基于最小方差算法对空燃比回路的性能评估
|
摘要
控制系统性能评估的理论研究在过去十年里吸引了许多研究人员和学者的关注。因此,相关评价方法得到了迅速的发展,相继出现了不少新的研究成果。但是,关于控制器性能评价技术的研究领域仍然存在着一些尚未解决的问题,从而限制了性能评价技术在工业过程中的广泛应用。因此,本文从实际应用出发,在前人的研究成果的基础上,尤其是对以最小方差为基准的性能评价方法进行了深入的探索。
目前,如何利用性能评估方法评价汽车中的控制回路逐渐成为炙手可热的研究课题。本文采用基于最小方差算法对汽车发动机中空燃比回路进行性能评估,依据目标的性能指标来缩小控制器的参数范围,以达到更好的控制空燃比的目的。本论文以最小方差基准理论研究为基础,将其应用于空燃比系统模型,实现对空燃比控制回路的性能评估,最后给出基于此算法评估的Matlab仿真。主要研究工作包括:
1.首先,介绍了性能评价领域的历史与研究现状,以及课题的研究方向及意义。
2.简要分析了性评价控制性能算法优劣的因素,总结归纳最小方差算法的历史与现状,并对单输入单输出(SISO)和多输入多输出(MIMO)系统的最小方差算法性能评价方法进行理论推导。
3.确定评估算法与性能指标后,根据空燃比系统模型确定每个变量与控制变量之间的转换关系,然后获得输出数据和延迟时间估计。
4.根据这些获得的信息进行以最小方差为基准的性能指标的计算,并在Matlab中进行仿真实验,评价在所选定的控制器下系统性能的好坏。通过对性能指标的统计规律来分析,定量地给出了可以接受的控制性能指标的上下限,并确定该性能指标下对应控制器参数所能达到的范围。
在文章的结尾,总结了自己所做的工作,对下一步的研究方向做了进一步的展望。
Performance assessement of control systems in the past two decades has attracted many researchers and scholars. Therefore, the relevant assessment methods have been developed rapidly, and also considerable new research results have obtained. However, there are still many unresolved issues on the field of controller performance assessment techniques, which limits the wide range of application in process industry. Therefore, this paper proceeds from the view of practical application, based on the results of previous researchers, and especially explores deeply the minimum variance(MV) based performance assessment.
Currently, how to use the performance assessment for evaluating vehicle control loops has been one of most active research topic. This paper adopts the algorithm of MV based performance assessment to evaluate the air-fuel ratio control loop of gas engine. Base on expected performance index, the scope of the controller parameters can be narrowed to achieve the purpose of better control of air-fuel ratio. It can also expand the application scope to increase the applicability. The paper is arranged as follows: first, the theory of MV benchmark is reviewed, which will be applied to the air-fuel ratio system model. Then the optimal values of the parameters in the range of air-fuel ratio control loop are obtained. Last, the simulations of system base on this algorithm in Matlab are given. The main work includes:
1.The history and current status of the field of performance assessment is presented, as well as the direction and significance of the research topic.
2.The factors of the merits and limitations of different control performance algorithms are analysed, the history and current status of MV algorithm is summarized, and also the theoretical derivation of MV algorithm of SISO and MIMO systems are given.
3.After the determination of the algorithms and the performance indicators, determine the conversion relationship between each variable and control variables based on the model of air-fuel ratio systems, then obtain output data and estimation of time delay.
4.According to the obtained knowledge, the MV performance index is calculated, Matlab simulation are carried out as well, which are used to evaluate the system work in a good or bad condition under the selected controller. By analysing the statistics laws of performance assessment, the acceptable upper and lower control performance indicators are quantitatively given. And the performance can be achieved under the corresponding range of controller parameters to achieve this ultimate goal of this paper.
Finally, sum up the work that I have done, and make further prospects on the future research directions.
目前,如何利用性能评估方法评价汽车中的控制回路逐渐成为炙手可热的研究课题。本文采用基于最小方差算法对汽车发动机中空燃比回路进行性能评估,依据目标的性能指标来缩小控制器的参数范围,以达到更好的控制空燃比的目的。本论文以最小方差基准理论研究为基础,将其应用于空燃比系统模型,实现对空燃比控制回路的性能评估,最后给出基于此算法评估的Matlab仿真。主要研究工作包括:
1.首先,介绍了性能评价领域的历史与研究现状,以及课题的研究方向及意义。
2.简要分析了性评价控制性能算法优劣的因素,总结归纳最小方差算法的历史与现状,并对单输入单输出(SISO)和多输入多输出(MIMO)系统的最小方差算法性能评价方法进行理论推导。
3.确定评估算法与性能指标后,根据空燃比系统模型确定每个变量与控制变量之间的转换关系,然后获得输出数据和延迟时间估计。
4.根据这些获得的信息进行以最小方差为基准的性能指标的计算,并在Matlab中进行仿真实验,评价在所选定的控制器下系统性能的好坏。通过对性能指标的统计规律来分析,定量地给出了可以接受的控制性能指标的上下限,并确定该性能指标下对应控制器参数所能达到的范围。
在文章的结尾,总结了自己所做的工作,对下一步的研究方向做了进一步的展望。
Performance assessement of control systems in the past two decades has attracted many researchers and scholars. Therefore, the relevant assessment methods have been developed rapidly, and also considerable new research results have obtained. However, there are still many unresolved issues on the field of controller performance assessment techniques, which limits the wide range of application in process industry. Therefore, this paper proceeds from the view of practical application, based on the results of previous researchers, and especially explores deeply the minimum variance(MV) based performance assessment.
Currently, how to use the performance assessment for evaluating vehicle control loops has been one of most active research topic. This paper adopts the algorithm of MV based performance assessment to evaluate the air-fuel ratio control loop of gas engine. Base on expected performance index, the scope of the controller parameters can be narrowed to achieve the purpose of better control of air-fuel ratio. It can also expand the application scope to increase the applicability. The paper is arranged as follows: first, the theory of MV benchmark is reviewed, which will be applied to the air-fuel ratio system model. Then the optimal values of the parameters in the range of air-fuel ratio control loop are obtained. Last, the simulations of system base on this algorithm in Matlab are given. The main work includes:
1.The history and current status of the field of performance assessment is presented, as well as the direction and significance of the research topic.
2.The factors of the merits and limitations of different control performance algorithms are analysed, the history and current status of MV algorithm is summarized, and also the theoretical derivation of MV algorithm of SISO and MIMO systems are given.
3.After the determination of the algorithms and the performance indicators, determine the conversion relationship between each variable and control variables based on the model of air-fuel ratio systems, then obtain output data and estimation of time delay.
4.According to the obtained knowledge, the MV performance index is calculated, Matlab simulation are carried out as well, which are used to evaluate the system work in a good or bad condition under the selected controller. By analysing the statistics laws of performance assessment, the acceptable upper and lower control performance indicators are quantitatively given. And the performance can be achieved under the corresponding range of controller parameters to achieve this ultimate goal of this paper.
Finally, sum up the work that I have done, and make further prospects on the future research directions.
引文
[1]俞娅娅.控制系统在线闭环辨识与性能评价方法研究.华北电力大学,2004.
[2]薛美盛,Fei Qi,张毅,王川,白东进.控制回路性能评估综述. 1671-7848 (2009)05-0507-06,2009.
[3] Harris T J. Seppala C T. Desborough L D A Review of Performance Monitoring and Assessment Techniques for Univariate and Multivariate Control Systems 1999.
[4] Maying Y. Xiao M J Controller Performance Assessment and Monitoring-theory and Application 2002.
[5]孟嗣宗,郭少平,张文海.发动机精确空燃比控制方法的研究[J].内燃机工程,1999.
[6]张文海.发动机稳态工况和过渡工况空燃比控制方法的研究.清华大学硕士学位论文,1996.
[7] Desborough L, Miller R. Increasing customer value of industrial control performance monitoring-Honeywell's experience[J]. AIChE Symposium Series, 2002, 98:153-186.
[8] Astrom K J. Introduction to stochastic control theory[M].New York: Academic, 1970.
[9] Box J E P, Jenkins J M. Time series analysis: Forecasting and control[M].San Francisco,CA:Holden-Day,1970.
[10] DeVries W R, Wu S. Evaluation of process control effectiveness and diagnosis of variation in paper basis weight via multivariate time series analysis[J]. IEEE Transactions on Automatic Control, 1978, 23(4):702-708.
[11] Harris T J. Assessment of control loop performance[J]. Canadian Journal of Chemical Engineering, 1989, 67(5):856-861.
[12] Desborough L, Harris T J. Performance assessment measures for univariate feedback control [J]. Canadian Journal of Chemical Engineering, 1992, 70(6): 1186-1197.
[13] Huang B, Ding S X, Thornhill N. Practical solutions to multivariate feedback control performance assessment problem: Reduced a priori knowledge of interactor matrices[J]. Journal of Process Control, 2005, 15(5):573-583.
[14] Airikka P'Heikkinen Niemeila I.Process control assessment on foundation field bus devices[C]//ISA Expo,2003:2I.23.
[15] Stanfelj N, Marlin T E, MacGregor J F. Monitoring and diagnosing process control performance: The single-loop case[J]. Industrial and Engineering Chemistry Research, 1993, 32(2):301-314.
[16] Desborough L, Harris T J. Performance assessment measures for univariate feedforward/ feedback control[J]. Canadian Journal of Chemical Engineering, 1993, 71(4):605-616.
[17] Tyler M L, Morari M. Performance monitoring of control systems using likelihood methods [C].Seattle, WA, USA: American Control Conference, 1995.
[18] Harris T J, Boudreau F, MacGregor J F. Performance assessment of multivariable feedback controllers[J]. Automatica,1996, 32(11):1505-1518.
[19] Huang B. Multivariate controller performance analysis: method, application and challenge[J]. AIChE Symposium Series, 2002, 98(326):190-207.
[20] Huang B, Ding S X, Thornhill N. Alternative solutions to multivariate control performance assessment problems[J]. Journal of Process Control, 2006, 16(5): 457-471.
[21] Horch A, Isaksson A J. Modified index for control performance assessment [J].Journal of Process Control,1 999,9(6):475-483.
[22] Huang B, Shah S L. Practical issues in multivariable feedback control performance assessment[J].Journal of Process Control,1998,8(5-6):421-430.
[23] Huang B, Shah S L, Miller R. Feed/forward plus feedback controller performance assessment of MIMO systems[J]. IEEE Transactions on Control Systems Technology, 2000, 8(3): 580-587.
[24] Huang B, Shah S L, Kwok EK. Good, bad or optimal? Performance assessment of multivariable processes[J]. Automatica,1997, 33(6):1175-1183.
[25] Grimble M J. Restricted structure control loop performance assessment for state-space systems[C]. Anchorage, AK: Proceedings of the American Control Conference, 2002.
[26] Yu J, Qin S J. Statistical MIMO controller performance monitoring. Part I: Data-driven covariance benchmark[J]. Journal of Process Control, 2008, 18(3-4): 277-296.
[27] Harris T J, Seppala C T, Jofriet P J et al. Plant-wide feedback control performance assessment using an expert-system framework [J]. Control Engineering Practice, 1996 , 4 (9):1297-1303.
[28] Joronen T. A simple fuzzy statistical evaluation for process control performance[C]. Gelendzhik, Russia: IEEE International Conference on ArtificialIntelligence Systems, 2002.
[29] Kendra S J, Cinar A. Controller performance assessment by frequency domain techniques [J]. Journal of Process Control , 1997, 7(3):181-194.
[30] Harris T J. Statistical properties of quadratic-type performance indices [J].Journal of Process Control, 2004, 14 (8):899-914.
[31] Basseville M. On-board component fault detection and isolation using the statistical local approach [J]. Automatics, 1998, 34(11):1391-1415.
[32] Thornhill N F, Oettinger M, Fedenczuk P. Refinery-wide control loop performance assessment[J]. Journal of Process Control,1999, 9(2): 109-124.
[33] Gerry J P. Prioritizing and optimizing problem loops using a loop monitoring system[J]. Technical Papers-ISA, 2002, 422: 145-150.
[34] Ingimundatson A. Performance assessment using a normalized gradient[J]. Journal of Process Control, 2006, 16(10):1013-1020.
[35] Yim S Y, Ananthakumar H G, Benabbas L, et al. Using process topology in plant-wide control loop performance assessment[J]. Computers and Chemical Engineering, 2006, 31(2):86-99.
[36]李刚,王庆林.多扰动动态下的PID控制器性能评价[J].微计算机信息.2007, 23 (10-1);13-14, 76. (Li Gang, Wang Qinglin. PID controller performance assessment for system with multidisturbance dynamics[J]. Microcomputer Information, 2007, 23 (10-1):13-14, 76).
[37]张彤,王庆林.基于MV基准界的多变量控制系统性能评价方法[J].北京理工大学学报, 2007, 27(8):689-694, 699. (Zhang Tong, Wang Qinglin. Multivariable control system performance assessment methods based on MV benchmarking bounds [J]. Journal of Beijing Institute of Technology, 2007, 27(8):689-694,699).
[38]孙金明,左信,季德伟等. PID控制器性能评价[J].化工自动化及仪表, 2004, 31(5):21-23. (Sun Jinming, Zuo Xin, Ji De wei, et al. PID performance assessment[J]. Control and Instruments in Chemical Industry, 2004, 31(5): 21-23.).
[39]张强.基于系统输人/输出数据的预测控制器性能监控[D].上海:上海交通大学,2007. (Zhang Qiang. Performance monitoring of model predictive controller using input/output data [D]. Shanghai: Shanghai Jiaotong University, 2007.).
[40] Thornhill N F, Sadowski R ,Davis J R, et al. Practical experiences in refinery control loop performance assessment[C]. Exeter, UK: UKACC International Conference on Control, 1996.
[41] Jelali M. Performance assessment of control systems in rolling mills-application to strip thickness and flatness control[J]. Journal of Process Control, 2007, 17(10): 805-816.
[42] Hagglund T. Industrial implementation of on-line performance monitoring tools[J]. Control Engineering Practice, 2005, 13(11):1383-1390.
[43] K. J. Astrom. Introduction to Stochastic Control Theory. Academic Press, 1979.
[44] L. Desborough, T. J. Harris. performance assessment measures for univariate feedforward/feedback control, Canadian Journal of Chemical Engineering, 1993, 26:605-616.
[45] N. Stanfelj, T. E. Marlin, J. F MacGregor, Monitoring and diagnosis of process control performance: The single-loop case. Ind. Eng. Chem. Res, 1993, 67 (10): 856-869.
[46] D. kozub, C. E. Garcia. Monitoring and diagnosis of automated controllers in the chemical process industries. In: AIChE Annual Meeting, 1993.
[47] M. L. Tyler, M. Morari, performance monitoring of control systems using maximum likelihood methods, Automatica, 1996, 32(8):1145-1162.
[48] C. B. Lynch, G. A. Dumont, Control loop performance monitoring, IEEE Trans. Cont. Sys. Tech, 1996, 4(2):185-192.
[49]刘芳.核电站除氧器控制系统性能评估,上海交通大学,硕士学位论文, 2007.
[50] Eriksson, P. and A. Isaksson. Some aspects of control performance monitoring. In Proc.3re IEEE Conf.. Control Applications, Glasgow, Scotland, 1994:1029- 1034.
[51] Harris T., C. Seppala, and L. Desborough. A review of performance monitoring and assessment techniques for univariate and multivariate control system[J]. Process control, 1999, 9:1-17.
[52] Qin, J. Control performance monitoring– a review and assessment, Comp. And Chem.Eng., 1998, 1(23):173-186.
[53] G. E. P. Box, G. M. Jenkins. Time series analysis: For casting and control, 1970.
[54]李国勇.智能控制与MATLAB在电控发动机中的应用,电子工业出版, 2007.
[55] Harris, T. Assessment of closed loop performance. Can.j. Chem. Eng. 1989. 67: 865-861.
[56]倪计民.汽车内燃机原理[M].上海:同济大学出版社,1996.
[57] M Cichy,M Konczakowsky. Bond Graph Model of the IC Engine as an Element of Energetic Systems[J]. Mechanism and Machie Theory, 2001(36):683-687.
[58] Duksun Shim, Jaehong Park, Pramod P, Khargonekar and William B Ribbens. Reducing Automotive Engine Speed Fluctuation at Idle[J]. IEEE Transactions onControl Systems Technology, 1996, 4(4).
[59] Joseph J, Scillieri, Julia H, Buckland and James S, Freudenberg. Reference Feedforward in the Idle Speed Control of a Direct-Injection Spark-Ignition Engine[J]. IEEE Transactions on Control Systems Technology, 2005, 54(1).
[60] Mirolaw Wendeker, Jacek Czarnigowski. Hybrid Air/Fuel Ratio Control Using the Adaptive Estimation and Neural Network[J]. SAE, 2000, Paper No. 2000-01-1248.
[61]陈家瑞.汽车构造[M].北京:机械工业出版社, 2000.
[62]鹿笑冬,欧阳明高等.用于发动机控制模型的基于循环的平均值离散建模方法[J].内燃机学报, 2001.01.
[63] BOSCH.汽车工程手册[M].北京:北京理工大学出版社, 1999.
[64]刘铮,王建昕.汽车发动机原理教程[M].北京:清华大学出版社, 2001.
[65]张俊红.汽车发动机构造[M].天津:天津大学出版社, 2006.
[66]杨杰民.现代汽车发动机构造[M].上海:上海交通大学出版社, 1999.
[2]薛美盛,Fei Qi,张毅,王川,白东进.控制回路性能评估综述. 1671-7848 (2009)05-0507-06,2009.
[3] Harris T J. Seppala C T. Desborough L D A Review of Performance Monitoring and Assessment Techniques for Univariate and Multivariate Control Systems 1999.
[4] Maying Y. Xiao M J Controller Performance Assessment and Monitoring-theory and Application 2002.
[5]孟嗣宗,郭少平,张文海.发动机精确空燃比控制方法的研究[J].内燃机工程,1999.
[6]张文海.发动机稳态工况和过渡工况空燃比控制方法的研究.清华大学硕士学位论文,1996.
[7] Desborough L, Miller R. Increasing customer value of industrial control performance monitoring-Honeywell's experience[J]. AIChE Symposium Series, 2002, 98:153-186.
[8] Astrom K J. Introduction to stochastic control theory[M].New York: Academic, 1970.
[9] Box J E P, Jenkins J M. Time series analysis: Forecasting and control[M].San Francisco,CA:Holden-Day,1970.
[10] DeVries W R, Wu S. Evaluation of process control effectiveness and diagnosis of variation in paper basis weight via multivariate time series analysis[J]. IEEE Transactions on Automatic Control, 1978, 23(4):702-708.
[11] Harris T J. Assessment of control loop performance[J]. Canadian Journal of Chemical Engineering, 1989, 67(5):856-861.
[12] Desborough L, Harris T J. Performance assessment measures for univariate feedback control [J]. Canadian Journal of Chemical Engineering, 1992, 70(6): 1186-1197.
[13] Huang B, Ding S X, Thornhill N. Practical solutions to multivariate feedback control performance assessment problem: Reduced a priori knowledge of interactor matrices[J]. Journal of Process Control, 2005, 15(5):573-583.
[14] Airikka P'Heikkinen Niemeila I.Process control assessment on foundation field bus devices[C]//ISA Expo,2003:2I.23.
[15] Stanfelj N, Marlin T E, MacGregor J F. Monitoring and diagnosing process control performance: The single-loop case[J]. Industrial and Engineering Chemistry Research, 1993, 32(2):301-314.
[16] Desborough L, Harris T J. Performance assessment measures for univariate feedforward/ feedback control[J]. Canadian Journal of Chemical Engineering, 1993, 71(4):605-616.
[17] Tyler M L, Morari M. Performance monitoring of control systems using likelihood methods [C].Seattle, WA, USA: American Control Conference, 1995.
[18] Harris T J, Boudreau F, MacGregor J F. Performance assessment of multivariable feedback controllers[J]. Automatica,1996, 32(11):1505-1518.
[19] Huang B. Multivariate controller performance analysis: method, application and challenge[J]. AIChE Symposium Series, 2002, 98(326):190-207.
[20] Huang B, Ding S X, Thornhill N. Alternative solutions to multivariate control performance assessment problems[J]. Journal of Process Control, 2006, 16(5): 457-471.
[21] Horch A, Isaksson A J. Modified index for control performance assessment [J].Journal of Process Control,1 999,9(6):475-483.
[22] Huang B, Shah S L. Practical issues in multivariable feedback control performance assessment[J].Journal of Process Control,1998,8(5-6):421-430.
[23] Huang B, Shah S L, Miller R. Feed/forward plus feedback controller performance assessment of MIMO systems[J]. IEEE Transactions on Control Systems Technology, 2000, 8(3): 580-587.
[24] Huang B, Shah S L, Kwok EK. Good, bad or optimal? Performance assessment of multivariable processes[J]. Automatica,1997, 33(6):1175-1183.
[25] Grimble M J. Restricted structure control loop performance assessment for state-space systems[C]. Anchorage, AK: Proceedings of the American Control Conference, 2002.
[26] Yu J, Qin S J. Statistical MIMO controller performance monitoring. Part I: Data-driven covariance benchmark[J]. Journal of Process Control, 2008, 18(3-4): 277-296.
[27] Harris T J, Seppala C T, Jofriet P J et al. Plant-wide feedback control performance assessment using an expert-system framework [J]. Control Engineering Practice, 1996 , 4 (9):1297-1303.
[28] Joronen T. A simple fuzzy statistical evaluation for process control performance[C]. Gelendzhik, Russia: IEEE International Conference on ArtificialIntelligence Systems, 2002.
[29] Kendra S J, Cinar A. Controller performance assessment by frequency domain techniques [J]. Journal of Process Control , 1997, 7(3):181-194.
[30] Harris T J. Statistical properties of quadratic-type performance indices [J].Journal of Process Control, 2004, 14 (8):899-914.
[31] Basseville M. On-board component fault detection and isolation using the statistical local approach [J]. Automatics, 1998, 34(11):1391-1415.
[32] Thornhill N F, Oettinger M, Fedenczuk P. Refinery-wide control loop performance assessment[J]. Journal of Process Control,1999, 9(2): 109-124.
[33] Gerry J P. Prioritizing and optimizing problem loops using a loop monitoring system[J]. Technical Papers-ISA, 2002, 422: 145-150.
[34] Ingimundatson A. Performance assessment using a normalized gradient[J]. Journal of Process Control, 2006, 16(10):1013-1020.
[35] Yim S Y, Ananthakumar H G, Benabbas L, et al. Using process topology in plant-wide control loop performance assessment[J]. Computers and Chemical Engineering, 2006, 31(2):86-99.
[36]李刚,王庆林.多扰动动态下的PID控制器性能评价[J].微计算机信息.2007, 23 (10-1);13-14, 76. (Li Gang, Wang Qinglin. PID controller performance assessment for system with multidisturbance dynamics[J]. Microcomputer Information, 2007, 23 (10-1):13-14, 76).
[37]张彤,王庆林.基于MV基准界的多变量控制系统性能评价方法[J].北京理工大学学报, 2007, 27(8):689-694, 699. (Zhang Tong, Wang Qinglin. Multivariable control system performance assessment methods based on MV benchmarking bounds [J]. Journal of Beijing Institute of Technology, 2007, 27(8):689-694,699).
[38]孙金明,左信,季德伟等. PID控制器性能评价[J].化工自动化及仪表, 2004, 31(5):21-23. (Sun Jinming, Zuo Xin, Ji De wei, et al. PID performance assessment[J]. Control and Instruments in Chemical Industry, 2004, 31(5): 21-23.).
[39]张强.基于系统输人/输出数据的预测控制器性能监控[D].上海:上海交通大学,2007. (Zhang Qiang. Performance monitoring of model predictive controller using input/output data [D]. Shanghai: Shanghai Jiaotong University, 2007.).
[40] Thornhill N F, Sadowski R ,Davis J R, et al. Practical experiences in refinery control loop performance assessment[C]. Exeter, UK: UKACC International Conference on Control, 1996.
[41] Jelali M. Performance assessment of control systems in rolling mills-application to strip thickness and flatness control[J]. Journal of Process Control, 2007, 17(10): 805-816.
[42] Hagglund T. Industrial implementation of on-line performance monitoring tools[J]. Control Engineering Practice, 2005, 13(11):1383-1390.
[43] K. J. Astrom. Introduction to Stochastic Control Theory. Academic Press, 1979.
[44] L. Desborough, T. J. Harris. performance assessment measures for univariate feedforward/feedback control, Canadian Journal of Chemical Engineering, 1993, 26:605-616.
[45] N. Stanfelj, T. E. Marlin, J. F MacGregor, Monitoring and diagnosis of process control performance: The single-loop case. Ind. Eng. Chem. Res, 1993, 67 (10): 856-869.
[46] D. kozub, C. E. Garcia. Monitoring and diagnosis of automated controllers in the chemical process industries. In: AIChE Annual Meeting, 1993.
[47] M. L. Tyler, M. Morari, performance monitoring of control systems using maximum likelihood methods, Automatica, 1996, 32(8):1145-1162.
[48] C. B. Lynch, G. A. Dumont, Control loop performance monitoring, IEEE Trans. Cont. Sys. Tech, 1996, 4(2):185-192.
[49]刘芳.核电站除氧器控制系统性能评估,上海交通大学,硕士学位论文, 2007.
[50] Eriksson, P. and A. Isaksson. Some aspects of control performance monitoring. In Proc.3re IEEE Conf.. Control Applications, Glasgow, Scotland, 1994:1029- 1034.
[51] Harris T., C. Seppala, and L. Desborough. A review of performance monitoring and assessment techniques for univariate and multivariate control system[J]. Process control, 1999, 9:1-17.
[52] Qin, J. Control performance monitoring– a review and assessment, Comp. And Chem.Eng., 1998, 1(23):173-186.
[53] G. E. P. Box, G. M. Jenkins. Time series analysis: For casting and control, 1970.
[54]李国勇.智能控制与MATLAB在电控发动机中的应用,电子工业出版, 2007.
[55] Harris, T. Assessment of closed loop performance. Can.j. Chem. Eng. 1989. 67: 865-861.
[56]倪计民.汽车内燃机原理[M].上海:同济大学出版社,1996.
[57] M Cichy,M Konczakowsky. Bond Graph Model of the IC Engine as an Element of Energetic Systems[J]. Mechanism and Machie Theory, 2001(36):683-687.
[58] Duksun Shim, Jaehong Park, Pramod P, Khargonekar and William B Ribbens. Reducing Automotive Engine Speed Fluctuation at Idle[J]. IEEE Transactions onControl Systems Technology, 1996, 4(4).
[59] Joseph J, Scillieri, Julia H, Buckland and James S, Freudenberg. Reference Feedforward in the Idle Speed Control of a Direct-Injection Spark-Ignition Engine[J]. IEEE Transactions on Control Systems Technology, 2005, 54(1).
[60] Mirolaw Wendeker, Jacek Czarnigowski. Hybrid Air/Fuel Ratio Control Using the Adaptive Estimation and Neural Network[J]. SAE, 2000, Paper No. 2000-01-1248.
[61]陈家瑞.汽车构造[M].北京:机械工业出版社, 2000.
[62]鹿笑冬,欧阳明高等.用于发动机控制模型的基于循环的平均值离散建模方法[J].内燃机学报, 2001.01.
[63] BOSCH.汽车工程手册[M].北京:北京理工大学出版社, 1999.
[64]刘铮,王建昕.汽车发动机原理教程[M].北京:清华大学出版社, 2001.
[65]张俊红.汽车发动机构造[M].天津:天津大学出版社, 2006.
[66]杨杰民.现代汽车发动机构造[M].上海:上海交通大学出版社, 1999.