面波相对于声呐基阵平面y 轴的出射角,τi(θx,θy)表示此时第个阵元发射相对于参考点阵元的延时,显然有τ0(θx,θy)=0.1.2 阵列信号的合成方法信号合成技术是以频率合成理论为依据的,频率合成理论约于世纪3年代形成,其发展经历了3代直接频率合成技术、锁相频率合成技术和直接数字频率合成(Di rect Digit al Synt hesizer,简称DDS)技术.DDS技术是一种全数字频率合成技术,它将先进的数字信号处理技术引入信号合成领域,实现了合成信号的频率转换速度与频率准确度之间的统一.采用该技术合成的信号具有相位变换连续、频率转换速度快、频率分辨率高、相位噪声低、集成度高和易于控制等多种优点.在阵列信号合成时,需要对各个通道输出信号的类型和相位进行灵活控制,因此应采用DDS技术.阵列信号源是基于DDS技术的,但与普通的DDS信号源不同的是,本信号源信号种类多、通道数多,并且各个通道之间要求严格同步.阵列信号源有多个通道,各个通道发出的信号类型相同且相位关系确定.为了达到这一要求,各个通道的输出信号应严格保持同步.从式(1)中可以看出,合成一个N通道的阵列信号,可以采用以下的方法:首先,根据输出信号类型,合成一个该类型的信号,并作为参考通道的信号S0(t).然后,根据各个通道相对参考通道延时τi的要求,对S0(t)进行大小的延时,从而获得各个通道的信号S i(t).阵列信号合成的关键是对一个信号如何实现高分辨率的延时,即在实际应用中,对延时时间τi有较高的分辨率要求.一般地,为实现较高的延时分辨率可以提高系统中D/A转换器的采样频率,但这样无疑会增加硬件的复杂度,同时也大大增加了系统的成本.本文根据阵列波束成形原理,提出了一种基于延时滤波算法的阵列波束成形方法.在保持原始采样率不变的情况下,采用多采样率信号处理技术,对采样数据进一步处理:插值、滤波和选抽,最终实现了一种新型的多通道、多信号类型且波束方向可调的阵列信号源.2 延时滤波算法2.1 延时滤波算法的基本原理多采样率信号处理技术一般指的是利用增采样、减采样、压缩器或扩展器等各种方式来提高信号处理系统效率的技术[2].多采样率处理不是通过改变系统的采样时钟来达到的,而是保持原始的采样率不变,再对采样数据进一步处理:抽选或内插,前者可降低系统的采样率,形成“降采样”;后者将提高系统的采样率,形成“升采样”[3].为了实现高分辨率的信号延时,本文采用延时滤波算法合成阵列信号延时滤波算法是多采样率信号处理技术的一种应用实例,它集合了数据内插、反镜像滤波、延时、抗混叠滤波和数据抽9122第6期黄 飞,乔纯捷等:基于延时滤波算法的阵列信号合成技术8200:.选等过程.延时滤波算法的基本原理可由图2表示如下:图2 延时滤波算法的原理结构首先,在信号的原始采样频率下,对输入数字信号s 0(n)进行M 倍插值,提高信号的采样频率,使其达到延时分辨率的要求.其次,将插值后的信号通过反镜像滤波器h 1(n),滤除信号的镜像边带.再次,根据插值后的采样频率,对信号进行τi 大小的延时.然后,将信号通过抗混叠滤波器h 2(n ),滤除带外部分.最后,对信号进行M 倍的抽选,获得恢复原始采样频率的输出信号s i (n ).s i (n )即为s 0(n )在较高分辨率下进行延时的结果.2.2 延时滤波算法的简化为了降低计算量,便于工程实现,根据延时滤波算法的基本原理,对延时滤波算法进行简化.从图2不难看出,为了使处理后的信号s i (n )恢复到原始采样频率,数据的抽选因子与插值因子保持一致,都是M .当数据抽选因子不大于插值因子时,通过反镜像滤波器h 1(n )和延时之后的信号,不会因后面的数据抽选而发生信号混叠.这样一来,就可将抗混叠滤波器h 2(n)省去.另外,可以将延时时间τi 在高采样频率(M 倍插值后,可满足延时分辨率的采样频率)下离散化,得到数字化的延时d i .然后将反镜像滤波器与延时合并,得到滤波器h (n -d i ),本文称之为延时滤波器.根据以上分析,得到了经简化的延时滤波算法的原理框图,如图3所示.图3 延时滤波算法的简化结构延时滤波算法经过简化,去掉了原理框图中的冗余部分,减少了计算量,仅剩下三个必要的环节:数据插值、延时滤波和数据抽选.这三个环节有机结合,缺一不可.其中滤波器h (n )是一个N 阶的FIR 滤波器.3 基于延时滤波的阵列信号合成算法根据图3所示的延时滤波算法的原理结构,可以得到()、()、()和()之间的关系式,如式(3)、()和(5)所示s 01(n )=s 0(n/M ),n =0,M ,2M …0,else(3)s 02(n)=∑N -1+d k ’=d i s01(n -k ’)h (k ’-d i )=∑N -1k=0s01(n -k -d i )h (k )(4)s i (n )=s 02(nM )(5)式(4)中,FIR 滤波器h (n )是N 阶的.利用式(4)和(5),可以得到式(6)s i (n )=∑N -1k =0s 01(nM -k -d i )h (k )(6)令m =[d i /M ],p =d i mol M ,显然有d i =mM +p ,且d i ,m,p 都为整数.当n <m 时,显然s i (n )=0;当n Εm 时,根据公式(3)和(6),通过数学推导,可以得到式(7)s i (n )=∑[N -1M]+1k=0s(n -k -m )h (kM -p )(7)通过以上分析,得到了延时滤波算法的数学表达式,如公式(8)所示:s i (n )=0,(n <m ) (8)∑[N -1M]+1k=0s(n -k -m )h (kM -p ),(n Εm )其中,m =[d i /M ],p =d i mol M ,有d i =mM +p ,且d i ,m ,p 都为整数.h(n)是N 阶的F IR 滤波器,对于n >N -1和n <0的情况,h (n )值都应取零.从式(8)可以看出,延时滤波算法结构简单,计算量小,适合工程实现阵列信号合成.4 仿真结果在研究了延时滤波算法的原理与方法之后,用Matlab 软件对基于此算法合成的阵列信号进行仿真.设阵列信号源有6个通道,D /A 转换器的采样速率为100kH z ,输出信号的通带范围为0~40k H z ,各通道的延时分辨率为0.5μs.为了满足这一要求,这里需要对原始数据进行20倍插值(M =20),将信号的采样频率提高到2MHz.因此,这里采用最优滤波器的设计方法,即Parks 2McClellan 算法设计得到在2MH z 采样频率下的,通带范围为0~40kHz 的低通滤波器h(n).在设计好FIR 滤波器之后,利用公式(8),仿真得到了500Hz 正弦波,在100kHz 的D /A 转换器采样频率下,各个通道之间相对延时100.5μs 的6通道阵列信号源的波形,如图4所示.从仿真结果可以看出,采用延时滤波算法,可以在较低的D 采样频率下,合成较高延时分辨率的阵列信号0222电 子 器 件第30卷8s 0n s 01n s 02n s i n 4./A .图4 6通道阵列信号源的仿真结果5 结论近年来,随着雷达、声自导和精确定位等技术的飞速发展,阵列信号源的作用变得越来越重要,目前已成为信号源技术领域的一个崭新的发展方向.本着简单、可靠、高效和低成本的目标,本文针对阵列信号源的设计提出了一种实用的合成技术———基于延时滤波算法的阵列信号合成技术.目前,该技术已成功应用于某自导系统的阵列信号源中,为我国自导技术的发展做出了贡献.本文的研究成果不仅能应用于自导系统,而且还可以在超声波定位与导航、预警设备、雷达等领域对其结论进行推广.参考文献:[1] 李启虎.声呐信号处理引论[M ],海洋出版社,1985.[2] 丁玉美,高西全.数字信号处理[M ],西安电子科技出版社,2001.[3] 倪养华,王重玮.数字信号处理———原理与实现[M ],西安交通大学出版社,1998.[4] 白居宪.低噪声频率合成[M ],西安交通大学出版社,1995.[5] 郑君里,应启珩,杨为理.信号与系统[M ],高等教育出版社,2001.[6] A.V.奥本海默,R.W.谢弗,J.R.巴克.Di screte 2Ti m e SignalProces s i ng (Second Edi tio n )[M ],西安交通大学出版社,2001.[7] 赵红怡,张常年.数字信号处理及其Matl ab 实现[M],化学工业出版社,2002.[8] 李志舜.现代鱼雷自导系统及其发展趋势[J ],鱼雷技术,1999,7(1):12215.[9] 朱埜.主动声呐检测信息原理[M ],海洋出版社,1990.(上接第2217页)同时,从图12中还可以看出,加速度滤波法控制效果振荡剧烈,不如状态判断归一法控制效果稳定.因此综合比较这三种策略,可看出状态判断归一法是最优的半主动控制策略.4 总结研究结果表明,直接将半主动控制策略耦合于H ∞控制器时,由于所限定耗能状态的不连续性,会严重影响H ∞控制器的减振效果,说明实现基于H ∞控制技术的较优半主动控制策略应是合理实现控制力与耗能状态信息的结合.基于H ∞控制器的加速度滤波和状态判断归一法,与现有的参考模型法相比,其减振效果十分明显,可以使加速度最大均方根值降低60%.在能耗方面,所提出的加速度滤波法和状态归一法可以在减振效果优于参考模型法的前提下,降低所需控制能量.参考文献:[1] Karnopp D C and Cros by M J.Vi bration C ont rol us i ng Semi 2Act ive F o rce Generato rs[J ].Jo urnal of Engi neeri ng for Indus 2t ry ,1974,96:6192626.[2] Hro vat D.Applicat ion s of Ot imal Cnt rol t o Avanced Ato m o 2S D []T f SM ,3,115:3282342.[3] F o da S G.Neuro 2Fzy Cnt rol of a Smi 2At i ve Car SuspensionSyst em[C]//Co mmunicatio ns ,Comp ut ers and signal Pro cess 2in g ,IEEE ,2001,2:6862689.[4] Seung G J ,K i m I S and Y oo n K S etc.Ro bust H ∞Cont roll erDes i gn for Perfor m ance Imp rovement of a S emi 2Act ive Suspen 2s io n Syst ems [C ]//Proceedi ngs of Indu st ri al El ect ro nics ,IEEE ,2000,2:7062709.[5] 威鲁麦特H P 著.车辆动力学模拟及其方法[M ].北京理工大学出版社,1998.[6] 杨礼康.基于磁流变技术的车辆半主动悬挂系统理论与试验研究[D ].浙江大学,2004.[7] Choi S B ,Choi Y T and Park D W.A Sli di ng Mode Cont rol ofa Ful l 2Car Elect rorheol ogi cal Suspensio n Sy stem vi a Hardware i n 2t he 2Loop Si mul at ion [J ].Transact ions of t he ASM E ,J our 2nal of Dynam i c sys tems ,Measurement ,and C ont rol ,2000,122(3):1142121.[8] GB/T 4970—1996.中华人民共和国国家标准,平顺性评价指标的意义与计算(附录A).[9] 周克敏[美],Doyle J C [美],G lo ver K[美]著.毛剑琴译.鲁棒与最优控制[M ].国防工业出版,2002.[10] Yue C ,But s uen T and Hedrick J K.Al ternati ve Cont rol Lawsfor Aut omot ive Acti ve Suspension s [J ].Transact ion s of t he ASM E J ournal of Dynamic syst ems ,Measurem ent s ,and C o nt rol ,1989,111(6):2862291.[11] 陈虹,赵桂军,孙鹏远,郭孔辉.H2和H ∞主动悬架统一理论框架与比较[J ].汽车工程,2003,25(1):126.1222第6期黄 飞,乔纯捷等:基于延时滤波算法的阵列信号合成技术8ti ve spe nsio n sign J .ra nsac tio ns o t he A E 199。

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Coupling an Advanced Land Surface-Hydrology Model with the Penn State-NCAR MM5 Modeling System1

VOLUME 129
MONTHLY WEATHER REVIEW
APRIL 2001
Coupling an Advanced Land Surface–Hydrology Model with the Penn State–NCAR MM5 Modeling System. Part I: Model Implementation and Sensitivity
1. Introduction For more than a decade, it has been widely accepted that land surface processes and their modeling play an important role, not only in large-scale atmospheric models including general circulation models (GCMs) (e.g., Mintz 1981; Rowntree 1983, etc.), but also in regional and mesoscale atmospheric models (Rowntree and Bolton 1983; Ookouchi et al. 1984; Mahfouf et al. 1987; Avissar and Pielke 1989; Chen and Avissar 1994a,b, etc.). Mesoscale models that resolve wavelengths from 1 to 100 km (i.e., from meso-␥ to meso-␤ scales) are often used for three applications: 1) regional climate simulations, 2) numerical weather prediction, and 3) air quality monitoring. Therefore, during the last few years, we have witnessed rapid progress in developing and testing land surface models in mesoscale atmospheric models (e.g., Bougeault et al. 1991; Giorgi et al. 1993; Bringfelt 1996; Smirnova et al. 1996; F. Chen et al. 1997; Pielke et al. 1997).

模型预测控制——现状与挑战

第39卷第3期自动化学报Vol.39,No.3 2013年3月ACTA AUTOMATICA SINICA March,2013模型预测控制—现状与挑战席裕庚1,2李德伟1,2林姝1,2摘要30多年来,模型预测控制(Model predictive control,MPC)的理论和技术得到了长足的发展,但面对经济社会迅速发展对约束优化控制提出的不断增长的要求,现有的模型预测控制理论和技术仍面临着巨大挑战.本文简要回顾了预测控制理论和工业应用的发展,分析了现有理论和技术所存在的局限性,提出需要加强预测控制的科学性、有效性、易用性和非线性研究.文中简要综述了近年来预测控制研究和应用领域发展的新动向,并指出了研究大系统、快速系统、低成本系统和非线性系统的预测控制对进一步发展预测控制理论和拓宽其应用范围的意义.关键词模型预测控制,约束控制,大系统,非线性系统引用格式席裕庚,李德伟,林姝.模型预测控制—现状与挑战.自动化学报,2013,39(3):222−236DOI10.3724/SP.J.1004.2013.00222Model Predictive Control—Status and ChallengesXI Yu-Geng1,2LI De-Wei1,2LIN Shu1,2Abstract Since last30years the theory and technology of model predictive control(MPC)have been developed rapidly. However,facing to the increasing requirements on the constrained optimization control arising from the rapid development of economy and society,the current MPC theory and technology are still faced with great challenges.In this paper,the development of MPC theory and industrial applications is briefly reviewed and the limitations of current MPC theory and technology are analyzed.The necessity to strengthen the MPC research around scientificity,effectiveness,applicability and nonlinearity is pointed out.We briefly summarize recent developments and new trends in the area of MPC theoretical study and applications,and point out that to study the MPC for large scale systems,fast systems,low cost systems and nonlinear systems,will be significant for further development of MPC theory and broadening MPC applicationfields. Key words Model predictive control(MPC),constrained control,large scale system,nonlinear systemsCitation Yu-Geng Xi,De-Wei Li,Shu Lin.Model predictive control—status and challenges.Acta Automatica Sinica, 2013,39(3):222−236模型预测控制(Model predictive control, MPC)从上世纪70年代问世以来,已经从最初在工业过程中应用的启发式控制算法发展成为一个具有丰富理论和实践内容的新的学科分支[1−3].预测控制针对的是有优化需求的控制问题,30多年来预测控制在复杂工业过程中所取得的成功,已充分显现出其处理复杂约束优化控制问题的巨大潜力.进入本世纪以来,随着科学技术的进步和人类社会的发展,人们对控制提出了越来越高的要求,不收稿日期2012-06-25录用日期2012-09-29Manuscript received June25,2012;accepted September29, 2012本文为黄琳院士约稿Recommended by Academician HUANG Lin国家自然科学基金(60934007,61074060,61104078)资助Supported by National Natural Science Foundation of China (60934007,61074060,61104078)1.上海交通大学自动化系上海2002402.系统控制与信息处理教育部重点实验室(上海交通大学)上海2002401.Department of Automation,Shanghai Jiao Tong University, Shanghai2002402.Key Laboratory of System Control and Information Processing of Ministry of Education(Shanghai Jiao Tong University),Shanghai200240该文的英文版同时发表在Acta Automatica Sinica,vol.39,no.3, pp.222−236,2013.再满足于传统的镇定设计,而希望控制系统能通过优化获得更好的性能.但在同时,优化受到了更多因素的制约,除了传统执行机构等物理条件的约束外,还要考虑各种工艺性、安全性、经济性(质量、能耗等)和社会性(环保、城市治理等)指标的约束,这两方面的因素对复杂系统的约束优化控制提出了新的挑战.近年来,在先进制造、能源、环境、航天航空、医疗等许多领域中,都出现了不少用预测控制解决约束优化控制问题的报道,如半导体生产的供应链管理[4]、材料制造中的高压复合加工[5]、建筑物节能控制[6]、城市污水处理[7]、飞行控制[8]、卫星姿态控制[9]、糖尿病人血糖控制[10]等,这与上世纪预测控制主要应用于工业过程领域形成了鲜明对照,反映了人们对预测控制这种先进控制技术的期望.本文将在分析现有成熟的模型预测控制理论和工业预测控制技术的基础上,指出存在的问题,综述当前针对这些问题的研究动向,并对模型预测控制未来可能的研究提出若干看法.3期席裕庚等:模型预测控制—现状与挑战2231现有预测控制理论和应用技术存在的问题上世纪70年代从工业过程领域发展起来的预测控制,是在优化控制框架下处理约束系统控制问题的,反映了约束控制的研究从反馈镇定向系统优化的发展.大量的预测控制权威性文献都无一例外地指出,预测控制最大的吸引力在于它具有显式处理约束的能力[1−3,11−12],这种能力来自其基于模型对系统未来动态行为的预测,通过把约束加到未来的输入、输出或状态变量上,可以把约束显式表示在一个在线求解的二次规划或非线性规划问题中.随着预测控制工业应用的普及和软件产品的成熟,标准二次规划算法和序贯二次规划算法被引入预测控制的优化求解.在全球数千个大型工业设施上的成功应用,表明预测控制作为一种实际可用的约束控制算法,已受到了工业过程控制领域的广泛认同[1].Qin等在2003年发表的著名论文[1]中对工业预测控制的发展历程和应用现状做了完整的综述,根据到1999年对于国际上5家主要预测控制软件厂商产品应用的不完全统计,预测控制技术已在全球4600多个装置和过程中得到应用,涉及炼油、石化、化工、聚合、制汽、制浆与造纸等工业领域,预测控制软件产品也已经历了四个发展升级阶段.在我国,预测控制软件开发及典型工程应用被纳入国家“九五”科技攻关,浙江大学、清华大学、上海交通大学等单位都开发了具有自主知识产权的多变量预测控制软件并在一些工业过程中得到成功应用.浙大中控技术有限公司等还实现了预测控制软件的商品化并在国内推广,有力地推动了预测控制在我国的工业应用.尽管预测控制在国内外工业过程中都得到了成功应用,但作为要解决当前经济社会面临的约束优化控制问题的有效技术,仍有以下局限性:1)从现有算法来看,主要还只适用于慢动态过程和具有高性能计算机的环境,从而大大限制了其在更广阔应用领域和应用场合的推广现有的工业预测控制算法需要在线求解把模型和约束嵌入在内的优化问题,每一步都需采用标准规划算法进行迭代,涉及很大的计算量和计算时间,使其只能用于可取较大采样周期的动态变化慢的过程,并且不能应用在计算设备配置较低的应用场合(如DCS的底层控制).Qin在文献[1]中对已投运的线性预测控制产品的应用领域进行了分类,在所统计的2942个案例中,炼油、石化、化工领域占了绝大部分,分别为1985、550、144例.虽然这只是到1999年为止的数据,而且统计的只是国际上主要预测控制商用软件产品的应用状况,但还是趋势性地反映出预测控制的规模应用主要局限在过程工业领域,特别是炼油、石化工业.对于制造、机电、航空等领域内的大量快速动态系统,如果不采用性能较高的计算设备,这类标准优化算法就很难满足小采样周期下的实时计算要求,所以至今未能在这些领域内形成规模应用.2)从应用对象来看,主要还限于线性或准线性过程现有工业预测控制技术的主流是针对线性系统的,成熟的商用软件及成功案例的报道以线性系统为多,虽然软件厂商也推出了一些非线性预测控制产品,但据文献[1]统计,其投运案例数大致只及线性预测控制产品的2%,远未形成规模.即使在过程工业中,预测控制技术的应用也只局限在某些过程非线性不严重的行业,如精炼、石化等,而在非线性较强的聚合、制气、制浆与造纸等领域应用不多.造成这一现象主要是由于在工业过程中非线性机理建模要耗费很大代价,而且很难得到准确的模型,此外非线性约束优化问题的在线求解尚缺乏实时性高的有效数值算法.面对着经济社会发展各行各业对预测控制技术的需求,对象或问题的非线性将更为突出.控制界和工业界都认识到发展非线性预测控制的重要性,例如以非线性模型预测控制为主题的两次国际研讨会NMPC05、NMPC08,就汇聚了国际知名学者和工业界专家认真评价和讨论非线性模型预测控制的现状、未来方向和未解决的问题[13−14].但到目前为止,虽然非线性模型预测控制已成为学术界研究的热点,但在工业实践中仍然处于刚起步的状态[15].3)从应用技巧来看,主要还需依靠经验和基于专用技巧(Ad-hoc)的设计现有的预测控制算法多数采用工业界易于获得的阶跃响应或脉冲响应这类非参数模型,并通过在线求解约束优化问题实现优化控制,对于约束系统无法得到解的解析表达式,这给用传统定量分析方法探求设计参数与系统性能的关系带来了本质的困难,使得这些算法中的大量设计参数仍需人为设定并通过大量仿真进行后验,因此除了需要花费较大的前期成本外,现场技术人员的经验对应用的成败也起着关键的作用,实施和维护预测控制技术所需要的高水平专门知识成为进一步应用预测控制的障碍.30多年来,工业预测控制的技术和产品仍保持着其原有的模式,并没有从预测控制丰富的理论成果中获取有效的支持.最近,应用界已认识到长期以来在过程工业中成功应用但其基本模式保持不变的工业预测控制算法的局限性,研发预测控制技术的著名软件公司Aspen Technology正在考虑摆脱传224自动化学报39卷统的模式,通过吸取理论研究的成果研发预测控制的新产品[16].综上所述,预测控制技术的应用虽然取得了很大的成功,特别在过程控制界已被认为是唯一能以系统和直观的方式处理多变量约束系统在线优化控制的先进技术,但它的应用领域和对象仍因现有算法存在的瓶颈而受到局限,对于更广泛的应用领域和更复杂的应用对象,只能从原理推广的意义上去研究开发相应的预测控制技术,远没有形成系统的方法和技术.此外,现有的工业预测控制算法与近年来迅速发展的预测控制理论几乎没有联系,也没有从中汲取相关的成果来指导算法的改进.因此在解决由于科学技术和经济社会发展所带来的各类新问题时,还面临着一系列新的挑战.与预测控制的实际应用相比较,预测控制的理论研究从一开始就落后于其实践.纵观预测控制理论研究的进程,不难发现它经历了两个阶段[17]:上世纪80年代到90年代以分析工业预测控制算法性能为特征的预测控制定量分析理论,以及上世纪90年代以来从保证系统性能出发设计预测控制器的预测控制定性综合理论.由于后者能够处理包括线性或非线性的对象,包括输入、输出和状态约束在内的相当一般的约束,包括稳定性、优化性能和鲁棒稳定等不同要求的问题,因此引起了学术界极大的兴趣.十多年来,在国际主流学术刊物上已涌现了大量相关论文,呈现出学术的深刻性和方法的创新性,也为约束系统优化控制的研究带来了新的亮点.经过十多年的发展后,预测控制的定性综合理论虽然已取得了丰硕的成果,发表了数以百计的具有很高理论价值的论文,但就目前的研究成果来看,还未能被应用领域所接受.除了这些理论所综合出的算法具有工业界不常采用的模型外,其从综合出发的研究思路也存在着本质的不足.1)物理意义不明确,难以与应用实践相联系预测控制的定性综合理论与定量分析理论不同,在每一时刻的滚动优化中,不是面对一个已有的、根据实际优化要求和约束条件确定的在线优化问题,而需要把在线优化的内容结合控制律一并综合设计.为了得到系统性能的理论保证,往往需要在具有物理意义的原始优化问题中修改性能指标(加入终端惩罚项),加入诸如终端状态约束、终端集约束等人为约束[18],这不但增加了设计的保守性,而且因为这些人为约束与系统受到的实际物理约束一并表达为同一优化问题中的约束条件,使得优化问题中具有物理意义的原始约束湮没在一系列复杂数学公式所表达的整体条件中,这些条件需要通过计算后验,缺乏对实际应用中关注的带有物理意义的分析结论.最典型的如在实际应用中的可行解指的是系统满足所有硬约束的解,而在预测控制定性综合理论中,可行性是指除了满足对系统状态和输入的硬约束外,还要满足包括不变集、Lyapunov函数递减、性能指标上限等在内的由系统设计所引起的一系列附加约束,甚至后者还成为约束的主体,因此很难与应用实践紧密联系.此外,约束下系统状态的可行域有多大,线性矩阵不等式是否有解,如果无解,约束放松到何种程度可以求解等,都无法从现有的研究结果中得到.2)在线计算量大,无法为应用领域所接受预测控制定性综合理论研究的出发点是如何在理论上保证闭环系统在算法滚动实施时的稳定性、最优性和鲁棒性,通常要把原优化问题转化为由新的性能指标和一系列线性矩阵不等式(Linear matrix inequality,LMI)约束描述的优化算法,所以几乎每一篇论文都会根据所研究的问题提出一个甚至多个预测控制综合算法.但是这些研究的重点几乎都放在算法条件如何保证性能的理论证明上,至于算法的具体实施,则认为已有相应的求解软件包即可,并不关注其在线实现的代价.大量人为约束的加入,虽然对系统性能保证是必要的,但同时也极大地增加了优化求解的计算量.特别对鲁棒预测控制问题,由于所附加的LMI条件不但与优化时域相关,而且与系统不确定性随时域延伸的各种可能性有关,LMI的数目将会急剧增长,对在线计算量的影响更为突出.虽然近年来这一问题已开始得到重视,但总体上因其在线计算量大的不足,很难受到应用领域的关注,也很少有在实际中成功应用的案例报道.在预测控制形成的初期,人们曾多次指出其理论研究落后于实际应用,两者之间存在着较大的差距.经过十多年来学术界的努力,虽然形成了成果丰富的预测控制定性综合理论,但由于两者的出发点不同,其理论意义明显高于实用价值,实际上并没有缩小预测控制理论和应用间的差距,远未成为可支持实际应用的约束优化控制的系统理论.综合以上对预测控制应用状况和理论发展的分析可以看出,虽然预测控制的工业应用十分成功,预测控制的理论研究体系也相当完善,但现有的预测控制理论和应用之间存在着严重的脱节,不能满足当前经济社会发展对约束优化控制的要求.我们可以把现有预测控制理论和应用技术存在的问题主要归结为:1)有效性问题.无论是工业预测控制算法还是由预测控制定性综合理论所设计的控制算法,均面临着在线求解约束优化问题计算量大这一瓶颈,极3期席裕庚等:模型预测控制—现状与挑战225大地限制了其应用范围和应用场合.2)科学性问题.预测控制理论研究和实际应用仍有较大距离,商品化应用软件很少吸收理论研究的新成果,理论研究的进展也不注意为实际应用提供指导,缺少既有性能保证又兼顾计算量和物理直观性的综合设计理论和算法.3)易用性问题.目前的预测控制算法都建立在约束优化控制问题一般描述和求解的基础上,对计算环境的要求和培训维护成本都比较高,缺少像PID控制器那样形式简洁、可应用于低配置计算环境、易于理解和掌握的“低成本”约束预测控制器.4)非线性问题.目前预测控制理论和算法的主要成果是针对线性系统的,由于实际应用领域中存在大量非线性控制问题,这方面的研究特别是应用还很不成熟.2当前研究动态随着本世纪科技、经济和社会的发展,各应用领域对约束优化控制的需求日益增长,人们对上面提到的工业预测控制算法和现有预测控制理论的不足有了越来越清晰的认识,促使预测控制理论和应用的研究向着更深的层次发展.当前,模型预测控制已成为控制界高度关注的热点,在各类学术刊物和会议上发表的与预测控制相关的论文居高不下.仅在2007年∼2011年的五年中,通过对Elsevier出版物及IEEE数据库的不完全检索,已查到预测控制相关论文1319篇,其中在Au-tomatica、Control Engineering Practice、Journal of Process Control、IEEE Transactions on Auto-matic Control等刊物上发表的相关论文数分别为74篇、75篇、164篇、35篇.2008年和2011年两次IFAC世界大会上,与预测控制有关的论文分别为131篇和138篇.对预测控制工业应用技术做出全面综述的论文“A survey of industrial model predictive control technology”[1]在2008年IFAC 世界大会上获得CEP最佳论文奖,全面综述预测控制稳定性理论的论文“Constrained model predic-tive control:stability and optimality”[2]在2011年IFAC世界大会上获得了最有影响力奖(High Impact Award).在国内,除了与国际同步开展的对预测控制理论的研究外[19−26],预测控制的应用已从传统的炼油、石化、化工行业延伸到电力[27]、钢铁[28]、船舶[29]、空天[30]、机电[31]、城市交通[32]、渠道[33]、农业温室[34]等领域,各种新的改进算法和策略也屡见报道.通过对近年来国内外预测控制研究工作的分析,可以清楚地看到,一方面,人们对预测控制解决在线约束优化控制寄予很高的期望,试图利用它解决各自领域中更多更复杂的问题;另一方面,工业预测控制算法的不足和现有预测控制理论的局限,又使人们在解决这些问题时不能简单地应用已有的理论或算法,必须研究克服其不足的新思路和新方法.这种需求和现状的矛盾,构成了近年来预测控制理论和算法发展的强大动力,同时也是预测控制理论和算法尽管似乎已很成熟,但人们仍然还在不断研究的主要原因.针对上述预测控制理论和算法的不足,近阶段国内外开展的研究可大致归结为以下几个方面:1)研究降低预测控制在线优化计算量的结构、策略和算法预测控制在线求解约束优化问题计算量大这一瓶颈,极大地限制了其应用范围和应用场合.针对这一问题,人们从结构、策略、算法层面开展了广泛的研究.a)结构层面:递阶和分布式控制结构随着制造、能源、环境、交通、城市建设等领域的迅猛发展,企业集成优化系统、交通控制系统、排水系统、污水处理系统、灌溉系统等大规模系统的预测控制受到了格外的关注[7,35−38],这类大系统的特点是组成单元多、模型复杂、变量数目巨大,整体求解其大规模约束优化问题在实际中几乎不可行.因此,针对实际系统的应用需求,人们普遍借鉴传统大系统理论提供的递阶控制结构把整体优化求解的复杂性进行分解.虽然基于同一模型分解协调的多级递阶控制方法在理论上已发展得较为成熟,但考虑到模型和实际环境的复杂性,在研究中通常更倾向于应用在不同层次采用不同模型的多层递阶结构[39],其研究的重点在于确定各层次的模型和优化控制目标以及协调各层次之间的关系,由此发展有效可行的控制框架和算法,所提出的控制方案和算法常通过仿真或实际运行数据加以验证.在大规模系统预测控制的研究中,近年来更受到重视的是采用分布式结构降低计算复杂性[40−41],分布式预测控制基于用局部信息进行局部控制的思想把大规模约束优化控制问题分解为多个小规模问题,不仅可以大大降低计算负担,而且提高了整体系统的鲁棒性.分布式预测控制的研究重点包括各子系统之间耦合关联的处理、子系统的优化决策及相互间的信息交换机制、全局稳定性的保证及最优性的评估等[42].近年来通信技术的发展和分布式控制软硬件的完善,使分布式预测控制从理论走向实践,应用已遍及到多个领域,包括过程控制[43]、电力系统[44]、交通系统[45]及近年来十分活跃的多智能体协作控制等[46].b)策略层面:离线设计/在线综合与输入参数226自动化学报39卷化策略在预测控制定性综合理论研究中,虽然系统性能可得到严格的理论保证,但设计所带来的额外计算负担十分庞大,导致本来已成为应用瓶颈的在线计算复杂性更为突出,这也是应用界对这些理论研究成果可用性的主要质疑.针对这一问题,在预测控制的定性综合中提出了“离线设计、在线综合”的策略,通过把所综合控制律的部分在线计算转换为离线计算,达到降低在线计算量的目的.文献[47]应用该思路给出了文献[18]提出的约束鲁棒预测控制器的简化设计方法;文献[48]利用名义系统指标离线设计不变集序列,在线时通过核算当前状态所在的最优不变集来确定控制律;类似的设计还包括文献[49];文献[50]通过离线求解有限时域优化控制序列,并采用Set membership来得到近似最优解,以提高求解效率.在这里特别要提到的是由Bemporad等提出的显式(Explicit)模型预测控制器[51−52],它通过对预测控制在线约束优化问题的分析,离线求解多参数规划问题,对约束状态空间分区并设计各区间的显式反馈控制律;在线控制时,只需依据系统的当前状态,选择实施相应的状态反馈控制律.这种方法把大量计算转移到离线进行,在线控制律的计算十分简易,而且有坚实的理论基础,因此受到了广泛的关注,进一步研究算法简化和对非线性系统的推广、以及算法在微处理器中的应用等也已见报道,如文献[53−54].但该方法离线需求解一个NP-hard的多参数规划问题,离线计算量随着问题规模增大而急剧增加,同时由于分区数的指数增长而导致巨大的内存需求,只能应用于小规模的问题[55].为此,近年来国内外学者进行了进一步的探索.文献[56]采用分段连续网格函数(Lattice PWA function)表示显式预测控制的解,以降低其对于存储空间和在线计算的要求;文献[57]通过分析二次规划问题求解方法在存储和计算方面的复杂度,提出一种半在线的显式预测控制算法,在存储量和在线计算时间之间进行平衡;文献[58]将动态规划和显式预测控制方法相结合,把预测控制的优化问题分解为小规模问题;而文献[59]针对非线性系统预测控制问题提出了近似的显式预测控制方法.离线设计、在线综合的方法能有效地解决预测控制在线优化计算量大的瓶颈问题,但要求原有的预测控制器设计方法可以进行分解,并且需要为在线综合保留一定的自由度,因此不能适用于所有的预测控制定性综合算法.在工业预测控制算法中,为了降低在线优化的计算量,很早就采用了启发式的“输入参数化”策略[1],包括输入“分块化(Blocking)”技术[60]和预测函数控制算法[61],前者把一定时间段内的控制量设置为不变,以减少控制自由度的代价来降低在线优化问题的规模,后者则把控制量表达为一组基函数的组合,使在线优化变量转化为数目较少的基函数的系数.这些策略虽然有很强的实用性并已大量应用于实际过程,但缺乏对系统稳定性等的理论保证.现有的预测控制稳定性综合方法在用于这类系统时,又因输入参数化造成递归可行性难以保证而不能奏效.近年来国内外学者对此进行了进一步的研究.对于Blocking技术,文献[62]采用时变的集结矩阵保证集结预测控制器的闭环稳定性,文献[63−64]就集结预测控制器的可行性问题进行了研究,并提出改善其可行性的方法.文献[65]提出了预测控制优化变量的广义集结策略,这一框架不但可以涵盖以上两种方法,而且由于把原有输入参数化的物理映射扩展为集结变换的数学映射,提供了更大的设计自由度,也为系统分析建立了必要的基础.在此基础上,文献[66]进一步设计了等效/拟等效集结策略以改善集结预测控制的控制性能.c)算法层面:各种改进或近似优化算法针对约束预测控制在线优化的问题形式,对标准优化算法进行改进或做适当近似,也是近期来降低预测控制在线计算量的一类尝试.文献[3]列举了在线求解大型二次规划(Quadratic programming, QP)和非线性规划问题时对算法的若干改进工作.文献[55]提出用扩展的Newton-Raphson算法取代现有算法中常用的二次规划和半定规划(Semi-definite programming,SDP)算法,可使计算量降低10倍以上.文献[67]提出了三种针对预测控制在线求解QP问题的快速算法.文献[68]打破了求解优化问题中“优化直至收敛”的概念,提出了实时迭代的概念,它在每一采样时刻只需计算一次迭代,其结果通过特定的移位与下一时刻的优化问题联系起来,在此基础上,文献[69]又提出了基于伴随导数和非精确雅可比阵的优化算法.文献[70]提出了采用部分列表的快速、大规模模型预测控制方法.此外,采用神经网络求解二次规划等问题又有了新的发展,与以往的工作相比,新的神经网络方法在保证收敛到全局最优解及降低神经网络结构复杂度方面都取得了较好的结果[71].2)鲁棒预测控制理论的研究更加注重实际可用性鲁棒预测控制理论在上世纪90年代中期已初步形成,从本世纪以来更成为预测控制理论研究的重点,在已有大量成果基础上,近年的研究更注意向实际靠拢和解决相关的难点问题.。

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Proceedings of the 2001 Winter Simulation Conference B. A. Peters, J. S. Smith, D. J. Medeiros, and M. W. Rohrer, eds.WHY WE NEED TO OFFER A MODELING AND SIMULATION ENGINEERING CURRICULUM Leo J. De Vin Mats Jägstam Centre for Intelligent Automation Department of Engineering Science University of Skövde Box 408 SE 541 28 SKÖVDE, SWEDENABSTRACT This paper describes some identifiable trends in the manufacturing industry regarding the increased use of simulation tools, especially by small- to medium-sized companies. These trends have resulted in the need for a new type of engineer, namely simulation engineer. This need prompted the University of Skövde to develop a B.Sc. simulation engineering study program. The contents and layout of the program, which started in Autumn 2000, are described. After receiving a firm foundation in manufacturing, logistics and mathematics in the first year, the main focus of the second year is on simulation. In the third year, which includes a substantial examination project, a specialization in manufacturing or in logistics is possible. Although simulation-related examination projects are already now carried out in other study programs, the simulation engineer will be able to cover a larger part of simulation projects and will have a broader overview of available simulation tools. 1 INTRODUCTIONsimulation tools is fairly common in research, their use in undergraduate programs is usually restricted to a limited number of exercises in one or two course modules. As a result, graduates are relatively unfamiliar with the use of simulation tools. This can result in using these tools incorrectly or in not utilizing them to the full potential. Recognizing this situation, the University of Skövde was the first Swedish university to develop an undergraduate study program that focuses on simulation engineering. This decision was based on identified industrial need for a new type of engineer, and on the results of a survey among school pupils that showed good potential for enrolment of students. It is envisaged that within about three years from now, the industrial demand for freshly graduated engineers with this background will have increased sharply, in particular the demand from small- to medium sized companies (SMEs). This expectation is based on the situation in Europe, in particular in Sweden, but a similar trend has been identified in the USA by Crosbie (2000). 2 TECHNOLOGY TRENDS AND POTENTIAL BENEFITS OF SIMULATIONThe use of computer based simulation tools in product and production development has sharply increased in the past years. These tools, such as discrete event simulation and geometry simulation, can be used to analyze products and production systems, for instance to test and evaluate different concepts during the design stage. They can also be used for operator training and off-line programming. Companies have also started to use simulations as a tool for communication between different actors in supply chains, initially internally but increasingly between companies. This holds especially for discrete event simulation (e.g. production flow simulation) and geometry simulation (e.g. computer aided robotics), which are the main area of attention in this paper. Given the importance that simulation tools have gained in industry, it is remarkable how little attention is being paid to the use of these tools in education. Whereas the use ofSimulation of production systems is nowadays widely used. In the case of new production systems or production system modifications, often a thorough analysis with the use of simulation tools is carried out either by the user or by the supplier of the production system. Although a simulation or the availability of data (e.g., models and performance data of equipment) for simulations is currently still a selling argument for production system suppliers, this is rapidly becoming a prerequisite for selling production systems or production system components. Similarly, suppliers of products or components are increasingly required to demonstrate the technical capabilities and output performance of their production facilities before they are granted an order. Even if this is not the case, many candidate suppliers carry out simulations in or-1599De Vin and Jägstam der to make sure that they do not accept orders that they will not be able to manage. Shorter product life cycles mean that problems in production nowadays have more serious consequences than in the past. As an example, Owen and Steeple (1998) found in a survey that it was typical for SME automotive suppliers to struggle to meet customer expectations after the introduction of new products. Typically, what would happen is that after securing an order, there would be problems in the beginning of production. This is indicated in Figure 1 where the curve “P” indicates the customer’s perception of quality and the line “E” indicates their expectation or demand. For the SME supplier, high costs are associated with the failure to meet product quality and delivery due dates. Contingency actions may include repair, tool modifications, premium time working, and increased logistic costs for expediting products. For the Original Equipment Manufacturer (OEM) client, costs can be a multitude, for instance missing a pre-arranged vehicle homologation test can seriously delay the launch date of new models With shorter product life cycles, the costs for these contingency actions form a much higher percentage of the overall order costs. Furthermore, there is less time available to restore customer confidence in the supplier’s ability. This makes the need to avoid potential quality defects and delivery problems paramount. 3 THE NEED FOR SIMULATION ENGINEERSFigure 1: Potential Start-Up Problem in New Product Introduction, after Owen and Steeple (1998) Through simulations of the production system, early detection of potential problems becomes much easier than by “gut feeling”. Furthermore, alternative solutions for actual production can be explored against relatively modest costs. In addition to this, the simulations can be used to build customer confidence (by inviting the customer to a demonstration), or can be an aid to discuss proposed product modifications with the customer. These are some of the reasons for the increasing use of production simulation in industry, including SMEs.Simulation is a powerful engineering tool, but only when it is used in a correct and systematic way. In the abovementioned example of the assembly cell, a nominal simulation would not have revealed the problems associated with geometrical variations. Not recognizing the limitations of a nominal simulation could have resulted in taking an erroneous decision. In many cases, people have a tendency to ignore the limitations of a particular simulation run and jump to conclusions just because “they’ve seen it on the screen”. Another problem in many simulation projects is, that the goal and purpose of a simulation project is not always clear at the beginning. During many simulation projects, there tends to be a pressure to study more and more aspects of a production system, or to address ‘what if’ questions that the project was not supposed to give an answer to in the beginning. Not only can this result in the original goal not being achieved, it can for instance also result in detailed decisions being based on simulations carried out with the use of crude models or provisional input data which were initially intended for a concept study of a production system only. One of the major problems, especially in discrete event simulation, is caused by the input data. Often, input data are incomplete and even if they are available, they may not always be correct. One example was encountered in a project in which data gathered over a number of years was used. The simulations did not give an accurate account of the actual situation because some stations in the production line were at the end of their technical life. In another project, storage/retrieval times in a warehouse had been measured during a period when production was low and the warehouse fairly empty. These were not representative at all for a full-production situation Apart from these problems, an important issue is that many problems can be solved, or at least studied thoroughly, without simulation, as mentioned for instance by Karlsson and Samuelsson (2000) and by Sadowski and Grabau (2000). The mere availability of simulation tools is no excuse to use them in situations where other methods may be more appropriate. It is the simulation engineer’s responsibility to use simulation tools in an appropriate way. Especially in SMEs, the simulation engineer may be the only person in a company who has the background and knowledge to judge the merits of particular simulation tools or to understand the validity of certain results. 4 EXAMPLES OF RECENT SIMULATION PROJECTSIn this section, some examples of examination projects in the more traditional study programs offered by the depart-1600De Vin and Jägstam ments of Engineering Science and Industrial Management are given. Recent examples include: [1] One examination project focused on developing a methodology for simulating production lines. A large customer for production systems wanted a structured work procedure where the machine and component suppliers for a line would supply models of their equipment at an early stage in production system development projects. The customer could then evaluate performance, layout, work schedules and so on, thus reducing the number of errors built into the line. [2] Another type of examination projects focuses on the study of bottlenecks in existing production systems. One project concerned a problem of a freezer/refrigerator production system that consisted of one main line with several feeding lines. The problem was that congestion tended to occur after some hours of operation. The result of the simulation study was that implementing some simple queuing rules resulted in a considerably higher capacity. [3] A variety of other projects study alternative layouts of production lines. Sometimes these lines can be fairly complex. A simple example regarded a line for automatic assembly of hydraulic valves. The study revealed that the production capacity of the initial layout was lower than required. It was suggested to double one assembly station. It was possibly to increase the production capacity even further but this was not justifiable from an economic point of view. [4] Examination projects related to geometry simulation are still in the minority. One of these projects was the development of a software application for simulation and off-line programming of a robotassisted pressbrake. The main problem in this project was to simulate the robot gripper position and orientation during sheet bending. A difference between these examination projects and work that can be carried out by students and graduates from a simulation engineering programme is that the latter should have more experience with a variety of simulation tools and a broader understanding of simulation in general. Graduates from a simulation engineering programme should also have experience with discrete simulation as well as with continuous simulation, something that is considered to be rare, according to Crosbie (2000). A typical simulation project could incorporate a number of phases as indicated in Figure 2. Naturally, these phases must not be treated as rigid sequential steps but must be seen as activities carried out in a concurrent way as pointed out by for instance Sadowski and Grabau (2000). The availability of data for instance influences the selection of methods as well as the definition of a realistic goal. Furthermore, time constraints need to be taken into account when defining a simulation project and this may mean that for instance that only a crude model can be built.Figure 2: Typical Activities in a Simulation Project When comparing current examination projects with envisaged examination projects for simulation engineers (or, for that matter, projects as they may carry out in their professional career), then a simulation engineer may (need to) address all the phases whereas currently, examination projects tend to cover only a number of them. In the current situation, simulation is often part of examination projects in for instance the Automation Engineering programe. In these projects, the first three activities are usually already addressed by the company at the start of the project, or at least to a fairly large extent. Furthermore, the company usually has to supply input data that the students may use on an “as is” basis. Important steps such as verification and validation of models, and the early analysis of results can be enforced through supervision. Usually, the final interpretation of the simulation results that is the basis for instance for an investment decision is left to the company. A simulation engineer will often be expected to cover a wider range of phases. It would also be expected from a simulation engineer to select the most promising approach(es) for a simulation project. Furthermore, the steps1601De Vin and Jägstam “verification, validation and timely analysis” should be like a second nature to the simulation engineer. In a professional setting, a simulation engineer will often also be held responsible for the correctness of input data and share final responsibility for capital and human resource investments. The need for engineers who can take care of a simulation project from the beginning to the end was one of the reasons to develop the Simulation Engineering programme at Skövde. 5 THE SIMULATION ENGINEERING PROGRAMME AT THE UNIVERSITY OF SKÖVDE but different aspects of simulation results need to be highlighted for different audiences (e.g., machine operators or managing directors).Theoretical Experience Design Experience Process Experience Production Experience Practical ExperienceProblem solverComputer Experience5.1 Subject Area and Size of the Study Programme The subject area of the study programme can be described as “simulation of industrial manufacturing systems”. This includes an overlapping area of Automation Engineering, Industrial Management and supporting/enabling disciplines (such as Computer Science and Mathematics). As Crosbie (2000) points out, finding the right balance between various skills is considered to be one of the key issues to be addressed when designing a simulation curriculum. Since SMEs usually recruit engineers up to BSc/BEng level, it was thought to be appropriate to develop a programme that leads to a Bachelor degree. 5.2 Skills and Tasks of a Simulation Engineer Technical skills of a simulation engineer include computer programming, mathematics (in particular statistics), manufacturing technology, industrial automation, modeling and system design, data acquisition, logistics, production planning and production economics, and product data management (PDM). It should be stressed here that the simulation engineer will need to have a sound knowledge of manufacturing so as to be able to define projects, evaluate results and so on. The ideal simulation engineer as industry would like to receive them straight from college is shown in Figure 3. This engineer is a combination of an engineer with a sound background in a number of traditional engineering disciplines and an engineer with state-of-the-art IT skills and knowledge in a broad range of disciplines, according to VSOP III (1998). Even though such expectations may not be completely realistic as also signaled by SME/AFFT (2001), it definitely poses a challenge for academia to try and meet these expectations as good as possible. Social skills required from a simulation engineer include team-working skills, project based working, good communication skills with people from different backgrounds and at different levels in a company. Simulation and visualisation can be powerful communication tools, Figure 3: The “Ideal” Simulation Engineer The industrial simulation engineer will most likely work project based during the first years of their professional life. This can be as internal or external consultant, simulation application developer (the latter would probably require some continued studies or additional courses), modeling of manufacturing systems. Other roles can be production manager, a coordinating role in production and logistics, or roles related to production economics. 5.3 General Layout of the Study Programme The first year contains only one specific simulation course, ‘introduction to production flow simulation’. This may seem remarkable but it is a consequence of the fact that one has to have a deeper understanding of manufacturing before one is able to simulate manufacturing systems adequately. Therefore, the studies in the first year provide a solid basis in amongst others manufacturing, logistics, mathematics and programming skills (Figure 4). An additional advantage of this approach is that it is relatively easy for students to switch between study programs within automation engineering or industrial management. In this phase of the studies, the emphasize is on giving students a broad but relatively deep understanding of manufacturing and related topics. In the second year, over 50% of the study effort is related to simulation engineering. This includes courses in modeling of manufacturing systems, discrete event simulation, computer aided robotics (geometry simulation), and a simulation project based on a real-life industrial problem. In addition to this, the students read a number of engineering and production management courses, as well as ergonomics and psychology of work. In this phase, the students improve their IT-skills and become acquainted with the latest management philosophy and simulation tools.1602De Vin and Jägstam Year 1 Foundation: Manufacturing, Logistics, Mathematics, Programming Year 2 Simulation and continued studies in logistics and manufacturing Year 3 variant Logist. variant Manuf. Exam. project Exam. project carried out at an SME as these smaller companies do not always can make a license available to the students. Thus, such projects also provide an excellent opportunity for SMEs to become familiar with the use and potential costs and benefits of simulation tools. 6 CONCLUSIONSFigure 4: General Layout of the Study Program In the first half of the third year, students can chose between two variants, namely a package of courses with a focus on manufacturing engineering, and a package related to logistics and management. In this phase, the students continue to build their knowledge and expertise in a selected area of manufacturing topics. Normally (but not automatically), this means that the students specialize either in discrete event simulation or in geometry simulation. In the second half of the third year, they carry out a substantial examination project, normally within industry and sometimes within one of the research projects of the Centre for Intelligent Automation. 5.4 Master Level Studies Currently, there are no plans for a new Master level study programme in simulation engineering. The existing Master level programs (Mechatronics, Computer Integrated Manufacture, and Manufacturing Management) already provide the possibility for Master level studies with a substantial focus on simulation engineering. Furthermore, the main lack of simulation engineers was found to be on B.Sc. level. Graduates from the Simulation Engineering programme qualify for the above-mentioned Master programs. It is currently more likely that the existing programs will be modified or extended with alternative streams than that a completely new Masters programme will be developed. 5.5 Current Status The first group of students for the new programme started in Autumn 2000. This means that the academic year 2000/2001 is a pilot year. It was decided to start with a small group of eight students. Although this is relatively ineffective from a financial point of view, it makes the development of new course modules easier. In addition to this, the students form a close group that is actively involved in the evaluation of the programme. Within the programme, a number of simulation tools are used, but mainly Quest and Igrip. The Centre for Intelligent Automation has some software licenses available that can be used by students doing an examination project in industry. This is particularly helpful when a project isFrom an increased use of simulation tools in industry and an increased number of examination projects related to simulation, the need for a new type of engineer has been identified. This has resulted in the development of a B.Sc. programme in simulation engineering at the University of Skövde, Sweden. The programme as a whole is characterized by a relatively high percentage project related study effort. The first phase is characterized by studies in the field of manufacturing, logistics and supporting disciplines in order to create a firm engineering basis. During the second phase, the focus shifts towards simulation engineering. The third phase starts with a specialization in logistics or in manufacturing engineering and is completed with a substantial examination project. The first graduates are expected in 2003, by which time it is expected that the demand for simulation engineers will be even larger than today. An evaluation of the overall programme can be expected around 2004. REFERENCES Banks J., Carson J. S., and Nelson B. L., 1996. Discreteevent system simulation,.Prentice-Hall, Inc., Upper Saddle River, New Jersey 07458, 2nd edition. Crosbie, R. E., 2000.A model curriculum in modeling and simulation: Do we need it? Can we do it?, In Proceedings of the 2000 Winter Simulation Conference, 16661668 Karlsson J. and Samuelsson, F., 2000. Simulation techniques in industrial environment, Examination project, University of Skövde, (in Swedish) Owen, J. A. and Steeple, D., 1998. New product Introduction: An Examination of SME Automotive Component Suppliers, IMC-15 Conference, Newtownabbey UK. Sadowski D. A. and Grabau, M. R., 2000. Tips for successful practice of simulation, In Proceedings of the 2000 Winter Simulation Conference, 26-31 SME/AFFT, 2001. Forming & Fabricating Tech Trends 2001, published by the Association for Forming & Fabricating Technologies of the Society of Manufacturing Engineers, Dearborn MI, USA VSOP III, 1998. The use of IT in Manufacturing Industry for Integration of Product, Process and Production Development, Industrial Research and Development Corporation, (in Swedish)1603De Vin and Jägstam AUTHOR BIOGRAPHIES LEO J. DE VIN is a Professor and Head of the Centre for Intelligent Automation at the University of Skövde in Sweden. He received his Ph.D. in Computer Aided Process Planning from The University of Twente in The Netherlands, and later worked as a Senior Researcher for The University of Ulster at Jordanstown. He is the founding coordinator of the Mechatronic Systems research platform at the University of Skövde. He is a senior member of the Society of Manufacturing Engineers. His email address is <leo@ite.his.se>. MATS JÄGSTAM is a Ph.D. candidate at the Center for Intelligent Automation, University of Skövde. He received his M.Sc. from University of Skövde. He has been the tutor for the Automation Engineering Program. His main interests are development and implementation of new working methods in an era of Digital Manufacturing. His main interests includes simulation in logistics, production and system development methodologies. His e-mail address is <mats@ite.his.se>.1604。

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