混合动力传动系统建模及优化控制研究
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摘要
摘要：随着经济的发展,能源问题逐渐成为一个战略问题。在石油价格持续增长和全球变暖趋势的影响下,世界各国对节能减排提出了更高的要求。汽车作为燃油消耗及温室气体排放大户,如何改善其燃油经济性和排放已成为当务之急。
集成了发动机及电动机的混合动力汽车在保持传统汽车优点的同时,又提高了燃油经济性,被认为是中短期内解决节能减排问题的最佳选择。混合动力传动系统由常规变速箱集成电机组成,是一个机电液耦合的复杂系统,国内外对混合动力传动系统的能量管理等方面做了大量的研究,但对于复杂传动系统建模及控制、工况切换、挡位切换、乘坐舒适性等方面均较少研究。结合国内外在车辆建模、优化控制等方面的研究成果,对混合动力传动系统进行了分析,重点研究了系统运动学、动力学特性分析及建模方法、极端工况下的控制、状态切换优化等问题,所提出的方法具有很强的通用性,可用于其它类型车辆建模及优化控制。
本文首先建立了混合动力车辆的动态特性模型,并对影响操作性和工况切换过程平顺性的子系统进行了重点研究。由于非线性部件广泛存在于传动系统中,基于净贡献判据提出了一种非线性系统的线性参数模型递归辨识算法,通过迭代求解实现了模型项的增加、减少和系数矩阵的更新,并可同时得到模型结构和参数,仿真结果表明所提出的算法具有很高的辨识精度和数值稳定性。
根据杠杆等效原理,建立了基于键合图的综合分析方法,并将之用于某双模式混合动力传动系统的特性分析及建模,将行星传动等复杂传动系统的运动学及动力学分析统一于同一过程中；由于传动系统结构复杂,很多部件的参数很难通过测量得到,本文研究了一种基于测试数据的模型参数辨识方法,采用遗传算法结合键合图分析结果辨识模型参数,所提出的方法同样也可用于类似系统模型辨识和控制参数优化。
状态反馈控制和模糊控制在数学上具有一定的联系,通过分析,提出了一种全新的状态反馈控制器设计方法,结合模糊逻辑,设计了模糊-状态反馈控制器,并将之用于时滞非线性系统的镇定和对给定信号的跟踪。与常规的状态反馈控制器相比,参数选取方便,控制器响应速度快,鲁棒性更强。
为了提高双模式混合动力传动系统的效率,根据运动学分析结果,提出了传动系统输入、输出速比控制的概念,并以此为基础,综合考虑发动机的热效率及传动系统的效率,得到了整车燃油经济性瞬时最优情况下发动机的运行工况点。研究发现通过减小传动系统内部的能量循环,能够显著提高传动系统的效率和车辆的燃油经济性。
针对采用AMT (Automated Manual Transmission)的某并联混合动力车辆在运行工况切换和挡位切换过程中存在的冲击较大等问题,本文基于最优控制的相关理论,提出了一种离合器接合过程最优跟踪曲线的设计方法,并研究了解耦控制算法实现对优化曲线的跟踪,离合器接合平稳、快速,大大减小了冲击。
离合器及其驱动机构是一个非线性系统,为了实现对其运动的精确控制,研究了一种非线性最优控制算法,首先通过伪线性化得到系统的状态空间模型,由于模型的参数为时变参数,会随着系统状态的变化而改变,控制器通过每一步求解Riccati方程得到最优控制律,从而实现运行工况切换或换挡过程中离合器位移的精确控制,充分满足了混合动力车辆对过渡工况快速性和平顺性等方面的需求。
二次型最优指标函数权重矩阵通常由经验法得到,基于输出协方差约束,研究了一种Q和R的计算方法,保证控制器稳定的同时,对系统中的过程噪声也具有很强的抑制能力。
非线性最优控制器在应用过程中的每一步都需要求解状态相关Riccati方程,为了满足实际系统应用的需要,研究了一种基于矩阵向量化和牛顿梯度的迭代方法求解Riccati方程。为了满足算法快速收敛的需要,考虑到系统状态变化的连续性,迭代求得的Riccati方程解将被作为初始值代入下一状态相关Riccati方程的求解过程中。与常规的方法相比,该方法大大加快了方程的求解速度,为控制器的实时应用提供了前提。
为了加快离合器的接合过程,同时减小传动系统输出轴的转矩波动,提出了一种耦合双滑模控制算法,通过协调控制发动机和电机的输出转矩,减小了运行工况和挡位切换过程中的冲击,提高了乘坐舒适性。
通过本课题的研究,更进一步了解了混合动力传动系统的设计及优化控制等关键技术,所提出的分析方法具有很强的适应性,可为类似研究提供借鉴。
ABSTRACT:Considering the great impact of fuel price increase and global warming trend, a growing demand on energy conservation and reduction of carbon dioxide emissions have been put forward world widely. The automotive industry has also been pressing to produce automobiles with better fuel economy and lower exhaust emissions. Compared with conventional vehicles, hybrid electric vehicles (HEV) are cleaner and more efficient. Based on the comprehensive studies of the domestic and foreign achievements, hybrid transmission systems are analyzed in this thesis.
In the first place, a dynamic HEV model is derived. Considering the wide existence of nonlinear components in the transmission system, a linear-in-the-parameters model identification algorithm is derived, based on the net contribution criteria, so that both the structure and parameters can be obtained simultaneously and recursively. Simulation resultscomfirm its efficacy.
According to the lever equivalent analog approach, a unified bond graph based analysis method is proposed and used for both the kinematics and dynamics modeling of a two-mode hybrid transmission system. Considering the unknown parameters of most components, a data based parameter identification algorithm is proposed, which usethe genetic algorithm (GA) and bond graph analysis results.
Based on the analysisoffuzzy control and output feedback control algorithm, a fuzzy state feedback controller is presented and applied to the stabilization of a time-delay nonlinear system and tracking control. Compared with the conventional state feedback controller, it is easier for the proposed algorithm to select the controller parameters and with better robustness.Transformation of power withindifferent types, which is called "power circulation", happens frequently and influences the transmission efficiency greatly. To address this issue, a transmission speed ratio based control strategy is researched. Considering the efficiency of both engine and transmission system, the engine operation points for instantaneous optimal fuel economy is derived. The efficiency of the transmission system and vehicle fuel economy are significantly improved.
To improve the mode switch and gear shifting impact of a hybrid vehicle equipped with an AMT, an optimal engine and clutch reference speeds generating algorithm is proposed. Gear shifting duration weight is used to balance the performance of gear shifting duration and smoothness. Numerical simulation results indicate that the balanced shifting performance can be achieved through using decoupled tracking control of the optimal reference speeds during the clutch engagement process.
The nonlinear characteristic of the clutch and its driven systems determin that it is hard to get satisfied results with conventional control approaches. Therefore, a nonlinear optimal control algorithm is researched. By solving the state dependent Riccati equation (SDRE) each step, an optimal control law is derived. Hence, a well clutch displacement during the work condition switch and gear shifting process is guaranteed.Optimal cost function index matrices Q and R are usually obtained by empirical methods. Based on the concept of output covariance constraint, an optimal cost function index matrix calculation method is researched so that the stability and robustness of the optimal controller can be guaranteed simultaneously.
A method that iteratively solving the SDRE online based on matrix vectorization and Newton gradient is derived. Considering the continuous of the system states, iterative results of each step are taken as the initial condition for the next step of SDRE solving. Compared with the conventional method, the computation load is dramaticlly reduced.
In order to accelerate the clutch engagement process and also reduce the transmission output torque ripple, a coupled dual sliding mode control strategy is proposed. The two portion of the controller coordinate with each other and the torque change during the transition process are compensated by the motor. The controller can not only guarantee the fast mode switch, but also reduce the output torque vibration simultaneously.
To sum up, through the research conducted in this thesis, further understanding of the key technologies of hybrid transmission system analysis can be achieved. The proposed analysis methods have strong adaptability and can provide reference for similar researches.
集成了发动机及电动机的混合动力汽车在保持传统汽车优点的同时,又提高了燃油经济性,被认为是中短期内解决节能减排问题的最佳选择。混合动力传动系统由常规变速箱集成电机组成,是一个机电液耦合的复杂系统,国内外对混合动力传动系统的能量管理等方面做了大量的研究,但对于复杂传动系统建模及控制、工况切换、挡位切换、乘坐舒适性等方面均较少研究。结合国内外在车辆建模、优化控制等方面的研究成果,对混合动力传动系统进行了分析,重点研究了系统运动学、动力学特性分析及建模方法、极端工况下的控制、状态切换优化等问题,所提出的方法具有很强的通用性,可用于其它类型车辆建模及优化控制。
本文首先建立了混合动力车辆的动态特性模型,并对影响操作性和工况切换过程平顺性的子系统进行了重点研究。由于非线性部件广泛存在于传动系统中,基于净贡献判据提出了一种非线性系统的线性参数模型递归辨识算法,通过迭代求解实现了模型项的增加、减少和系数矩阵的更新,并可同时得到模型结构和参数,仿真结果表明所提出的算法具有很高的辨识精度和数值稳定性。
根据杠杆等效原理,建立了基于键合图的综合分析方法,并将之用于某双模式混合动力传动系统的特性分析及建模,将行星传动等复杂传动系统的运动学及动力学分析统一于同一过程中；由于传动系统结构复杂,很多部件的参数很难通过测量得到,本文研究了一种基于测试数据的模型参数辨识方法,采用遗传算法结合键合图分析结果辨识模型参数,所提出的方法同样也可用于类似系统模型辨识和控制参数优化。
状态反馈控制和模糊控制在数学上具有一定的联系,通过分析,提出了一种全新的状态反馈控制器设计方法,结合模糊逻辑,设计了模糊-状态反馈控制器,并将之用于时滞非线性系统的镇定和对给定信号的跟踪。与常规的状态反馈控制器相比,参数选取方便,控制器响应速度快,鲁棒性更强。
为了提高双模式混合动力传动系统的效率,根据运动学分析结果,提出了传动系统输入、输出速比控制的概念,并以此为基础,综合考虑发动机的热效率及传动系统的效率,得到了整车燃油经济性瞬时最优情况下发动机的运行工况点。研究发现通过减小传动系统内部的能量循环,能够显著提高传动系统的效率和车辆的燃油经济性。
针对采用AMT (Automated Manual Transmission)的某并联混合动力车辆在运行工况切换和挡位切换过程中存在的冲击较大等问题,本文基于最优控制的相关理论,提出了一种离合器接合过程最优跟踪曲线的设计方法,并研究了解耦控制算法实现对优化曲线的跟踪,离合器接合平稳、快速,大大减小了冲击。
离合器及其驱动机构是一个非线性系统,为了实现对其运动的精确控制,研究了一种非线性最优控制算法,首先通过伪线性化得到系统的状态空间模型,由于模型的参数为时变参数,会随着系统状态的变化而改变,控制器通过每一步求解Riccati方程得到最优控制律,从而实现运行工况切换或换挡过程中离合器位移的精确控制,充分满足了混合动力车辆对过渡工况快速性和平顺性等方面的需求。
二次型最优指标函数权重矩阵通常由经验法得到,基于输出协方差约束,研究了一种Q和R的计算方法,保证控制器稳定的同时,对系统中的过程噪声也具有很强的抑制能力。
非线性最优控制器在应用过程中的每一步都需要求解状态相关Riccati方程,为了满足实际系统应用的需要,研究了一种基于矩阵向量化和牛顿梯度的迭代方法求解Riccati方程。为了满足算法快速收敛的需要,考虑到系统状态变化的连续性,迭代求得的Riccati方程解将被作为初始值代入下一状态相关Riccati方程的求解过程中。与常规的方法相比,该方法大大加快了方程的求解速度,为控制器的实时应用提供了前提。
为了加快离合器的接合过程,同时减小传动系统输出轴的转矩波动,提出了一种耦合双滑模控制算法,通过协调控制发动机和电机的输出转矩,减小了运行工况和挡位切换过程中的冲击,提高了乘坐舒适性。
通过本课题的研究,更进一步了解了混合动力传动系统的设计及优化控制等关键技术,所提出的分析方法具有很强的适应性,可为类似研究提供借鉴。
ABSTRACT:Considering the great impact of fuel price increase and global warming trend, a growing demand on energy conservation and reduction of carbon dioxide emissions have been put forward world widely. The automotive industry has also been pressing to produce automobiles with better fuel economy and lower exhaust emissions. Compared with conventional vehicles, hybrid electric vehicles (HEV) are cleaner and more efficient. Based on the comprehensive studies of the domestic and foreign achievements, hybrid transmission systems are analyzed in this thesis.
In the first place, a dynamic HEV model is derived. Considering the wide existence of nonlinear components in the transmission system, a linear-in-the-parameters model identification algorithm is derived, based on the net contribution criteria, so that both the structure and parameters can be obtained simultaneously and recursively. Simulation resultscomfirm its efficacy.
According to the lever equivalent analog approach, a unified bond graph based analysis method is proposed and used for both the kinematics and dynamics modeling of a two-mode hybrid transmission system. Considering the unknown parameters of most components, a data based parameter identification algorithm is proposed, which usethe genetic algorithm (GA) and bond graph analysis results.
Based on the analysisoffuzzy control and output feedback control algorithm, a fuzzy state feedback controller is presented and applied to the stabilization of a time-delay nonlinear system and tracking control. Compared with the conventional state feedback controller, it is easier for the proposed algorithm to select the controller parameters and with better robustness.Transformation of power withindifferent types, which is called "power circulation", happens frequently and influences the transmission efficiency greatly. To address this issue, a transmission speed ratio based control strategy is researched. Considering the efficiency of both engine and transmission system, the engine operation points for instantaneous optimal fuel economy is derived. The efficiency of the transmission system and vehicle fuel economy are significantly improved.
To improve the mode switch and gear shifting impact of a hybrid vehicle equipped with an AMT, an optimal engine and clutch reference speeds generating algorithm is proposed. Gear shifting duration weight is used to balance the performance of gear shifting duration and smoothness. Numerical simulation results indicate that the balanced shifting performance can be achieved through using decoupled tracking control of the optimal reference speeds during the clutch engagement process.
The nonlinear characteristic of the clutch and its driven systems determin that it is hard to get satisfied results with conventional control approaches. Therefore, a nonlinear optimal control algorithm is researched. By solving the state dependent Riccati equation (SDRE) each step, an optimal control law is derived. Hence, a well clutch displacement during the work condition switch and gear shifting process is guaranteed.Optimal cost function index matrices Q and R are usually obtained by empirical methods. Based on the concept of output covariance constraint, an optimal cost function index matrix calculation method is researched so that the stability and robustness of the optimal controller can be guaranteed simultaneously.
A method that iteratively solving the SDRE online based on matrix vectorization and Newton gradient is derived. Considering the continuous of the system states, iterative results of each step are taken as the initial condition for the next step of SDRE solving. Compared with the conventional method, the computation load is dramaticlly reduced.
In order to accelerate the clutch engagement process and also reduce the transmission output torque ripple, a coupled dual sliding mode control strategy is proposed. The two portion of the controller coordinate with each other and the torque change during the transition process are compensated by the motor. The controller can not only guarantee the fast mode switch, but also reduce the output torque vibration simultaneously.
To sum up, through the research conducted in this thesis, further understanding of the key technologies of hybrid transmission system analysis can be achieved. The proposed analysis methods have strong adaptability and can provide reference for similar researches.
引文
[1]李嘉,王立敏.开放的竞争市场维系能源供需均衡-《BP世界能源统计2012》解析[J].国际石油经济.2012,20(7)：36-41.
[2]B. Petroleum. BP statistical review of world energy[M], BP,2012.
[3]欧阳明高.我国节能与新能源汽车发展战略与对策[J].汽车工程.2006,28(4)：317-321.
[4]佳琳.2011年底全国机动车保有量达2.25亿辆[J].商用汽车.2012(2)：41-41.
[5]何志生,左曙光,潘良明.新能源汽车技术发展现状和趋势[J].汽车研究与开发.2005,6：27-30.
[6]贾翔.权威解读《节能与新能源汽车产业发展规划(2012-2020)》[J].汽车纵横.2012(8)：14-27.
[7]N. J. Schouten, M. A. Salman, N. A. Kheir. Energy management strategies for parallel hybrid vehicles using fuzzy logic[J]. Control Engineering Practice.2003,11(2):171-177.
[8]陈清泉.电动汽车,混合动力汽车和燃料电池汽车的发展前景[J].汽车安全与节能学报.2011,2(1)：12-24.
[9]T. A. Burress, S. L. Campbell, C. Coomcr, et al. Evaluation of the 2010 Toyota Prius hybrid synergy drive system[R]. Oak Ridge National Laboratory (ORNL) Power Electronics and Electric Machinery Research Facility,2011.
[10]张越.新能源汽车发展现状及未来展望[J].汽车工业研究.2013,2：39-41.
[11]董翔宇,陈慧岩,李春芾.重型商用混合动力车自动变速技术综述[J].机械传动.2010,34(12)：75-79.
[12]M. News. John Deere answers call to utilize hybrid vehicles[EB/OL]. Available: http://www.machinefinder.com/ww/en-US/articles/john-deere-answers-call-to-utilize-hybrid-v ehicles-1602.2011.
[13]A. Van Der Heijden, A. Scrrarcns, M. Camlibel, et al. Hybrid optimal control of dry clutch engagement[J]. International Journal of Control.2007,80(11):1717-1728.
[14]L. Hyeoun-Dong, S. Seung-Ki, C. Han-Sang, et al. Advanced gear-shifting and clutching strategy for a parallel-hybrid vehicle[J]. IEEE Industry Applications Magazine.2000,6(6): 26-32.
[15]Z. Sun, H. Kumar. Challenges and opportunities in automotive transmission control[C]. Proceedings of the 2005 American Control Conference (ACC), USA,2005:3284-3289.
[16]N. Kim., J. Kim., H. Kim. Control strategy for a dual-mode electromechanical, infinitely variable transmission for hybrid electric vehicles[J]. Proceedings of the Institution of Mechanical Engineers, Part D:Journal of Automobile Engineering.2008,222(9):1587-1601.
[17]J. Liu, H. Peng. Control optimization for a power-split hybrid vehicle[C]. Proceedings of the 2006 American Control Conference (ACC), USA,2006:466-471.
[18]B. Conlon. Comparative analysis of single and combined hybrid electrically variable transmission operating modes[J]. SAE Transactions.2005,114(6):1265-1275.
[19]金涛涛.综合传动装置换挡品质优化研究[D].北京：北京交通大学,2008.
[20]M. Schulz. Circulating mechanical power in a power-split hybrid electric vehicle transmission[J]. Proceedings of the Institution of Mechanical Engineers, Part D:Journal of Automobile Engineering.2004,218(12):1419-1425.
[21]何忠波,梁宪福,韩正军等.AMT换挡过程动力学建模及换挡品质影响因素分析[J].军械工程学院学报.2008,16(6)：45-49.
[22]J. M. Miller. Power electronics in hybrid electric vehicle applications[C].2003 Applied Power Electronics Conference and Exposition (APEC),2003:23-29.
[23]W. JongSeob, R. Langari, M. Ehsani. An energy management and charge sustaining strategy for a parallel hybrid vehicle with CVT[J]. IEEE Transactions on Control Systems Technology. 2005,13(2):313-320.
[24]K. T. Chau, Y. S. Wong. Overview of power management in hybrid electric vehicles[J]. Energy Conversion and Management.2002,43(15):1953-1968.
[25]F. R. Salmasi. Control strategies for hybrid electric vehicles:evolution, classification, comparison, and future trends[J]. IEEE Transactions on Vehicular Technology.2007,56(5): 2393-2404.
[26]何云信,谢文尚.汽车混合动力技术发展现状及前景[J].装备制造技术.2010,2：133-134.
[27]M. Salman, N. J. Schouten, N. A. Kheir. Control strategies for parallel hybrid vehicles[C]. Proceedings of the 2000 American Control Conference (ACC), USA,2000:524-528.
[28]M. Sivertsson, U. Pettersson, A. Andersson, et al. Optimization of fuel consumption in a hybrid powertrain[J]. SAE Transactions.2001,111(6):1572-1579.
[29]UNPE. Hybrid electric vehicles:An overview of current technology and its application in developing and transitional countries [R]. United Nations Environment Programme, United Nations,,2009.
[30]E. Rask, M. Duoba, H. Lohse-Busch. Recent hybrid electric vehicle trends and technologies [C].2011 IEEE Conference on Vehicle Power and Propulsion Conference (VPPC), USA,2011:1-6.
[31]N. Kim., J. Kwon., A. Rousseau. Trade-off between multi-mode powertrain complexity and fuel consumption[C]. Proceedings of the 25th World Battery, Hybrid and Fuel Cell Electric Vehicle Symposium & Exhibition, China,2010:1-11.
[32]L. Situ. Electric Vehicle development:The past, present & future[C].3rd International Conference on Power Electronics Systems and Applications, Hong Kong,2009:1-3.
[33]L. B. Lave, H. L. MacLean. An environmental-economic evaluation of hybrid electric vehicles:Toyota's Prius vs. its conventional internal combustion engine Corolla[J]. Transportation Research Part D:Transport and Environment.2002,7(2):155-162.
[34]K. Fukuo, A. Fujimura, M. Saito, et al. Development of the ultra-low-fuel-consumption hybrid car-INSIGHT[J]. JSAE Review.2001,22(1):95-103.
[35]张卫青.混合动力汽车的发展现状及其关键技术[J].重庆工学院学报.2006,20(5)：19-22.
[36]过学迅,张杰山,胡朝峰.日美混合动力汽车发展的比较研究[J].上海汽车.2006,3：7-10.
[37]段牧染,赵洁.车辆混合动力传动技术发展现状与展望[J].内燃机与配件.2011,8：27-30.
[38]G. C. Congress. Toyota cumulative global hybrid sales pass 5M, nearly 2M in US[EB/OL]. Available:http://www.greencarcongress.com/2013/04/tmc-20130417.html.2013.
[39]曾鹏.我国新能源汽车发展现状及问题[J].上海汽车.2009,8：5-7.
[40]邢向龙.混合动力城市客车关键技术的发展[J].汽车与配件.2011,30(7)：37-39.
[41]李超,宁国宝,王健.油-电混合动力客车动力系统平台的国内研究现状[J].北京汽车.2009,1：34-38.
[42]杜建福.国内混合动力客车发展现状及分析[J].商用汽车.2010,6：60-61.
[43]P. C. Breedveld. Modeling and simulation of dynamic systems using bond graphs[J]. Control Systems, Robotics and Automation.2008:1-36.
[44]R. Zanasi, A. Visconti, G. Sandoni, et al. Dynamic modeling and control of a car transmission system[C]. Proceedings of the 2001 IEEE/ASME International Conference on Advanced Intelligent Mechatronics,2001:416-421.
[45]K. Chen, B. Alain, B. Alain, et al. Global modeling of different vehicles[J]. Vehicular Technology Magazine.2009,4(2):80-89.
[46]H. L. Benford, M. B. Leising. The lever analogy:A new tool in transmission analysis[M], Society of Automotive Engineers,1981.
[47]H. K. Khalil. Nonlinear systems[M], Prentice hall Upper Saddle River,2002.
[48]G. Brouwn, A. Krijgsman, H. Verbruggen, et al. Single-layer networks for nonlinear system identification[J]. Engineering Applications of Artificial Intelligence.1994,7(3):227-243.
[49]S. Chen, C. F. N. Cowan, P. M. Grant. Orthogonal least squares learning algorithm for radial basis function networks[J]. IEEE Transactions on Neural Networks.1991,2(2):302-309.
[50]G. Huang, P. Saratchandran, N. Sundararajan. A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation[J]. IEEE Transactions on Neural Networks.2005,16(1):57-67.
[51]K. Li, J. Peng, E. Bai. A two-stage algorithm for identification of nonlinear dynamic systems[J]. Aulomatica.2006,42(7):1189-1197.
[52]J. Peng, K. Li, D. Huang. A hybrid forward algorithm for RBF neural network construction[J]. IEEE Transactions on Neural Networks.2006,17(6):1439-1451.
[53]D. Huang. The united adaptive learning algorithm for the link weights and shape parameter in RBFN for pattern recognition[J]. International Journal of Pattern Recognition and Artificial Intelligence.1997,11(6):873-888.
[54]X. Hong, C. J. Harris, S. Chen, et al. Robust nonlinear model identification methods using forward regression[J]. IEEE Transactions on Systems, Man and Cybernetics, Part A:Systems and Humans.2003,33(4):514-523.
[55]R. Habcr, H. Unbchaucn. Structure identification of nonlinear dynamic systems-a survey on input/output approaches[J]. Automatica.1990,26(4):651-677.
[56]S. Chen, S. A. Billings, W. Luo. Orthogonal least squares methods and their application to non-linear system identification[J]. International Journal of Control.1989,50(5):1873-1896.
[57]K. Li, J. Peng, G. W. Irwin. A fast nonlinear model identification method[J]. IEEE Transactions on Automatic Control.2005,50(8):1211-1216.
[58]Q. Zhu, S. Billings. Fast orthogonal identification of nonlinear stochastic models and radial basis function neural networks[J]. International Journal of Control.1996,64(5):871-886.
[59]S. K. Nguang. Robust stabilization of a class of time-delay nonlinear systems[J]. IEEE Transactions on Automatic Control.2000,45(4):756-762.
[60]Q. Wang, Y. Jing, S. Zhang. Adaptive output tracking control of a class of nonlinear systems[C]. Proceedings of the 5th World Congress on Intelligent Control and Automation (WCICA 2004),2004:876-880.
[61]徐兆棣,张嗣瀛.不确定非线性时滞系统的鲁棒可靠控制[J].控制与决策.2001,16(4)：452-456.
[62]L. Keviczky, C. Banyasz. Robust stability and performance of time-delay control systems[J]. ISA transactions.2007,46(2):233-237.
[63]伏玉笋,田作华,施颂椒.非线性时滞系统输出反馈镇定[J].自动化学报.2002,28(5)：802-805.
[64]陈为胜,李俊民.一类非线性时滞输出反馈系统的自适应控制[J].控制理论与应用.2004,21(5)：844-847.
[65]任勤,宋林锋.一类非线性时滞系统通过自适应Backstepping的控制[J].太原理工大学学报.2006,37(6)：721-724.
[66]杨军,张兴照,陈为胜.随机时滞非线性系统状态反馈镇定[J].科技通报.2007,23(6)：885-890.
[67]F. U. Syed, M. L. Kuang, J. Czubay, et al. Derivation and experimental validation of a power-split hybrid electric vehicle model[J]. IEEE Transactions on Vehicular Technology. 2006,55(6):1731-1747.
[68]Z. Rahman, K. L. Butler, M. Ehsani. A comparison study between two parallel hybrid control concepts[M], SAE,2000.
[69]J. Gao, G. G. Zhu, E. G. Strangas, et al. Equivalent fuel consumption optimal control of a series hybrid electric vehicle[J]. Proceedings of the Institution of Mechanical Engineers, Part D:Journal of Automobile Engineering.2009,223(8):1003-1018.
[70]C. C. Lin, H. Peng, J. W. Grizzle, et al. Power management strategy for a parallel hybrid electric truck[J]. IEEE Transactions on Control Systems Technology.2003,11(6):839-849.
[71]L. V Perez, G. R. Bossio, D. Moitre, et al. Optimization of power management in an hybrid electric vehicle using dynamic programming[J]. Mathematics and Computers in Simulation. 2006,73(1):244-254.
[72]C.-C. Lin, H. Peng, J. W. Grizzle. A stochastic control strategy for hybrid electric vehicles[C]. Proceedings of the 2004 American Control Conference (ACC), USA,2004:4710-4715.
[73]J. Scordia, R. M. Desbois, R. Trigui, et al. Global optimisation of energy management laws in hybrid vehicles using dynamic programming[J]. International journal of vehicle design.2005, 39(4):349-367.
[74]于秀敏,曹珊,李君等.混合动力汽车控制策略的研究现状及其发展趋势[J].机械工程学报.2006,42(11)：10-16.
[75]I. Arsie, M. Graziosi, C. Pianese, et al. Optimization of supervisory control strategy for parallel hybrid vehicle with provisional load estimate[C]. Proceeding of AVEC04,2004: 23-27.
[76]A. Poursamad, M. Montazeri. Design of genetic-fuzzy control strategy for parallel hybrid electric vehicles[J]. Control Engineering Practice.2008,16(7):861-873.
[77]Y. Zhang, H. Liu. Fuzzy logic torque control strategy for parallel hybrid electric vehicles[C]. Proceedings of the 8th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), China,2011:640-644.
[78]崔星,项昌乐.混合动力系统分流耦合机构工作模式分析[J].农业工程学报.2009,25(11)：158-163.
[79]J. M. Miller. Hybrid electric vehicle propulsion system architectures of the e-CVT type[J]. IEEE Transactions on Power Electronics.2006,21(3):756-767.
[80]黄英,万国强,崔涛等.AMT换挡过程发动机控制策略研究[J].汽车工程.2012,34(3)：245-248.
[81]余天明,郑磊,李颂.电控机械式自动变速器离合器灰色预测PID控制技术[J].农业机械学报.2011,42(8)：1-6.
[82]黄智明,张建武,鲁统利等.基于DMC-PID串级控制算法的AMT离合器接合位置控制[J].汽车工程.2010,32(2)：133-136.
[83]B. Gao, H. Chen, K. Sanada. Clutch slip control of automatic transmission using backstepping technique[C].2009 ICROS-SICE International Joint Conference, Fukuoka, Japan,2009: 3015-3019.
[84]A. Rachid, M. Alamir, P. Tona. Unified MPC strategy for idle-speed control, vehicle start-up and gearing applied to an Automated Manual Transmission[C]. Proceedings of the 17th World Congress of the International Federation of Automatic Control (IFAC), South Korea,2008.
[85]G. Qin, A. Ge, J.-J. Lee. Knowledge-based gear-position decision[J]. IEEE Transactions on Intelligent Transportation Systems.2004,5(2):121-125.
[86]J. Qu, Y. Zhang. Control of clutch engagement for AMT based on Fuzzy Logic[C]. Proceeding of the 2010 International Symposium on Information Science and Engineering, China,2010:47-50.
[87]S. Sakaguchi, I. Sakai, T. Haga. Application of fuzzy logic to shift scheduling method for automatic transmission[C]. The 2nd IEEE International Conference on Fuzzy Systems,1993: 52-58.
[88]杨春兰,薛大为,程荣龙.机械式自动变速系统起步过程离合器智能控制[J].机械工程师.2008,10：33-35.
[89]陈清洪,秦大同.动变速汽车神经网络三参数换挡策略[J].控制理论与应用.2010,27(11)：1580-1584.
[90]陈清洪,秦大同,叶心.AMT重型汽车起步离合器最优控制[J].中国公路学报.2010,23(1)：116-121.
[91]高智,钟再敏,孙泽昌.电动AMT选换挡电机执行机构位置最优控制[J].汽车工程.2011,33(2)：133-137.
[92]H. Hu, X. Wang, Y. Shao. Optimization of the shift quality of dual clutch transmission using genetic algorithm[C]. Proceeding of the 6th International Conference on Natural Computation, China,2010:4152-4156.
[93]Y. Murphey. Intelligent vehicle power management:An overview[M], Springer Berlin Heidelberg,2008.
[94]G. Lucentc, M. Montanari, C. Rossi. Modelling of an automated manual transmission system[J]. Mechatronics.2007,17(2):73-91.
[95]J. Pu, C. Yin. Optimal control of fuel economy in parallel hybrid electric vehicles [J]. Proceedings of the Institution of Mechanical Engineers, Part D:Journal of Automobile Engineering.2007,221(9):1097-1106.
[96]C.-C. Lin, J.-M. Kang, J. W. Grizzle, et al. Energy management strategy for a parallel hybrid electric truck[C]. Proceedings of the 2001 American Control Conference (ACC), USA,2001: 2878-2883.
[97]A. Sciarretta, M. Back, L. Guzzella. Optimal control of parallel hybrid electric vehicles[J]. IEEE Transactions on Control Systems Technology.2004,12(3):352-363.
[98]A. Rajagopalan, G. Washington, G. Rizzoni, et al. Development of fuzzy logic and neural network control and advanced emissions modeling for parallel hybrid vehicles[R]. National Renewable Energy Laboratory,2003.
[99]B. K. Powell, K. E. Bailey, S. R. Cikanek. Dynamic modeling and control of hybrid electric vehicle powertrain systems[J]. IEEE Transactions on Control Systems.1998,18(5):17-33.
[100]H. K. Khalil. Nonlinear systems (Third Edition)[M], Prentice hall Englewood Cliffs, NJ, 2002.
[101]吴受章.最优控制理论与应用[M],机械工业出版社,2008.
[102]M. Athans, P. L. Falb. Optimal control:an introduction to the theory and its applications[M], Dover Publications New York,2007.
[103]J. R. Cloutier, C. N. D'Souza, C. P. Mracek. Nonlinear regulation and nonlinear H-infinity control via the state-dependent Riccati equation technique. I-Theory[C].1st International Conference on Nonlinear Problems in Aviation and Aerospace, USA,1996:117-130.
[104]杨帆,赵党军,李国平等.基于SDRE方法的一级旋转倒立摆控制[J].华中科技大学学报(自然科学版).2008,36(10)：25-28.
[105]T. Cimen. State-dependent Riccati equation (SDRE) control:a survey[C]. Proceedings of the 17th World Congress of the International Federation of Automatic Control (IFAC), South Korea,2008:3761-3775.
[106]J. R. Cloutier. State-dependent Riccati equation techniques:an overview[C]. Proceedings of the 1997 American Control Conference (ACC), USA,1997:932-936.
[107]T. Cimen, S. P. Banks. Global optimal feedback control for general nonlinear systems with nonquadratic performance criteria[J]. Systems & Control Letters.2004,53(5):327-346.
[108]张军,徐世杰.基于SDRE方法的挠性航天器姿态控制[J].宇航学报.2008,29(1)：138-144.
[109]L. Wilbur, A. Andrew. A stability result with application to nonlinear regulation[J]. Journal of Dynamic Systems, Measurement, and Control.2002,124(3):452-456.
[110]P. Menon, V. Iragavarapu, G. Sweriduk, et al. Software tools for nonlinear missile autopilot design[R]. DTIC Document,1999.
[111]M. Xin, H. Pan. Nonlinear optimal control of spacecraft approaching a tumbling target[J]. Aerospace Science and Technology.2011,15(2):79-89.
[112]D. K. Parrish, D. B. Ridgely. Control of an artificial human pancreas using the SDRE method[C]. Proceedings of the 1997 American Control Conference (ACC),1997:1059-1060.
[113]W. Luo, Y.-C. Chu. Attitude control using the SDRE technique[C]. Proceedings of the 7th International Conference on Control, Automation, Robotics and Vision,2002:1281-1286.
[114]P. Shankar, R. K. Yedavalli, D. B. Doman. Dynamic inversion via state dependent Riccati equation approach:application to flight vehicles[C].2003 AIAA Guidance, Navigation, and Control Conference and Exhibit,2003:1-9.
[115]P. Shankar, R. K. Yedavalli, D. B. Doman. Stability domain estimation for dynamic inversion embedded SDRE flight controller[C].2004 AIAA Guidance, Navigation, and Control Conference and Exhibit, USA,2004:1-11.
[116]A. A. Bogdanov, E. A. Bogdanov, E. A. Wan, et al. Model predictive neural control of a high fidelity helicopter model[C]. Proceedings of the 2001 AIAA Guidance Navigation and Control Conference, Montreal, Canada,2001:1-9.
[117]E. A. Wan, A. A. Bogdanov. Model predictive neural control with applications to a 6 DOF helicopter model[C]. Proceedings of the 2001 American Control Conference (ACC), USA, 2001:488-493.
[118]陈谋,姜长生,吴庆宪等.一类串级不确定非线性系统的最优滑模控制器设计[J].系统工程理论与实践.2003,11：31-35.
[119]J. Shen, S. Balakrishnan. A class of modified Hopfield networks for control of linear and nonlinear systems[C]. Proceedings of the 1998 American Control Conference (ACC),1998: 964-969.
[120]A. Yoon, P. Khargonekar, K. Hebbale. Randomized algorithms for open-loop control of clutch-to-clutch transmissions[J]. Journal of dynamic systems, measurement, and control. 1999,121(3):508-517.
[121]A. Zhang, L. Zhang. RBF neural networks for the prediction of building interference effects[J]. Computers & Structures.2004,82(27):2333-2339.
[122]S. Haykin. Neural networks:a comprehensive foundation[J]. Prentice Hall, sa.2008.
[123]D. Gao, Y. Ji, C. Li. Frequency domain analysis based RBF networks and their applications to function approximations[J]. Proceeding of 2004 IEEE International Joint Conference on Neural Networks, Budapest,2004:2111-2116.
[124]Y. Ma, L. Ma, J. Ma. RBF neural network based fault diagnosis for the thermodynamic system of a thermal power generating unit[C]. Proceedings of 2005 International Conference on Machine Learning and Cybernetics, China,2005:4738-4743.
[125]T. Jin, P. Li. Application of neural networks in modelling of the transmission hydraulic actuator[J]. International Journal of Modelling, Identification and Control.2009,8(2): 148-157.
[126]牛铭奎,葛安林.高速开关电磁阀的特性应用研究[J].汽车技术.1999,7：13-16.
[127]陈丽君.电控机械式自动变速器液压换挡执行机构的研究[D].重庆：重庆大学,2011.
[128]张敏敏.自动变速器液压控制系统建模与模型简化[D].上海：上海交通大学,2010.
[129]刘少军,夏毅敏.高速开关电磁阀的PWM控制及改进技术[J].机床与液压.1998(4)：52-53.
[130]F. M. White. Fluid Mechanics[M], McGraw-Hill Companies, Inc.,2011.
[131]L. Ljung. System identification:theory for the user[M], Pearson Education,1998.
[132]P. Li, T. Jin, X. Du. Simple recursive algorithm for linear-in-thcparamctcrs nonlinear model identification[J]. Science in China Series F:Information Sciences.2009,52(10):1739-1745.
[133]T. Markel, A. Brooker, T. Hendricks, et al. ADVISOR:a systems analysis tool for advanced vehicle modeling[J]. Journal of Power Sources.2002,110(2):255-266.
[134]T. Jin, P. Li. Kinematics and dynamics modeling of the planet transmission system[C]. Proceeding of the 8th International Symposium on Test and Measurement, China,2009: 2234-2237.
[135]D. C. Karnopp, D. L. Margolis, R. C. Rosenberg. System dynamics:modeling, simulation, and control of mechatronic systems[M], Wiley, com,2012.
[136]J. Deur, M. Cipek, J. Petric. Bond graph modeling of series-parallel hybrid electric vehicle power train dynamics[C]. Proceedings of the 2010 Spring Simulation Multiconference, USA, 2010:1-10.
[137]M. R. Schmidt. Two-mode, compound-split electro-mechanical vehicular transmission[P]. Google Patents,1999.
[138]T. H. Grewe. Defining the General Motors 2-mode hybrid transmission[J]. SAE Technical Paper 2007-01-0273.2007.
[139]G. Filippini, N. Nigro, S. Junco. Vehicle dynamics simulation using bond graphs[J]. International Modeling and Simulation Multiconference.2007.
[140]P. Li, T. Jin, X. Du. MIMO CCS model identification based on GA for closed-loop field data[J]. International Journal of Modelling, Identification and Control.2011,13(1):30-37.
[141]P. Li, X. Du, Y. Liu. AGC parameters optimization using real coded genetic algorithm[C]. Power System Technology, Proceeding of the 2002 International Conference on PowerControl, 2002:646-650.
[142]M. Montazeri-Gh, A. Poursamad, B. Ghalichi. Application of genetic algorithm for optimization of control strategy in parallel hybrid electric vehicles[J]. Journal of the Franklin Institute.2006,343(4-5):420-435.
[143]W. Chang. An improved real-coded genetic algorithm for parameters estimation of nonlinear systems[J]. Mechanical Systems and Signal Processing.2006,20(1):236-246.
[144]T. Jin, P. Li, C. Chen, et al. Fuzzy state feedback stabilization for the time-delay nonlinear system[C]. Proceedings of the 29th Chinese Control Conference (CCC), China,2010: 488-491.
[145]O. Barbarisi, E. R. Westervelt, F. Vasca, et al. Power management decoupling control for a hybrid electric vehicle[C].44th IEEE Conference on Decision and Control and 2005 European Control Conference (CDC-ECC'05), Spain,2005:2012-2017.
[146]H. Yang, A. Smith, S. Swales, et al. Development of two-mode hybrid powertrain with enhanced EV capability[J]. SAE International Journal of Engines.2011,4(1):1058-1070.
[147]G. Paganelli, T. Guerra, S. Delprat, et al. Simulation and assessment of power control strategies for a parallel hybrid car[J]. Proceedings of the Institution of Mechanical Engineers, Part D:Journal of Automobile Engineering.2000,214(7):705-717.
[148]M. Duoba, H. Lohse-Busch, R. Carlson, et al. Analysis of power-split HEV control strategies using data from several vehicles[J]. SAE paper.2007:2007-01-0291.
[149]K. Bailey, S. Cikanek, N. Sureshbabu. Parallel hybrid electric vehicle torque distribution method[C]. Proceedings of the 15th World Congress of the International Federation of Automatic Control (IFAC), USA,2002:3708-3712.
[150]J. D. Wishart, Y. Zhou, Z. Dong. Review, modelling and simulation of two-mode hybrid vehicle architecture[C]. Proceedings of ASME 2007 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC/CIE 2007), USA,2007:1091-1112.
[151]B. Conlon. Comparative analysis of single and combined hybrid electrically variable transmission operating modes[J]. SAE paper.2005:2005-01-1162.
[152]叶明.机械自动变速传动建模与仿真及试验软件开发[D].重庆：重庆大学,2003.
[153]F. Garofalo, L. Glielmo, L. Iannelli, et al. Smooth engagement for automotive dry clutch[C]. Proceedings of the 40th IEEE Conference on Decision and Control,2001:529-534.
[154]A. Serrarens, M. Dassen, M. Steinbuch. Simulation and control of an automotive dry clutch[C]. Proceedings of the 2004 American Control Conference (ACC), USA,2004: 4078-4083.
[155]D. Karnopp. Computer simulation of stick-slip friction in mechanical dynamic systems[J]. Journal of Dynamic Systems, Measurement, and Control.1985,107(1):100-103.
[156]曹桂军,葛安林,郑磊等.电控机械式自动变速器换挡过程中离合器的接合控制[J].机械工程学报.2005,41(12)：234-238.
[157]L. Zhao, T. Liu, B. Song. Optimum design of automobile diaphragm spring clutch[C]. Proceedings of the 2008 Vehicle Power and Propulsion Conference (VPPC), China,2008:1-4.
[158]何仁,刘文光,黄大星.不同道路负荷的AMT车辆起步过程离合器控制策略[J].江苏大学学报(自然科学版).2009,30(5)：459-462.
[159]郭立书,葛安林.电控机械式自动变速器换挡过程控制[J].农业机械学报.2003,34(2)：l-3.
[160]赵永胜,刘志峰,杨文通等.基于dSPACE的汽车自动离合器快速控制原型试验[J].农业机械学报.2009,40(9)：12-15.
[161]L. Glielmo, L. Iannelli, V. Vacca, et al. Speed control for automated manual transmission with dry clutch[C]. Proceeding of the 43rd IEEE Conference on Decision and Control,2004: 1709-1714.
[162]L. Glielmo, L. Iannelli, V. Vacca, et al. Gearshift control for automated manual transmissions[J]. IEEE/ASME Transactions on Mechatronics.2006,11(1):17-26.
[163]P. Dolcini, C. C. de Wit, H. Bechart. Improved optimal control of dry clutch engagcmcnt[C]. Proceedings of the 16th World Congress of the International Federation of Automatic Control (IFAC), Czech,2005.
[164]J. Zhang, C. Li, G. Xi. System dynamic modelling and adaptive optimal control for automatic clutch engagement of vehicles[J]. Proceedings of the Institution of Mechanical Engineers, Part D:Journal of Automobile Engineering.2002,216(12):983-991.
[165]T. Jin, P. Li, Z. Jia. Optimal tracking design for dry clutch engagement[C]. Proceedings of 2013 Chinese Intelligent Automation Conference, China,2013:685-692.
[166]王庆年,冀尔聪,王伟华.并联混合动力汽车模式切换过程的协调控制[J].吉林大学学报(工学版).2008,38(1)：1-6.
[167]T. Jin, P. Li, G. Zhu. Optimal decoupled control for dry clutch engagement[C]. Proceedings of the 2013 American Control Conference (ACC), USA,2013:6740-6745.
[168]J. Pittner, M. A. Simaan. Optimum feedback controller design for tandem cold metal rolling[C]. Proceedings of the 17th World Congress of the International Federation of Automatic Control (IFAC), South Korea,2008:988-993.
[169]C. P. Mracek, J. R. Cloutier. Control designs for the nonlinear benchmark problem via the state-dependent Riccati equation method[J]. International journal of robust and nonlinear control.1998,8(4-5):401-433.
[170]D. Kleinman. Stabilizing a discrete, constant, linear system with application to iterative methods for solving the Riccati equation[J]. IEEE Transactions on Automatic Control.1974, 19(3):252-254.
[171]C. G. Broyden. A class of methods for solving nonlinear simultaneous equations[J]. Mathematics of computation.1965,19(92):577-593.
[172]P. P. Khargonekar, M. A. Rotea. Multiple objective optimal control of linear systems:the quadratic norm case[J]. IEEE Transactions on Automatic Control.1991,36(1):14-24.
[173]G. Zhu, M. Rotea, R. Skelton. A convergent algorithm for the output covariance constraint control problem[J]. SIAM Journal on Control and Optimization.1997,35(1):341-361.
[174]G. Zhu, K. M. Grigoriadis, R. E. Skelton. Covariance control design for hubble space telescope[J]. Journal of Guidance, Control, and Dynamics.1995,18(2):230-236.
[175]夏长亮,刘均华,俞卫等.基于扩张状态观测器的永磁无刷直流电机滑模变结构控制[J].中国电机工程学报.2006,26(20)：139-143.
[176]苗中华,王旭永,刘成良等.基于滑模变结构控制的液压伺服系统超低速轨迹跟踪[J].上海交通大学学报.2008,42(7)：1182-1186.
[177]蔡国梁,周维怀,郑松等.不确定离散系统全程滑模变结构控制[J].江苏大学学报(自然科学版).2010,31(3)：363-368.
[178]刘金琨,孙富春.滑模变结构控制理论及其算法研究与进展[J].控制理论与应用.2007,24(3)：407-418.
[179]刘金琨.滑模变结构控制MATLAB仿真[M],清华大学出版社,2005.
[180]P. Li, T. Jin, X. Du. Torque observer modelling for vehicle transmission shifting processing based on Neural Networks[C]. Proceedings of the 17th World Congress of the International Federation of Automatic Control (IFAC), South Korea,2008:16039-16044.
[2]B. Petroleum. BP statistical review of world energy[M], BP,2012.
[3]欧阳明高.我国节能与新能源汽车发展战略与对策[J].汽车工程.2006,28(4)：317-321.
[4]佳琳.2011年底全国机动车保有量达2.25亿辆[J].商用汽车.2012(2)：41-41.
[5]何志生,左曙光,潘良明.新能源汽车技术发展现状和趋势[J].汽车研究与开发.2005,6：27-30.
[6]贾翔.权威解读《节能与新能源汽车产业发展规划(2012-2020)》[J].汽车纵横.2012(8)：14-27.
[7]N. J. Schouten, M. A. Salman, N. A. Kheir. Energy management strategies for parallel hybrid vehicles using fuzzy logic[J]. Control Engineering Practice.2003,11(2):171-177.
[8]陈清泉.电动汽车,混合动力汽车和燃料电池汽车的发展前景[J].汽车安全与节能学报.2011,2(1)：12-24.
[9]T. A. Burress, S. L. Campbell, C. Coomcr, et al. Evaluation of the 2010 Toyota Prius hybrid synergy drive system[R]. Oak Ridge National Laboratory (ORNL) Power Electronics and Electric Machinery Research Facility,2011.
[10]张越.新能源汽车发展现状及未来展望[J].汽车工业研究.2013,2：39-41.
[11]董翔宇,陈慧岩,李春芾.重型商用混合动力车自动变速技术综述[J].机械传动.2010,34(12)：75-79.
[12]M. News. John Deere answers call to utilize hybrid vehicles[EB/OL]. Available: http://www.machinefinder.com/ww/en-US/articles/john-deere-answers-call-to-utilize-hybrid-v ehicles-1602.2011.
[13]A. Van Der Heijden, A. Scrrarcns, M. Camlibel, et al. Hybrid optimal control of dry clutch engagement[J]. International Journal of Control.2007,80(11):1717-1728.
[14]L. Hyeoun-Dong, S. Seung-Ki, C. Han-Sang, et al. Advanced gear-shifting and clutching strategy for a parallel-hybrid vehicle[J]. IEEE Industry Applications Magazine.2000,6(6): 26-32.
[15]Z. Sun, H. Kumar. Challenges and opportunities in automotive transmission control[C]. Proceedings of the 2005 American Control Conference (ACC), USA,2005:3284-3289.
[16]N. Kim., J. Kim., H. Kim. Control strategy for a dual-mode electromechanical, infinitely variable transmission for hybrid electric vehicles[J]. Proceedings of the Institution of Mechanical Engineers, Part D:Journal of Automobile Engineering.2008,222(9):1587-1601.
[17]J. Liu, H. Peng. Control optimization for a power-split hybrid vehicle[C]. Proceedings of the 2006 American Control Conference (ACC), USA,2006:466-471.
[18]B. Conlon. Comparative analysis of single and combined hybrid electrically variable transmission operating modes[J]. SAE Transactions.2005,114(6):1265-1275.
[19]金涛涛.综合传动装置换挡品质优化研究[D].北京：北京交通大学,2008.
[20]M. Schulz. Circulating mechanical power in a power-split hybrid electric vehicle transmission[J]. Proceedings of the Institution of Mechanical Engineers, Part D:Journal of Automobile Engineering.2004,218(12):1419-1425.
[21]何忠波,梁宪福,韩正军等.AMT换挡过程动力学建模及换挡品质影响因素分析[J].军械工程学院学报.2008,16(6)：45-49.
[22]J. M. Miller. Power electronics in hybrid electric vehicle applications[C].2003 Applied Power Electronics Conference and Exposition (APEC),2003:23-29.
[23]W. JongSeob, R. Langari, M. Ehsani. An energy management and charge sustaining strategy for a parallel hybrid vehicle with CVT[J]. IEEE Transactions on Control Systems Technology. 2005,13(2):313-320.
[24]K. T. Chau, Y. S. Wong. Overview of power management in hybrid electric vehicles[J]. Energy Conversion and Management.2002,43(15):1953-1968.
[25]F. R. Salmasi. Control strategies for hybrid electric vehicles:evolution, classification, comparison, and future trends[J]. IEEE Transactions on Vehicular Technology.2007,56(5): 2393-2404.
[26]何云信,谢文尚.汽车混合动力技术发展现状及前景[J].装备制造技术.2010,2：133-134.
[27]M. Salman, N. J. Schouten, N. A. Kheir. Control strategies for parallel hybrid vehicles[C]. Proceedings of the 2000 American Control Conference (ACC), USA,2000:524-528.
[28]M. Sivertsson, U. Pettersson, A. Andersson, et al. Optimization of fuel consumption in a hybrid powertrain[J]. SAE Transactions.2001,111(6):1572-1579.
[29]UNPE. Hybrid electric vehicles:An overview of current technology and its application in developing and transitional countries [R]. United Nations Environment Programme, United Nations,,2009.
[30]E. Rask, M. Duoba, H. Lohse-Busch. Recent hybrid electric vehicle trends and technologies [C].2011 IEEE Conference on Vehicle Power and Propulsion Conference (VPPC), USA,2011:1-6.
[31]N. Kim., J. Kwon., A. Rousseau. Trade-off between multi-mode powertrain complexity and fuel consumption[C]. Proceedings of the 25th World Battery, Hybrid and Fuel Cell Electric Vehicle Symposium & Exhibition, China,2010:1-11.
[32]L. Situ. Electric Vehicle development:The past, present & future[C].3rd International Conference on Power Electronics Systems and Applications, Hong Kong,2009:1-3.
[33]L. B. Lave, H. L. MacLean. An environmental-economic evaluation of hybrid electric vehicles:Toyota's Prius vs. its conventional internal combustion engine Corolla[J]. Transportation Research Part D:Transport and Environment.2002,7(2):155-162.
[34]K. Fukuo, A. Fujimura, M. Saito, et al. Development of the ultra-low-fuel-consumption hybrid car-INSIGHT[J]. JSAE Review.2001,22(1):95-103.
[35]张卫青.混合动力汽车的发展现状及其关键技术[J].重庆工学院学报.2006,20(5)：19-22.
[36]过学迅,张杰山,胡朝峰.日美混合动力汽车发展的比较研究[J].上海汽车.2006,3：7-10.
[37]段牧染,赵洁.车辆混合动力传动技术发展现状与展望[J].内燃机与配件.2011,8：27-30.
[38]G. C. Congress. Toyota cumulative global hybrid sales pass 5M, nearly 2M in US[EB/OL]. Available:http://www.greencarcongress.com/2013/04/tmc-20130417.html.2013.
[39]曾鹏.我国新能源汽车发展现状及问题[J].上海汽车.2009,8：5-7.
[40]邢向龙.混合动力城市客车关键技术的发展[J].汽车与配件.2011,30(7)：37-39.
[41]李超,宁国宝,王健.油-电混合动力客车动力系统平台的国内研究现状[J].北京汽车.2009,1：34-38.
[42]杜建福.国内混合动力客车发展现状及分析[J].商用汽车.2010,6：60-61.
[43]P. C. Breedveld. Modeling and simulation of dynamic systems using bond graphs[J]. Control Systems, Robotics and Automation.2008:1-36.
[44]R. Zanasi, A. Visconti, G. Sandoni, et al. Dynamic modeling and control of a car transmission system[C]. Proceedings of the 2001 IEEE/ASME International Conference on Advanced Intelligent Mechatronics,2001:416-421.
[45]K. Chen, B. Alain, B. Alain, et al. Global modeling of different vehicles[J]. Vehicular Technology Magazine.2009,4(2):80-89.
[46]H. L. Benford, M. B. Leising. The lever analogy:A new tool in transmission analysis[M], Society of Automotive Engineers,1981.
[47]H. K. Khalil. Nonlinear systems[M], Prentice hall Upper Saddle River,2002.
[48]G. Brouwn, A. Krijgsman, H. Verbruggen, et al. Single-layer networks for nonlinear system identification[J]. Engineering Applications of Artificial Intelligence.1994,7(3):227-243.
[49]S. Chen, C. F. N. Cowan, P. M. Grant. Orthogonal least squares learning algorithm for radial basis function networks[J]. IEEE Transactions on Neural Networks.1991,2(2):302-309.
[50]G. Huang, P. Saratchandran, N. Sundararajan. A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation[J]. IEEE Transactions on Neural Networks.2005,16(1):57-67.
[51]K. Li, J. Peng, E. Bai. A two-stage algorithm for identification of nonlinear dynamic systems[J]. Aulomatica.2006,42(7):1189-1197.
[52]J. Peng, K. Li, D. Huang. A hybrid forward algorithm for RBF neural network construction[J]. IEEE Transactions on Neural Networks.2006,17(6):1439-1451.
[53]D. Huang. The united adaptive learning algorithm for the link weights and shape parameter in RBFN for pattern recognition[J]. International Journal of Pattern Recognition and Artificial Intelligence.1997,11(6):873-888.
[54]X. Hong, C. J. Harris, S. Chen, et al. Robust nonlinear model identification methods using forward regression[J]. IEEE Transactions on Systems, Man and Cybernetics, Part A:Systems and Humans.2003,33(4):514-523.
[55]R. Habcr, H. Unbchaucn. Structure identification of nonlinear dynamic systems-a survey on input/output approaches[J]. Automatica.1990,26(4):651-677.
[56]S. Chen, S. A. Billings, W. Luo. Orthogonal least squares methods and their application to non-linear system identification[J]. International Journal of Control.1989,50(5):1873-1896.
[57]K. Li, J. Peng, G. W. Irwin. A fast nonlinear model identification method[J]. IEEE Transactions on Automatic Control.2005,50(8):1211-1216.
[58]Q. Zhu, S. Billings. Fast orthogonal identification of nonlinear stochastic models and radial basis function neural networks[J]. International Journal of Control.1996,64(5):871-886.
[59]S. K. Nguang. Robust stabilization of a class of time-delay nonlinear systems[J]. IEEE Transactions on Automatic Control.2000,45(4):756-762.
[60]Q. Wang, Y. Jing, S. Zhang. Adaptive output tracking control of a class of nonlinear systems[C]. Proceedings of the 5th World Congress on Intelligent Control and Automation (WCICA 2004),2004:876-880.
[61]徐兆棣,张嗣瀛.不确定非线性时滞系统的鲁棒可靠控制[J].控制与决策.2001,16(4)：452-456.
[62]L. Keviczky, C. Banyasz. Robust stability and performance of time-delay control systems[J]. ISA transactions.2007,46(2):233-237.
[63]伏玉笋,田作华,施颂椒.非线性时滞系统输出反馈镇定[J].自动化学报.2002,28(5)：802-805.
[64]陈为胜,李俊民.一类非线性时滞输出反馈系统的自适应控制[J].控制理论与应用.2004,21(5)：844-847.
[65]任勤,宋林锋.一类非线性时滞系统通过自适应Backstepping的控制[J].太原理工大学学报.2006,37(6)：721-724.
[66]杨军,张兴照,陈为胜.随机时滞非线性系统状态反馈镇定[J].科技通报.2007,23(6)：885-890.
[67]F. U. Syed, M. L. Kuang, J. Czubay, et al. Derivation and experimental validation of a power-split hybrid electric vehicle model[J]. IEEE Transactions on Vehicular Technology. 2006,55(6):1731-1747.
[68]Z. Rahman, K. L. Butler, M. Ehsani. A comparison study between two parallel hybrid control concepts[M], SAE,2000.
[69]J. Gao, G. G. Zhu, E. G. Strangas, et al. Equivalent fuel consumption optimal control of a series hybrid electric vehicle[J]. Proceedings of the Institution of Mechanical Engineers, Part D:Journal of Automobile Engineering.2009,223(8):1003-1018.
[70]C. C. Lin, H. Peng, J. W. Grizzle, et al. Power management strategy for a parallel hybrid electric truck[J]. IEEE Transactions on Control Systems Technology.2003,11(6):839-849.
[71]L. V Perez, G. R. Bossio, D. Moitre, et al. Optimization of power management in an hybrid electric vehicle using dynamic programming[J]. Mathematics and Computers in Simulation. 2006,73(1):244-254.
[72]C.-C. Lin, H. Peng, J. W. Grizzle. A stochastic control strategy for hybrid electric vehicles[C]. Proceedings of the 2004 American Control Conference (ACC), USA,2004:4710-4715.
[73]J. Scordia, R. M. Desbois, R. Trigui, et al. Global optimisation of energy management laws in hybrid vehicles using dynamic programming[J]. International journal of vehicle design.2005, 39(4):349-367.
[74]于秀敏,曹珊,李君等.混合动力汽车控制策略的研究现状及其发展趋势[J].机械工程学报.2006,42(11)：10-16.
[75]I. Arsie, M. Graziosi, C. Pianese, et al. Optimization of supervisory control strategy for parallel hybrid vehicle with provisional load estimate[C]. Proceeding of AVEC04,2004: 23-27.
[76]A. Poursamad, M. Montazeri. Design of genetic-fuzzy control strategy for parallel hybrid electric vehicles[J]. Control Engineering Practice.2008,16(7):861-873.
[77]Y. Zhang, H. Liu. Fuzzy logic torque control strategy for parallel hybrid electric vehicles[C]. Proceedings of the 8th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), China,2011:640-644.
[78]崔星,项昌乐.混合动力系统分流耦合机构工作模式分析[J].农业工程学报.2009,25(11)：158-163.
[79]J. M. Miller. Hybrid electric vehicle propulsion system architectures of the e-CVT type[J]. IEEE Transactions on Power Electronics.2006,21(3):756-767.
[80]黄英,万国强,崔涛等.AMT换挡过程发动机控制策略研究[J].汽车工程.2012,34(3)：245-248.
[81]余天明,郑磊,李颂.电控机械式自动变速器离合器灰色预测PID控制技术[J].农业机械学报.2011,42(8)：1-6.
[82]黄智明,张建武,鲁统利等.基于DMC-PID串级控制算法的AMT离合器接合位置控制[J].汽车工程.2010,32(2)：133-136.
[83]B. Gao, H. Chen, K. Sanada. Clutch slip control of automatic transmission using backstepping technique[C].2009 ICROS-SICE International Joint Conference, Fukuoka, Japan,2009: 3015-3019.
[84]A. Rachid, M. Alamir, P. Tona. Unified MPC strategy for idle-speed control, vehicle start-up and gearing applied to an Automated Manual Transmission[C]. Proceedings of the 17th World Congress of the International Federation of Automatic Control (IFAC), South Korea,2008.
[85]G. Qin, A. Ge, J.-J. Lee. Knowledge-based gear-position decision[J]. IEEE Transactions on Intelligent Transportation Systems.2004,5(2):121-125.
[86]J. Qu, Y. Zhang. Control of clutch engagement for AMT based on Fuzzy Logic[C]. Proceeding of the 2010 International Symposium on Information Science and Engineering, China,2010:47-50.
[87]S. Sakaguchi, I. Sakai, T. Haga. Application of fuzzy logic to shift scheduling method for automatic transmission[C]. The 2nd IEEE International Conference on Fuzzy Systems,1993: 52-58.
[88]杨春兰,薛大为,程荣龙.机械式自动变速系统起步过程离合器智能控制[J].机械工程师.2008,10：33-35.
[89]陈清洪,秦大同.动变速汽车神经网络三参数换挡策略[J].控制理论与应用.2010,27(11)：1580-1584.
[90]陈清洪,秦大同,叶心.AMT重型汽车起步离合器最优控制[J].中国公路学报.2010,23(1)：116-121.
[91]高智,钟再敏,孙泽昌.电动AMT选换挡电机执行机构位置最优控制[J].汽车工程.2011,33(2)：133-137.
[92]H. Hu, X. Wang, Y. Shao. Optimization of the shift quality of dual clutch transmission using genetic algorithm[C]. Proceeding of the 6th International Conference on Natural Computation, China,2010:4152-4156.
[93]Y. Murphey. Intelligent vehicle power management:An overview[M], Springer Berlin Heidelberg,2008.
[94]G. Lucentc, M. Montanari, C. Rossi. Modelling of an automated manual transmission system[J]. Mechatronics.2007,17(2):73-91.
[95]J. Pu, C. Yin. Optimal control of fuel economy in parallel hybrid electric vehicles [J]. Proceedings of the Institution of Mechanical Engineers, Part D:Journal of Automobile Engineering.2007,221(9):1097-1106.
[96]C.-C. Lin, J.-M. Kang, J. W. Grizzle, et al. Energy management strategy for a parallel hybrid electric truck[C]. Proceedings of the 2001 American Control Conference (ACC), USA,2001: 2878-2883.
[97]A. Sciarretta, M. Back, L. Guzzella. Optimal control of parallel hybrid electric vehicles[J]. IEEE Transactions on Control Systems Technology.2004,12(3):352-363.
[98]A. Rajagopalan, G. Washington, G. Rizzoni, et al. Development of fuzzy logic and neural network control and advanced emissions modeling for parallel hybrid vehicles[R]. National Renewable Energy Laboratory,2003.
[99]B. K. Powell, K. E. Bailey, S. R. Cikanek. Dynamic modeling and control of hybrid electric vehicle powertrain systems[J]. IEEE Transactions on Control Systems.1998,18(5):17-33.
[100]H. K. Khalil. Nonlinear systems (Third Edition)[M], Prentice hall Englewood Cliffs, NJ, 2002.
[101]吴受章.最优控制理论与应用[M],机械工业出版社,2008.
[102]M. Athans, P. L. Falb. Optimal control:an introduction to the theory and its applications[M], Dover Publications New York,2007.
[103]J. R. Cloutier, C. N. D'Souza, C. P. Mracek. Nonlinear regulation and nonlinear H-infinity control via the state-dependent Riccati equation technique. I-Theory[C].1st International Conference on Nonlinear Problems in Aviation and Aerospace, USA,1996:117-130.
[104]杨帆,赵党军,李国平等.基于SDRE方法的一级旋转倒立摆控制[J].华中科技大学学报(自然科学版).2008,36(10)：25-28.
[105]T. Cimen. State-dependent Riccati equation (SDRE) control:a survey[C]. Proceedings of the 17th World Congress of the International Federation of Automatic Control (IFAC), South Korea,2008:3761-3775.
[106]J. R. Cloutier. State-dependent Riccati equation techniques:an overview[C]. Proceedings of the 1997 American Control Conference (ACC), USA,1997:932-936.
[107]T. Cimen, S. P. Banks. Global optimal feedback control for general nonlinear systems with nonquadratic performance criteria[J]. Systems & Control Letters.2004,53(5):327-346.
[108]张军,徐世杰.基于SDRE方法的挠性航天器姿态控制[J].宇航学报.2008,29(1)：138-144.
[109]L. Wilbur, A. Andrew. A stability result with application to nonlinear regulation[J]. Journal of Dynamic Systems, Measurement, and Control.2002,124(3):452-456.
[110]P. Menon, V. Iragavarapu, G. Sweriduk, et al. Software tools for nonlinear missile autopilot design[R]. DTIC Document,1999.
[111]M. Xin, H. Pan. Nonlinear optimal control of spacecraft approaching a tumbling target[J]. Aerospace Science and Technology.2011,15(2):79-89.
[112]D. K. Parrish, D. B. Ridgely. Control of an artificial human pancreas using the SDRE method[C]. Proceedings of the 1997 American Control Conference (ACC),1997:1059-1060.
[113]W. Luo, Y.-C. Chu. Attitude control using the SDRE technique[C]. Proceedings of the 7th International Conference on Control, Automation, Robotics and Vision,2002:1281-1286.
[114]P. Shankar, R. K. Yedavalli, D. B. Doman. Dynamic inversion via state dependent Riccati equation approach:application to flight vehicles[C].2003 AIAA Guidance, Navigation, and Control Conference and Exhibit,2003:1-9.
[115]P. Shankar, R. K. Yedavalli, D. B. Doman. Stability domain estimation for dynamic inversion embedded SDRE flight controller[C].2004 AIAA Guidance, Navigation, and Control Conference and Exhibit, USA,2004:1-11.
[116]A. A. Bogdanov, E. A. Bogdanov, E. A. Wan, et al. Model predictive neural control of a high fidelity helicopter model[C]. Proceedings of the 2001 AIAA Guidance Navigation and Control Conference, Montreal, Canada,2001:1-9.
[117]E. A. Wan, A. A. Bogdanov. Model predictive neural control with applications to a 6 DOF helicopter model[C]. Proceedings of the 2001 American Control Conference (ACC), USA, 2001:488-493.
[118]陈谋,姜长生,吴庆宪等.一类串级不确定非线性系统的最优滑模控制器设计[J].系统工程理论与实践.2003,11：31-35.
[119]J. Shen, S. Balakrishnan. A class of modified Hopfield networks for control of linear and nonlinear systems[C]. Proceedings of the 1998 American Control Conference (ACC),1998: 964-969.
[120]A. Yoon, P. Khargonekar, K. Hebbale. Randomized algorithms for open-loop control of clutch-to-clutch transmissions[J]. Journal of dynamic systems, measurement, and control. 1999,121(3):508-517.
[121]A. Zhang, L. Zhang. RBF neural networks for the prediction of building interference effects[J]. Computers & Structures.2004,82(27):2333-2339.
[122]S. Haykin. Neural networks:a comprehensive foundation[J]. Prentice Hall, sa.2008.
[123]D. Gao, Y. Ji, C. Li. Frequency domain analysis based RBF networks and their applications to function approximations[J]. Proceeding of 2004 IEEE International Joint Conference on Neural Networks, Budapest,2004:2111-2116.
[124]Y. Ma, L. Ma, J. Ma. RBF neural network based fault diagnosis for the thermodynamic system of a thermal power generating unit[C]. Proceedings of 2005 International Conference on Machine Learning and Cybernetics, China,2005:4738-4743.
[125]T. Jin, P. Li. Application of neural networks in modelling of the transmission hydraulic actuator[J]. International Journal of Modelling, Identification and Control.2009,8(2): 148-157.
[126]牛铭奎,葛安林.高速开关电磁阀的特性应用研究[J].汽车技术.1999,7：13-16.
[127]陈丽君.电控机械式自动变速器液压换挡执行机构的研究[D].重庆：重庆大学,2011.
[128]张敏敏.自动变速器液压控制系统建模与模型简化[D].上海：上海交通大学,2010.
[129]刘少军,夏毅敏.高速开关电磁阀的PWM控制及改进技术[J].机床与液压.1998(4)：52-53.
[130]F. M. White. Fluid Mechanics[M], McGraw-Hill Companies, Inc.,2011.
[131]L. Ljung. System identification:theory for the user[M], Pearson Education,1998.
[132]P. Li, T. Jin, X. Du. Simple recursive algorithm for linear-in-thcparamctcrs nonlinear model identification[J]. Science in China Series F:Information Sciences.2009,52(10):1739-1745.
[133]T. Markel, A. Brooker, T. Hendricks, et al. ADVISOR:a systems analysis tool for advanced vehicle modeling[J]. Journal of Power Sources.2002,110(2):255-266.
[134]T. Jin, P. Li. Kinematics and dynamics modeling of the planet transmission system[C]. Proceeding of the 8th International Symposium on Test and Measurement, China,2009: 2234-2237.
[135]D. C. Karnopp, D. L. Margolis, R. C. Rosenberg. System dynamics:modeling, simulation, and control of mechatronic systems[M], Wiley, com,2012.
[136]J. Deur, M. Cipek, J. Petric. Bond graph modeling of series-parallel hybrid electric vehicle power train dynamics[C]. Proceedings of the 2010 Spring Simulation Multiconference, USA, 2010:1-10.
[137]M. R. Schmidt. Two-mode, compound-split electro-mechanical vehicular transmission[P]. Google Patents,1999.
[138]T. H. Grewe. Defining the General Motors 2-mode hybrid transmission[J]. SAE Technical Paper 2007-01-0273.2007.
[139]G. Filippini, N. Nigro, S. Junco. Vehicle dynamics simulation using bond graphs[J]. International Modeling and Simulation Multiconference.2007.
[140]P. Li, T. Jin, X. Du. MIMO CCS model identification based on GA for closed-loop field data[J]. International Journal of Modelling, Identification and Control.2011,13(1):30-37.
[141]P. Li, X. Du, Y. Liu. AGC parameters optimization using real coded genetic algorithm[C]. Power System Technology, Proceeding of the 2002 International Conference on PowerControl, 2002:646-650.
[142]M. Montazeri-Gh, A. Poursamad, B. Ghalichi. Application of genetic algorithm for optimization of control strategy in parallel hybrid electric vehicles[J]. Journal of the Franklin Institute.2006,343(4-5):420-435.
[143]W. Chang. An improved real-coded genetic algorithm for parameters estimation of nonlinear systems[J]. Mechanical Systems and Signal Processing.2006,20(1):236-246.
[144]T. Jin, P. Li, C. Chen, et al. Fuzzy state feedback stabilization for the time-delay nonlinear system[C]. Proceedings of the 29th Chinese Control Conference (CCC), China,2010: 488-491.
[145]O. Barbarisi, E. R. Westervelt, F. Vasca, et al. Power management decoupling control for a hybrid electric vehicle[C].44th IEEE Conference on Decision and Control and 2005 European Control Conference (CDC-ECC'05), Spain,2005:2012-2017.
[146]H. Yang, A. Smith, S. Swales, et al. Development of two-mode hybrid powertrain with enhanced EV capability[J]. SAE International Journal of Engines.2011,4(1):1058-1070.
[147]G. Paganelli, T. Guerra, S. Delprat, et al. Simulation and assessment of power control strategies for a parallel hybrid car[J]. Proceedings of the Institution of Mechanical Engineers, Part D:Journal of Automobile Engineering.2000,214(7):705-717.
[148]M. Duoba, H. Lohse-Busch, R. Carlson, et al. Analysis of power-split HEV control strategies using data from several vehicles[J]. SAE paper.2007:2007-01-0291.
[149]K. Bailey, S. Cikanek, N. Sureshbabu. Parallel hybrid electric vehicle torque distribution method[C]. Proceedings of the 15th World Congress of the International Federation of Automatic Control (IFAC), USA,2002:3708-3712.
[150]J. D. Wishart, Y. Zhou, Z. Dong. Review, modelling and simulation of two-mode hybrid vehicle architecture[C]. Proceedings of ASME 2007 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC/CIE 2007), USA,2007:1091-1112.
[151]B. Conlon. Comparative analysis of single and combined hybrid electrically variable transmission operating modes[J]. SAE paper.2005:2005-01-1162.
[152]叶明.机械自动变速传动建模与仿真及试验软件开发[D].重庆：重庆大学,2003.
[153]F. Garofalo, L. Glielmo, L. Iannelli, et al. Smooth engagement for automotive dry clutch[C]. Proceedings of the 40th IEEE Conference on Decision and Control,2001:529-534.
[154]A. Serrarens, M. Dassen, M. Steinbuch. Simulation and control of an automotive dry clutch[C]. Proceedings of the 2004 American Control Conference (ACC), USA,2004: 4078-4083.
[155]D. Karnopp. Computer simulation of stick-slip friction in mechanical dynamic systems[J]. Journal of Dynamic Systems, Measurement, and Control.1985,107(1):100-103.
[156]曹桂军,葛安林,郑磊等.电控机械式自动变速器换挡过程中离合器的接合控制[J].机械工程学报.2005,41(12)：234-238.
[157]L. Zhao, T. Liu, B. Song. Optimum design of automobile diaphragm spring clutch[C]. Proceedings of the 2008 Vehicle Power and Propulsion Conference (VPPC), China,2008:1-4.
[158]何仁,刘文光,黄大星.不同道路负荷的AMT车辆起步过程离合器控制策略[J].江苏大学学报(自然科学版).2009,30(5)：459-462.
[159]郭立书,葛安林.电控机械式自动变速器换挡过程控制[J].农业机械学报.2003,34(2)：l-3.
[160]赵永胜,刘志峰,杨文通等.基于dSPACE的汽车自动离合器快速控制原型试验[J].农业机械学报.2009,40(9)：12-15.
[161]L. Glielmo, L. Iannelli, V. Vacca, et al. Speed control for automated manual transmission with dry clutch[C]. Proceeding of the 43rd IEEE Conference on Decision and Control,2004: 1709-1714.
[162]L. Glielmo, L. Iannelli, V. Vacca, et al. Gearshift control for automated manual transmissions[J]. IEEE/ASME Transactions on Mechatronics.2006,11(1):17-26.
[163]P. Dolcini, C. C. de Wit, H. Bechart. Improved optimal control of dry clutch engagcmcnt[C]. Proceedings of the 16th World Congress of the International Federation of Automatic Control (IFAC), Czech,2005.
[164]J. Zhang, C. Li, G. Xi. System dynamic modelling and adaptive optimal control for automatic clutch engagement of vehicles[J]. Proceedings of the Institution of Mechanical Engineers, Part D:Journal of Automobile Engineering.2002,216(12):983-991.
[165]T. Jin, P. Li, Z. Jia. Optimal tracking design for dry clutch engagement[C]. Proceedings of 2013 Chinese Intelligent Automation Conference, China,2013:685-692.
[166]王庆年,冀尔聪,王伟华.并联混合动力汽车模式切换过程的协调控制[J].吉林大学学报(工学版).2008,38(1)：1-6.
[167]T. Jin, P. Li, G. Zhu. Optimal decoupled control for dry clutch engagement[C]. Proceedings of the 2013 American Control Conference (ACC), USA,2013:6740-6745.
[168]J. Pittner, M. A. Simaan. Optimum feedback controller design for tandem cold metal rolling[C]. Proceedings of the 17th World Congress of the International Federation of Automatic Control (IFAC), South Korea,2008:988-993.
[169]C. P. Mracek, J. R. Cloutier. Control designs for the nonlinear benchmark problem via the state-dependent Riccati equation method[J]. International journal of robust and nonlinear control.1998,8(4-5):401-433.
[170]D. Kleinman. Stabilizing a discrete, constant, linear system with application to iterative methods for solving the Riccati equation[J]. IEEE Transactions on Automatic Control.1974, 19(3):252-254.
[171]C. G. Broyden. A class of methods for solving nonlinear simultaneous equations[J]. Mathematics of computation.1965,19(92):577-593.
[172]P. P. Khargonekar, M. A. Rotea. Multiple objective optimal control of linear systems:the quadratic norm case[J]. IEEE Transactions on Automatic Control.1991,36(1):14-24.
[173]G. Zhu, M. Rotea, R. Skelton. A convergent algorithm for the output covariance constraint control problem[J]. SIAM Journal on Control and Optimization.1997,35(1):341-361.
[174]G. Zhu, K. M. Grigoriadis, R. E. Skelton. Covariance control design for hubble space telescope[J]. Journal of Guidance, Control, and Dynamics.1995,18(2):230-236.
[175]夏长亮,刘均华,俞卫等.基于扩张状态观测器的永磁无刷直流电机滑模变结构控制[J].中国电机工程学报.2006,26(20)：139-143.
[176]苗中华,王旭永,刘成良等.基于滑模变结构控制的液压伺服系统超低速轨迹跟踪[J].上海交通大学学报.2008,42(7)：1182-1186.
[177]蔡国梁,周维怀,郑松等.不确定离散系统全程滑模变结构控制[J].江苏大学学报(自然科学版).2010,31(3)：363-368.
[178]刘金琨,孙富春.滑模变结构控制理论及其算法研究与进展[J].控制理论与应用.2007,24(3)：407-418.
[179]刘金琨.滑模变结构控制MATLAB仿真[M],清华大学出版社,2005.
[180]P. Li, T. Jin, X. Du. Torque observer modelling for vehicle transmission shifting processing based on Neural Networks[C]. Proceedings of the 17th World Congress of the International Federation of Automatic Control (IFAC), South Korea,2008:16039-16044.
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