基于磁道钉导航的车道保持系统信息融合与控制技术研究
|
摘要
自动公路系统(Automated Highway System，简称AHS)是智能运输系统(Intelligent Transport System，简称ITS)领域中技术难度最大的系统。该系统包括车辆自动导航和控制、交通管理自动化以及事故处理自动化。这是智能运输系统的最终目标，即实现人、车、路高度一体化。自动公路系统的关键技术之一是车道保持系统，它是在道路导航设施和车载传感器等基础设施上，实现车辆自动行驶在期望的车道上。从车辆获取道路信息的方式来分类，自动驾驶有视觉导航和磁信号导航。视觉导航容易受天气、地面环境影响，不利于推广，而磁信号导航具有不受天气影响、可靠性高等优点，可以弥补视觉导航的不足，两者的联合使用可以提高安全性和可靠性，真正实现路-车一体化。磁性道钉导航是磁信号导航方式的一种，由于它具有成本低、易维护等优点，近几年成为国际研究的热点。国内在自动驾驶方面的研究主要集中在视觉导航方式上，以磁道钉方式导航的车道保持系统的研究除了本课题组外国内还没有见到相关报道。
本文以国家智能运输系统研究中心的自动公路系统试验车和专用的磁道钉道路为工作平台，在大量的现场实验基础上，针对基于磁道钉导航的车辆车道保持技术中的若干问题展开研究，运用神经网络、模糊逻辑、遗传算法等方法在多传感器信息融合、控制模型等方面进行了系统的研究，主要完成了以下几方面的工作：
(1)对车道保持系统硬件平台进行了设计，论述了导航设施——磁道钉的设计过程和车载工控机、步进电机等的选择。着重讨论了磁传感器的设计过程，并从信号的幅值、信号的信噪比、同时性和对称性等方面对传感器进行了测试，证明了设计的磁传感器获取道路磁场信息是可以用于自动控制的，后面建立控制模型奠定了基础。
(2)通过分析信息融合技术的研究现状，针对自行设计的传感器获取的信
武汉理工大学博士学位论文
息源的特点，提出了三种信息融合方法，分别建立了数学模型。尤其对神经网
络方法进行了深入的研究，建立了神经网络结构，确定了学习算法，并且通过
专门的实验获取训练数据，得到了收敛的网络权值。最后，用实验比较了三种
模型的优劣，确定神经网络信息融合方法比较接近实际。信息融合模型的建立，
实际上是汽车横向位置的定位，为后面进行控制模型的研究奠定了基础。
(3)设计了基于磁道钉导航的模糊控制器，对遗传算法进行了研究，提出
了变区域遗传算法，通过实例验证了该算法的有效性，并用于搜索模糊控制器
中的参数，取得了较好的效果。现场实验结果表明，该模糊控制器可以实现自
动控制，但控制精度有待于完善。
(4)从理论上探讨了磁道钉编码的意义，提出了磁道钉编码系统结构，并
针对本次试验设计了磁道钉编码系统，现场试验结果表明，磁道钉编码系统是
可靠的、实用的。
(5)在模糊控制的基础上，提出了模糊神经网络控制器，并用本文提出的
变区域多层遗传算法与BP相结合进行网络学习，在理论上证明了遗传学习算法
的收敛性。经过模型闭环系统训练，得到模糊神经网络控制器。用简化的二轮
模型对本章设计的模糊神经网络控制器进行仿真，仿真结果表明，本文设计的
模糊神经网络控制器比普通的模糊控制器在性能上有所改善，提高了控制模型
的性能。
(6)在讨论了学习控制研究方法和水平的基础上，分析了自动公路系统中
控制模型引入学习，尤其是在线学习的重要性和必要性。并设计了车道保持系
统在线学习控制结构模型，为下一步实现真正的在线学习控制提供了理论基础。
Automated Highway System (AHS) is the most difficulty sub system in Intelligent Transport System(ITS). The system includes vehicle automatic navigation and control, traffic management automation and traffic accident treatment automation. It is the final goal for ITS, i.e. integration of human, vehicle and road. Lane keeping system is one of the key technologies of AHS in which vehicle can be run following the anticipant lane automatically based upon the navigation installation and vehicle- mounted sensors. Vision navigation and magnetic information navigation are two types of automatic driving. Vision navigation is easy to disturbed by weather and road condition, however magnetic marker navigation hasn't these disadvantages. Its safety and reliability can be enhanced if these two types are used in one system. This is why magnetic marker navigation becomes a hotspot in recent years internationally. In China, vision navigation is the main research project for automatic driving, however only our group has studi
ed magnetic marker navigation in lane keeping system, no any other reports have been searched.
This dissertation outlines the study on several problems of vehicle lane keeping system based on magnetic markers. Site experiment were carried out on the testing vehicle for AHS and the magnetic markers road in National Intelligent Transport System Center. Also, study on multi-sensor information fusion and control model by using neural net system, fuzzy logic and genetic algorithm were carried out. The work completed are as follows mainly:
(1) Hardware system for lane keeping system including magnetic marker, magnetic sensor, control system and executor machine were designed. Research progress of magnetic sensor is described in detail. Test result indicated that the self-designed magnetic marker can be used to obtained magnetic information.
(2) Having analyzed the information fusion technology, three
mathematical models were made based on the signal sources obtained from self-designed magnetic sensor. The neural net method is discussed in detail, the configuration of neural net is established, study algorithm is decided. Training data were obtained from experiment. Simulation indicated that neural net method is more accurate than'the others two methods. In fact, information fusion modal is the location of vehicle lateral position which establishes research fundament of control modal later.
(3) The fuzzy controller based on magnetic markers is designed. The changeable area and hierarchical genetic algorithm is studied; several examples indicated that this algorithm which can be used to search parameter of fuzzy controller is effective. Site experiment showed that designed fuzzy controller can be used in lane keeping system.
(4) The significant of magnetic marker coding system is approached
theoretically, structure of this coding system is introduced; this coding system
is designed according to test requirement.
(5)Based on fuzzy control theory, fuzzy neural network (FNN) controller
is introduced; the changeable area and hierarchical genetic algorithm is used
to training fuzzy neural network. Simulation indicated that this designed
fuzzy neural controller is effective.
(6) Based on the discussion on learning control method, control model learning in AHS, particular in the important and necessity of online learning, is analyzed. Structure model of online learning control for lane keeping system is designed. It provides the theoretical fundament for further study on online learning control in the future.
本文以国家智能运输系统研究中心的自动公路系统试验车和专用的磁道钉道路为工作平台，在大量的现场实验基础上，针对基于磁道钉导航的车辆车道保持技术中的若干问题展开研究，运用神经网络、模糊逻辑、遗传算法等方法在多传感器信息融合、控制模型等方面进行了系统的研究，主要完成了以下几方面的工作：
(1)对车道保持系统硬件平台进行了设计，论述了导航设施——磁道钉的设计过程和车载工控机、步进电机等的选择。着重讨论了磁传感器的设计过程，并从信号的幅值、信号的信噪比、同时性和对称性等方面对传感器进行了测试，证明了设计的磁传感器获取道路磁场信息是可以用于自动控制的，后面建立控制模型奠定了基础。
(2)通过分析信息融合技术的研究现状，针对自行设计的传感器获取的信
武汉理工大学博士学位论文
息源的特点，提出了三种信息融合方法，分别建立了数学模型。尤其对神经网
络方法进行了深入的研究，建立了神经网络结构，确定了学习算法，并且通过
专门的实验获取训练数据，得到了收敛的网络权值。最后，用实验比较了三种
模型的优劣，确定神经网络信息融合方法比较接近实际。信息融合模型的建立，
实际上是汽车横向位置的定位，为后面进行控制模型的研究奠定了基础。
(3)设计了基于磁道钉导航的模糊控制器，对遗传算法进行了研究，提出
了变区域遗传算法，通过实例验证了该算法的有效性，并用于搜索模糊控制器
中的参数，取得了较好的效果。现场实验结果表明，该模糊控制器可以实现自
动控制，但控制精度有待于完善。
(4)从理论上探讨了磁道钉编码的意义，提出了磁道钉编码系统结构，并
针对本次试验设计了磁道钉编码系统，现场试验结果表明，磁道钉编码系统是
可靠的、实用的。
(5)在模糊控制的基础上，提出了模糊神经网络控制器，并用本文提出的
变区域多层遗传算法与BP相结合进行网络学习，在理论上证明了遗传学习算法
的收敛性。经过模型闭环系统训练，得到模糊神经网络控制器。用简化的二轮
模型对本章设计的模糊神经网络控制器进行仿真，仿真结果表明，本文设计的
模糊神经网络控制器比普通的模糊控制器在性能上有所改善，提高了控制模型
的性能。
(6)在讨论了学习控制研究方法和水平的基础上，分析了自动公路系统中
控制模型引入学习，尤其是在线学习的重要性和必要性。并设计了车道保持系
统在线学习控制结构模型，为下一步实现真正的在线学习控制提供了理论基础。
Automated Highway System (AHS) is the most difficulty sub system in Intelligent Transport System(ITS). The system includes vehicle automatic navigation and control, traffic management automation and traffic accident treatment automation. It is the final goal for ITS, i.e. integration of human, vehicle and road. Lane keeping system is one of the key technologies of AHS in which vehicle can be run following the anticipant lane automatically based upon the navigation installation and vehicle- mounted sensors. Vision navigation and magnetic information navigation are two types of automatic driving. Vision navigation is easy to disturbed by weather and road condition, however magnetic marker navigation hasn't these disadvantages. Its safety and reliability can be enhanced if these two types are used in one system. This is why magnetic marker navigation becomes a hotspot in recent years internationally. In China, vision navigation is the main research project for automatic driving, however only our group has studi
ed magnetic marker navigation in lane keeping system, no any other reports have been searched.
This dissertation outlines the study on several problems of vehicle lane keeping system based on magnetic markers. Site experiment were carried out on the testing vehicle for AHS and the magnetic markers road in National Intelligent Transport System Center. Also, study on multi-sensor information fusion and control model by using neural net system, fuzzy logic and genetic algorithm were carried out. The work completed are as follows mainly:
(1) Hardware system for lane keeping system including magnetic marker, magnetic sensor, control system and executor machine were designed. Research progress of magnetic sensor is described in detail. Test result indicated that the self-designed magnetic marker can be used to obtained magnetic information.
(2) Having analyzed the information fusion technology, three
mathematical models were made based on the signal sources obtained from self-designed magnetic sensor. The neural net method is discussed in detail, the configuration of neural net is established, study algorithm is decided. Training data were obtained from experiment. Simulation indicated that neural net method is more accurate than'the others two methods. In fact, information fusion modal is the location of vehicle lateral position which establishes research fundament of control modal later.
(3) The fuzzy controller based on magnetic markers is designed. The changeable area and hierarchical genetic algorithm is studied; several examples indicated that this algorithm which can be used to search parameter of fuzzy controller is effective. Site experiment showed that designed fuzzy controller can be used in lane keeping system.
(4) The significant of magnetic marker coding system is approached
theoretically, structure of this coding system is introduced; this coding system
is designed according to test requirement.
(5)Based on fuzzy control theory, fuzzy neural network (FNN) controller
is introduced; the changeable area and hierarchical genetic algorithm is used
to training fuzzy neural network. Simulation indicated that this designed
fuzzy neural controller is effective.
(6) Based on the discussion on learning control method, control model learning in AHS, particular in the important and necessity of online learning, is analyzed. Structure model of online learning control for lane keeping system is designed. It provides the theoretical fundament for further study on online learning control in the future.
引文
【1】吴超仲，严新平．自动公路系统横向控制研究．交通与计算机，2001．Vol.19，No.3：23～25
【2】Guldner J. Patwardhan S. Tan H.-S. and Zhang W.-B. Coding of Magnetic Markers for Demonstration of Automated Highway System. Transportation Research Board Anuual Meeting, Washington. DC, USA, 1997
【3】Tan H.-S. Guldner J.Etc The development of an Automated Steering Vehical Based on Roadway Magnets-A Case Study of Mechatronic System design. IEEE/ASME Transportation on Mechatronics, 1999
【4】Han-Shue Tan, Bénédicte Bougler. Vehicle Lateral Warning. Guidance and Control Based on Magnetic Markers:PATH Report of AHSRA Smart Cruise 21 Proving Tests. California PATH Working Paper. UCB-ITS-PWP-2001-6
【5】杨欣欣．智能移动机器人导航与控制技术研究：[博士学位论文]．北京：清华大学计算机科学技术系，1999
【6】吴晖．ALV路径跟踪控制方法研究：[博士学位论文]．长沙：国防科学技术大学，1997
【7】王荣本．图像识别自动引导车辆系统研究：[博士学位论文]．吉林工业大学，1995
【8】Lin C J and Lin C T. Reinforcement learning for an ART-based fuzzy adaptive learning control network. IEEE Trans. Neural Networks, 1996. 7(3): 709～731
【9】Rouveird C and Alberts P. A knowledge-level model of a configurable learning system. IEEE Expert Intell. Syst. & Their Appli..1996. 11(4): 50～58
【10】Yang J.Xu Y and Chert C S. Human action learning via Hidden Markov model. IEEE Trans. Syst., Man. and Cybern, 1997. 27(1): 34～44
【11】Layne J R and Passino K M. Fuzzy model reference learning control. J. Int. & Fuzzy Syst., 1996. 4(1): 33～47
【12】Chen S B. Wu L and Wang Q L. Self-learning fuzzy neural networks for control of uncertain systems with time delays. IEEE Trans.Syst., Man, and Cybern., 1997. 27(1): 142～148
【13】Chang Tang-Hsein. Vehicle Lateral Control Modeling for Steering Automation in the ADVANCE-F System. Transportation of Institute of Measurement and Control, 1994. Vol.18, No.3: 149～159
【14】Feng K.-T., Tan H.-S., Etc. Look-aheah human-machine Interface for Assistance of Manual Vehicle Steering. Vehicle system Dynamics, 1996. 26(4): 243～269
【15】渡边克也．ITS(高度道路交通)动向将来．大阪：关西经济研究中心，1999．9
【16】林鹰．新世纪的自动化高速公路．交通与运输，2000．2
【17】张智文，魏凤，侯福深，等．中国智能运输系统体系框架．第4届亚太地区ITS会议论文集
【18】谭业江，等译．车辆导航系统在日本的应用．国外公路，1997．17(1)
【19】严新平，吴超仲．中国智能运输系统发展现状与趋势．交通企业管理，2001．No.2：57～59
【20】纪寿文，王荣本，初秀民，徐友春．国内外智能车辆研究进展．中国交通研究与探索．北京：人民交通出版社，1999
【21】张友坤，王荣本，王志中，张国川．一种基于机器视觉导航的高速智能车辆总体设计概述．中国交通研究与探索．北京：人民交通出版社，1999
【22】杨利兵，吴超仲，严新平．自动公路系统国内外研究现状．第四届全国交通运输领域青年学术会议论文集．北京：中国民航出版社，2001．998～1002
【23】M. Agarwat. Combining neural and conventional paradigms for modelling, prediction and control. International Journal of Systems Science, 1997. 28(1): 65～81
【24】Guo Konghui, Guan Xin. Modelling of driver/vehicle directional control ystem. [J]. Vehicle System Dynamics, 1993. 22: 141～184.
【25】N. Amann, D. H. Owens and E. Rogers. Predictive optimal iterative control. International Journal of Control, 1998. 69(2): 203～226
【26】T. A. Badgwell. Robust model predictive control of stable linear systems. International Journal of Control, 1997. 68(4): 797～818
【27】Musavi M T, Chan K H, Hummels D M, et al. A Probabilitic Model for Evaluation of Neural Network Classifiers. Pattern Recognition, 1992. 25(10): 1241～1251
【28】R. Balakrishna and A. Ghosal. Modelling of slip for wheeled mobile robots. IEEE Trans. on Robotics and Automation, 1995. 11(D): 126～138
【29】F. D. Boyden and S. A. Velinsky. Dynamic modeling of wheeled mobile robots for high load application. In: Proc. of 1994 IEEE International Conference on Robotics and Automation, San Diego,, USA, 1994. 3071～3078
【30】吴翊，吴孟达，成礼智编著．数学建模的理论与实践．湖南：国防科技大学出版社，1999
【31】N. M. Charkari, K. Ishii and H. Mori. Sensory selection in vehicle detection and avoidance. Robotics and Autonomous Systems, 1996. 17:3～13
【32】E. D. Dickmanns, B. Mysliwe～and T. Christians. An integrated spatio-temporal approach to automatic visual guidance of autonomous vehicles. IEEE Trans. on System, Man and
Cybernetics, 1990. 20(6): 1273～1283
【33】J.Manyika and H.Durrant-Whyte. Data Fusion and Sensor Management: A Decentralized Information-Theoretic Approach. Ellis Horwood Limited, 1994
【34】E. Freund and R. Mayr. Nonlinear path control in automated vehicle guidance. IEEE Trans. on Robotics and Automation, 1997. 13(1): 49～60
【35】M. J. Grimble. Long range predictive optimal control law with guaranteed stability for process control applications. Journal of Dynamic Systems. Measurement, and Control. Dec., 1993. 115: 600～610
【36】H. Hu and M. Brady. A parallel processing architecture for sensor-based control of intelligent mobile robots. Robotics and Autonomous Systems, 1996. 17: 235～257
【37】K. J. Hunt, D. Sbarbaro, R. Zhikowski and P. J. Gawthrop. Neural networks for control systems-A survey, dutomatica, 1992. 28(6): 1083～1112
【38】A. J. Kelly. An intelligent predictive controller for autonomous vehicles. CMU Robotics Institute Technical Report, CMU-RI-TR-94-20, 1994
【39】AHS公道实验成功(ITS实用化试验)．ITS-Review, 1997. Vol. 2：2
【40】ITS-动出高度道路交通．三菱综合研究所ITS推进室．东京：日刊工业新闻社，1998．12
【41】李洪志．信息融合技术．北京：国防工业出版社，1996
【42】J.Manyika and H.Durrant-Whyte. Data fusion and sensor management:A Decentralized Information-Theoretic Approach. Ellis Horwood Linited, 1994
【43】康耀红．数据融合理论与应用．西安：西安电子科技大学出版社，1997
【44】李兆杰等．多传感器集成融合技术．传感器技术，1996(6)：1～45
【45】黄澍川．用多传感器融合系统识别摩擦副的磨损状态．摩擦学学报，1996(4)：367～370
【46】刘同明．基于证据理论模糊推理的多传感器信息融合海上目标识别．模式识别与人工智能，1999(1)：25～31
【47】徐从富等．Dempster-Shafer证据推理方法理论与应用综述．模式识别与人工智能，1999(4)：424～429
【48】王国宏等．基于模糊综合的目标识别时空数据融合算法．模式识别与人工智能，1997(1)：61～67
【49】唐文彬等．一种多传感器数据融合目标识别新方法．传感器技术，1998(3)：23～24
【50】张彦铎等．多传感器信息融合及在智能故障诊断中的应用．传感器技术．1999(2)，18～22
【51】黄瑛等．多传感器数据融合系统的软件实现．传感器技术，1998(6)：9～11
【52】王祁等．分布式多传感器数据融合．传感器技术，1997(5)：8～10
【53】王婷杰等．一种基于模糊理论的一致性数据融合方法．传感器技术，1999(6)：50～53
【54】金锋．数据融合与传感器建模．传感器技术，1999(6)：47～49
【55】马洪超等．地学数据融合技术综述．地质科技情报，1999(3)
【56】李军等．影像局部直方图匹配滤波技术用于遥感影像数据融合．测绘学报，1999(8)
【57】林明星等．基于神经网络的多传感器信息融合技术．新技术新工艺，1999(5)
【58】苏惠敏等．驾驶员助手系统的关键技术—信息融合．测控技术，1999(1)
【59】滕召胜等．基于数据融合的智能水分快速测定仪．1999(10)
【60】谷立臣等．基于信息融合的传感器故障检测方法．西安建筑科技大学学报，1999(3)
【61】张晓明等．基于神经网络的传感器融合和综合在刀具状态检测中的应用．组机床与自动化加工技术，1993(12)：15～23
【62】H.Tahani and J.M.Keller. Information fusion in computer vision using the fuzzy integral. IEEE Trans. Syst, Man and Cyberne, 1990. Vol.20, No.30: 733～741
【63】吴超仲，严新平．基于遗传算法的信息融合在摩擦学组合故障诊断中的应用．摩擦学学报，2001. No.3: 301～304
【64】P. J. Antsaldis. Neural networks in control systems. IEEE. Control Systems Magzine, Apr. 1990: 3～5
【65】T. A. Badgwell. Robust stability conditions for SISO model predictive control algorithms. Automatica, 1997. 33(7): 1357～1361
【66】Lee C C. Fuzzy logic in control systems: Fuzzy logic control(Part Ⅱ). IEEE Trans Syst. Man, Cybern, 1990. 20: 419～435
【67】D. Feng and B. H. Krogh. Satisficing Feedback Strategies for Local Navigation of Autonomous Mobile Robot. IEEE Transaction of Systems, Man and Cybernltics, 1990. 20(6): 1383～1395
【68】Buckley J J. Fuzzy logic controllers are universal approximations. Fuzzy Sets Syst, 1993. 58: 273～278
【69】T. Fukuda and T. Shibata. Theory and applications of neural networks industrial control systems. IEEE Trans. on Industrial Electronics, 1992. 39(6): 472～489
【70】Kim J M, Mendel J M. Fuzzy. basis functions: Comparisons with other basis functions. IEEE Trans Fuzzy Syst, 1995. 2(3): 158～168
【71】S. Horikawa, T. Furuhashi and Y. Uchikawa. On fuzzy modeling using fuzzy neural networks with the back-propagation algorithm. IEEE Trans. on Neural Networks, 1992. 3(5): 801～806
【72】T. C. Hu, A. B. Kahng and G. Robins. Optimal robust path planning in general environments. IEEE Trans. on Robotics and automation, 1993. 9(6): 775～784
【73】Wang L X. Stable adaptive fuzzy control of nonlinear systems. IEEE Trans Fuzzy Syst, 1993. 5(1): 146～155
[74] A. J. Kelly.A feedforward control approach to the local navigation problem for autonomous vehicles. CMU Robotics Institute Technical Report, 1994． CMU-RI-TR-94-17
[75] D. Langer, S.K.Rosenblatt, M. Hebert. An Integrated system for autonomous off-road navigation. In: Proceedings of the 1994 IEEE International Conference on Robotics and Automation. San Diego, USA, 1994: 414-419
[76] C. C. Lee. Fuzzy Logic in Control Systems: Fuzzy Logic Controller-Part I. IEEE Trans. on Systems. Man and Cybernetics, 1990． 20(2) : 404-418
[77] Ying H. A nonlinear fuzzy controller with linear control rules is the sum of a global two-dimensional multilevel relay and a local nonlinear proportional-integral controller [J] . Automatica, 1993． 29(2) : 499-505
[78] C. C. Lee. Fuzzy Logic in Control Systems: Fuzzy Logic Controller-Part Ⅱ. IEEE Trans. on Systems. Man and Cybernetics, 1990． 20(2) : 419-435
[79] B. Novel, G. Bastin and G. Campion. Modeling and control of non holonomic wheeled mobile robots. In: Proc. of the 1991． IEEE International ConJ-rence on Robotics and Automation. Sacramento. USA, Apr, 1991: 1130-1135
[80] IL C. Luo and M. G. Kay. Multisensor integration and fusion in intelligent systems. IEEE Trans, on Systems. Man ami Cybernetics, 1989． 19(5) : 901-931
[81] M. Maeda, Y. Maeda and S. Murakami. Fuzzy drive control of an autonomous mobile robot. Fuzzy Sets and Systems, 1991． 39: 195-204
[82] M. Nikravesh, A. E. Farell, T. G. Stanford. Dynamic neural network control for non-linear systems: optimal neural network structure and stability analysis. Chemical Engineering Journal, 1997． 68: 41-50
[83] M. Meng and A. C. Kak. Mobile robot navigation using neural networks and nonmetrical environment models. IEEE Control Systems, Oct., 1993: 30-39
[84] K. S. Narendra and K. Parthasarathy. Identification and control of dynamical systems using neural networks. IEEE Trans, on Neural Networks. 1990． 1(1) : 4-27
[85] S. Neusser and B. Hoefflinger. Parallel digital neural hardware for controller design. Mathematics and Computers in Simulation, 1996． 41(1-2) : 149-160
[86] Ying H.et al. Fuzzy control theory:the nonlinear case [J] . Automatica, 1990． 26(2) : 513-520
[87] D. H. Nguyen and B. Widrow. Neural networks for self-learning control systems. IEEE Control Systems Magzine, Apr, 1990: 18-23
[88] G. D. Nicolao, L. Magni and R. Scattolini. Stabilizing predictive control of nonlinear ARX Models. Automatics, 1997, 33(9) : 1691-1697
[89] B. Novel, G. Campion and G.Bastin. Control of Nonholonomic wheeled mobile robots by
state feedback linearization. The International Journal of Robotics Reasearch , 1995． 14(6) : 543-559
[90] Ju M S,et al. Design of adaptive fuzzy controls based on natural control laws [J] . Fuzzy Sets and System, 1996． 81(2) : 191-204
[91] H. Peng and M. Tomimaka. Preview control for vehicle lateral guidance in highway automation. Journal of Dynamic Systems. Measurement, and Control, Dec, 1993． 115: 679-686
[92] M. Piasecki. Mobile robot localization by fuzzy logic fusion of multisensor data. Robotics and Autonomous Systems, 1994． 12: 155-162
[93] D. A. Reece and S. Sharer. A computational model of driving for autonomous vehicles. CMU Technical Report, 1991． CMU-CS-91-122
[94] P. Reignier. Fuzzy logic techniques for mobile robot obstacle avoidance. Robotics and Autonomous Systems, 1994． 12: 143-153
[95] 吴超仲，王春燕，严新平，宋飞．自动公路系统磁道钉编码研究．2001海峡两岸智 能运输系统学术研讨会论文集，2002年5月，北京
[96] J. Theocharis and G. Vachtsevanos. Adaptive fuzzy neural networks as identifiers of discrete-time nonlinear dynamic systems. Journal of Intelligent and Robotic Systems, 1996． 17: 119-168
[97] C.Thorpe, M. Hebert, T. Kanade and S. Sharer. Toward autonomous driving: The CMU navlab Partt-Perception. IEEE Expert, Aug, 1991: 31-42
[98] Slotine J E, et al. Adaptive sliding controller synthesis for nonlinear systems [J] . Int. J.Control, 1986． 43(6) : 1631-1651
[99] C. Thorpe, M. Hebert, T Kanade and S. Shafer. Toward autonomous driving: The CMU navlab Part Ⅱ-Architecture and systems. IEEE Expert, Aug, 1991: 43-52
[100] C. Thorpe and T. Kanade. Third Annual Report for Perception for Outdoor Navigation. CMU Technical Report, 1992． CMU-RI-TR-92-16
[101] X. Yang,, M. Guo, K. He and B. Zhang. The Study on the Model of the Autonomous Mobile Robot Based on Neural Networks. In: Proceedings of 5th International Conference on Intelligent, 4utonomous Systems: Sapporo, Japan. Jun., 1998: 132-139
[102] Berenji H R and Khedkar P. Learning and turning fuzzy logic controllers through reinforcements. IEEE Trans.Neural Netwoks, 1992． 3(5) : 724-740
[103] Antsaklis P J. Intelligents learning control. IEEE Control Syst.Mag, 1995． 15(3) : 5-7
[104] 王立新著．自适应模糊系统与控制-设计与稳定性分析．北京：国防工业出版社， 1995
[105] Layne J R and Passino K M. Fuzzy model reference learning control [J] . .Int.& Fuzzy
Syst, 1996. 4(1): 33-47
【106】Carpenter G A, Grossberg S, et al. Fuzzy ART map: a neural network architecture for incremental supervised learning of analog multidimensional maps. IEEE Trans. Neural Netwoks, 1992. 3(5): 698～713
【107】吴超仲，王春燕，杨利兵，刘清彬，严新平，宋飞．智能公路系统车道保持模糊控制研究．公路交通科技，2002．Vol.19，No.2：131～133
【108】Jang J S R. Self-learning fuzzy controllers based on temporal back propagation, IEEE Trans. Neural Networks 1992. 3(5): 714～723
【109】Goldberg D E. Genetic algorithms and rule learning in dynamic system control. Proc. 1st Int. Conf. Genetic Algorithms, 1985: 8～15
【110】Lin C T and Lu Y C. A neural fuzzy system with fuzzy supervised learning. IEEE Trans. Syst., Man, and Cybern., 1906, 26(5): 744～763
【111】Parlos A, et al. An accelerated learning algorithm for multilayer perception networks. IEEE Trans. Neural Netwokrs, 1994. 5(3): 493～497
【112】梁化楼，戴贵亮．人工神经网络与遗传算法的结合：进展与展望．电子学报，1995．23(10)：265～273
【113】刘增良，刘有才．模糊逻辑与神经网络理论研究与探索．北京：北京航空航天大学出版社，1996，189～217
【114】Chen S B, Wu L and Wang Q L. Self-learning fuzzy neural networks for control of uncertain systems with time delays. IEEE Trans. Syst., Man, and Cybern., 1997. 27(1): 142～148
【115】Heskes T and Wiegerinck W. A theoretical comparison of batch-mode, on-line, cyclic and almost-cyclic learning. IEEE Trans. Neural Netwoks, 1996. 7(4): 919～924
【116】Karayiannis N B and Venetsanopoulos A N. Efficient learning algorithms for neural networks (ELEANNE). IEEE Trans. Syst., Man, and Cybem., 1993. 23(5): 1372～1383
【117】廖俊，林建亚，任德祥．模糊神经网络快速学习算法的研究．控制与决策，1997．12(5)：606～609
【118】乔长阁．用于数字式细胞神经网络设计的学习算法．电子科学学刊，1997．19(3)：403～406
【119】周旭东，王国栋．双向规则小脑模型神经网络CMAC学习控制．控制与决策，1997，12(2)
【120】Wu J C, et al. A sliding-model approach to fuzzy control design [J]. IEEE Trans. on Control Systems Technology, 1996. 4(2): 141～151
【121】罗中，谢永斌，朱重光．CMAC学习过程收敛性的研究．自动化学报，1997．23(4)：455～461
【122】Ishibuchi H, Fujioka R and Tanaka H. Neural networks that learn from fuzzy if-then
rules. IEEE Trans.Fuzzy Systems, 1993. 1(2): 85～97
【123】章云等．一种广义模糊神经网络的参数解藕学习算法．控制与决策，1997．12(5)：622～624
【124】Lofti A and Tsoi A C. Learning fuzzy inference systems using an adaptive membership function scheme. IEEE Trans.Syst.,Man,and Cybern., 1996. 26(2): 326～331
【125】Luciano A M and Savastano M. Fuzzy identification of systems with unsupervised learning. IEEE Trans. Syst., Man, and Cybern, 1997. 27(1): 138～141
【126】Kwang W A and Passino K M. Dynamically focused fuzzy learning control. IEEE Trans. Syst., Man, and Cybern, 1996. 26(1): 53～74
【127】吴超仲，严新平．变区域多层遗传算法的研究．计算机工程与科学，2001．Vol.23，No.3：66～69
【128】Qiao L, et al. Self-supervised learning algorithm of environment recognition in driving vehicle. IEEE Trans.Syst.,Man,and Cybern., 1996. 26(6): 843～850
【129】Yang J, Xu Y and Chen C S. Hi, man action learning via Hidden Markov model. IEEE Trans. Syst., Man, and Cybern, 1997. 27(1): 34～44
【130】Rouveird C and Alberts P. A knowledge-level model of a configurable learning system. IEEE Expert Intell. Syst. & Their Appli, 1996. 11(4): 50～58
【131】陈善本等．不确定对象的人工神经网络自学习控制方法．自动化学报，1997．23(1)：112～115
【132】Luo Z. On the convergence of LMS algorithm with adaptive learning rate for linear feedforword netwokrs. Nerual Computation, 1991. 3(2): 226～245
【133】吴超仲，严新平．一种多层遗传算法的提出与实现．武汉交通科技大学学报，2000．Vol.24，No.4：383～387
【134】Lin C T and Lee C S G. Reinforcement structure/parameter leraning for neural-network-based fuzzy logic control systems. IEEE Trans.Fuzzy Systems, 1994. 2(1): 46～63
【135】Lin C J and Lin C T. Reinforcement learning for an ART-based fuzzy adaptive learning control network. IEEE Trans.Neural Networks, 1996. 7(3): 709～731
【136】吴超仲，杨利兵，严新平．自动公路系统及其信息融合模型研究．第四届全国交通运输领域青年学术会议论文集．北京：中国民航出版社，2001．1003～1006
【137】Carpenter G A, Grossberg S and Rosen D B. Fuzzy ART: fast stable learning and categorization of analog patterns by an adaptive resonance system. Neural Netwoks, 1991. 4(5): 759～771
【138】Lin C J and Lin C T. An ART-based fuzzy adaptive learning control network. Proc. IEEE Int. Conf. Fuzzy System, Orlando, Florida, 1994: 1～6
【139】WU Chao-zhong, WANG Chun-yan, YAN Xin-ping, SONG Fei. Information Fusion
of Lateral Displacement Measurement in Automated Steering Control System. 9th World Congress on Intelligent Transport Systems
【140】Tiang T and li Y. Generalized Defuzzification Strategies and their parameter learning procedures. IEEE Trans.Fuzzy Systems, 1996. 4(1): 64～71
【141】Choi D I and Park S H. Self-Creating and organizing neural network. IEEE Trans. Neural Networks, 1994. 5(4): 561～575
【142】Saridis G N. Analytic formulation of the principle of increasing precision with decreasing intelligence for intelligent machines. Automatica, 1989. 25(3): 461-467
【143】Mode M C and Saridis G N. A Boltzmann machine for the organization of intelligent machines. IEEE Trans.Syst..Man.and Cybern, 1990. 20(5): 1094～1102
【144】蔡金狮．动力学系统辨识与建模．北京：国防工业出版社，1991，16～18
【145】孙增圻．智能控制理论与技术．北京：清华大学出版社，1997
【116】齐国光，郭木河，马少平，归圣华．轮式机器人最优控制的研究．机器人，1996．18(1)：21～27
【147】石海东．室外移动机器人规划决策技术的研究．[硕士学位论文]．北京：清华大学计算机科学与技术系，1996
【148】郭木河，何克，忠陶西平，敖旭辉．室外智能机器人的局部规划与导航控制方法．清华大学学报，1995．35(5)：7～13
【149】余志生．汽车理论(第3版)．北京：机械工业出版社，2000
【150】杨利兵．智能公路系统的车辆横向控制试验研究．[硕士学位论文]．武汉：武汉理工大学能源与动力工程学院，2002
【2】Guldner J. Patwardhan S. Tan H.-S. and Zhang W.-B. Coding of Magnetic Markers for Demonstration of Automated Highway System. Transportation Research Board Anuual Meeting, Washington. DC, USA, 1997
【3】Tan H.-S. Guldner J.Etc The development of an Automated Steering Vehical Based on Roadway Magnets-A Case Study of Mechatronic System design. IEEE/ASME Transportation on Mechatronics, 1999
【4】Han-Shue Tan, Bénédicte Bougler. Vehicle Lateral Warning. Guidance and Control Based on Magnetic Markers:PATH Report of AHSRA Smart Cruise 21 Proving Tests. California PATH Working Paper. UCB-ITS-PWP-2001-6
【5】杨欣欣．智能移动机器人导航与控制技术研究：[博士学位论文]．北京：清华大学计算机科学技术系，1999
【6】吴晖．ALV路径跟踪控制方法研究：[博士学位论文]．长沙：国防科学技术大学，1997
【7】王荣本．图像识别自动引导车辆系统研究：[博士学位论文]．吉林工业大学，1995
【8】Lin C J and Lin C T. Reinforcement learning for an ART-based fuzzy adaptive learning control network. IEEE Trans. Neural Networks, 1996. 7(3): 709～731
【9】Rouveird C and Alberts P. A knowledge-level model of a configurable learning system. IEEE Expert Intell. Syst. & Their Appli..1996. 11(4): 50～58
【10】Yang J.Xu Y and Chert C S. Human action learning via Hidden Markov model. IEEE Trans. Syst., Man. and Cybern, 1997. 27(1): 34～44
【11】Layne J R and Passino K M. Fuzzy model reference learning control. J. Int. & Fuzzy Syst., 1996. 4(1): 33～47
【12】Chen S B. Wu L and Wang Q L. Self-learning fuzzy neural networks for control of uncertain systems with time delays. IEEE Trans.Syst., Man, and Cybern., 1997. 27(1): 142～148
【13】Chang Tang-Hsein. Vehicle Lateral Control Modeling for Steering Automation in the ADVANCE-F System. Transportation of Institute of Measurement and Control, 1994. Vol.18, No.3: 149～159
【14】Feng K.-T., Tan H.-S., Etc. Look-aheah human-machine Interface for Assistance of Manual Vehicle Steering. Vehicle system Dynamics, 1996. 26(4): 243～269
【15】渡边克也．ITS(高度道路交通)动向将来．大阪：关西经济研究中心，1999．9
【16】林鹰．新世纪的自动化高速公路．交通与运输，2000．2
【17】张智文，魏凤，侯福深，等．中国智能运输系统体系框架．第4届亚太地区ITS会议论文集
【18】谭业江，等译．车辆导航系统在日本的应用．国外公路，1997．17(1)
【19】严新平，吴超仲．中国智能运输系统发展现状与趋势．交通企业管理，2001．No.2：57～59
【20】纪寿文，王荣本，初秀民，徐友春．国内外智能车辆研究进展．中国交通研究与探索．北京：人民交通出版社，1999
【21】张友坤，王荣本，王志中，张国川．一种基于机器视觉导航的高速智能车辆总体设计概述．中国交通研究与探索．北京：人民交通出版社，1999
【22】杨利兵，吴超仲，严新平．自动公路系统国内外研究现状．第四届全国交通运输领域青年学术会议论文集．北京：中国民航出版社，2001．998～1002
【23】M. Agarwat. Combining neural and conventional paradigms for modelling, prediction and control. International Journal of Systems Science, 1997. 28(1): 65～81
【24】Guo Konghui, Guan Xin. Modelling of driver/vehicle directional control ystem. [J]. Vehicle System Dynamics, 1993. 22: 141～184.
【25】N. Amann, D. H. Owens and E. Rogers. Predictive optimal iterative control. International Journal of Control, 1998. 69(2): 203～226
【26】T. A. Badgwell. Robust model predictive control of stable linear systems. International Journal of Control, 1997. 68(4): 797～818
【27】Musavi M T, Chan K H, Hummels D M, et al. A Probabilitic Model for Evaluation of Neural Network Classifiers. Pattern Recognition, 1992. 25(10): 1241～1251
【28】R. Balakrishna and A. Ghosal. Modelling of slip for wheeled mobile robots. IEEE Trans. on Robotics and Automation, 1995. 11(D): 126～138
【29】F. D. Boyden and S. A. Velinsky. Dynamic modeling of wheeled mobile robots for high load application. In: Proc. of 1994 IEEE International Conference on Robotics and Automation, San Diego,, USA, 1994. 3071～3078
【30】吴翊，吴孟达，成礼智编著．数学建模的理论与实践．湖南：国防科技大学出版社，1999
【31】N. M. Charkari, K. Ishii and H. Mori. Sensory selection in vehicle detection and avoidance. Robotics and Autonomous Systems, 1996. 17:3～13
【32】E. D. Dickmanns, B. Mysliwe～and T. Christians. An integrated spatio-temporal approach to automatic visual guidance of autonomous vehicles. IEEE Trans. on System, Man and
Cybernetics, 1990. 20(6): 1273～1283
【33】J.Manyika and H.Durrant-Whyte. Data Fusion and Sensor Management: A Decentralized Information-Theoretic Approach. Ellis Horwood Limited, 1994
【34】E. Freund and R. Mayr. Nonlinear path control in automated vehicle guidance. IEEE Trans. on Robotics and Automation, 1997. 13(1): 49～60
【35】M. J. Grimble. Long range predictive optimal control law with guaranteed stability for process control applications. Journal of Dynamic Systems. Measurement, and Control. Dec., 1993. 115: 600～610
【36】H. Hu and M. Brady. A parallel processing architecture for sensor-based control of intelligent mobile robots. Robotics and Autonomous Systems, 1996. 17: 235～257
【37】K. J. Hunt, D. Sbarbaro, R. Zhikowski and P. J. Gawthrop. Neural networks for control systems-A survey, dutomatica, 1992. 28(6): 1083～1112
【38】A. J. Kelly. An intelligent predictive controller for autonomous vehicles. CMU Robotics Institute Technical Report, CMU-RI-TR-94-20, 1994
【39】AHS公道实验成功(ITS实用化试验)．ITS-Review, 1997. Vol. 2：2
【40】ITS-动出高度道路交通．三菱综合研究所ITS推进室．东京：日刊工业新闻社，1998．12
【41】李洪志．信息融合技术．北京：国防工业出版社，1996
【42】J.Manyika and H.Durrant-Whyte. Data fusion and sensor management:A Decentralized Information-Theoretic Approach. Ellis Horwood Linited, 1994
【43】康耀红．数据融合理论与应用．西安：西安电子科技大学出版社，1997
【44】李兆杰等．多传感器集成融合技术．传感器技术，1996(6)：1～45
【45】黄澍川．用多传感器融合系统识别摩擦副的磨损状态．摩擦学学报，1996(4)：367～370
【46】刘同明．基于证据理论模糊推理的多传感器信息融合海上目标识别．模式识别与人工智能，1999(1)：25～31
【47】徐从富等．Dempster-Shafer证据推理方法理论与应用综述．模式识别与人工智能，1999(4)：424～429
【48】王国宏等．基于模糊综合的目标识别时空数据融合算法．模式识别与人工智能，1997(1)：61～67
【49】唐文彬等．一种多传感器数据融合目标识别新方法．传感器技术，1998(3)：23～24
【50】张彦铎等．多传感器信息融合及在智能故障诊断中的应用．传感器技术．1999(2)，18～22
【51】黄瑛等．多传感器数据融合系统的软件实现．传感器技术，1998(6)：9～11
【52】王祁等．分布式多传感器数据融合．传感器技术，1997(5)：8～10
【53】王婷杰等．一种基于模糊理论的一致性数据融合方法．传感器技术，1999(6)：50～53
【54】金锋．数据融合与传感器建模．传感器技术，1999(6)：47～49
【55】马洪超等．地学数据融合技术综述．地质科技情报，1999(3)
【56】李军等．影像局部直方图匹配滤波技术用于遥感影像数据融合．测绘学报，1999(8)
【57】林明星等．基于神经网络的多传感器信息融合技术．新技术新工艺，1999(5)
【58】苏惠敏等．驾驶员助手系统的关键技术—信息融合．测控技术，1999(1)
【59】滕召胜等．基于数据融合的智能水分快速测定仪．1999(10)
【60】谷立臣等．基于信息融合的传感器故障检测方法．西安建筑科技大学学报，1999(3)
【61】张晓明等．基于神经网络的传感器融合和综合在刀具状态检测中的应用．组机床与自动化加工技术，1993(12)：15～23
【62】H.Tahani and J.M.Keller. Information fusion in computer vision using the fuzzy integral. IEEE Trans. Syst, Man and Cyberne, 1990. Vol.20, No.30: 733～741
【63】吴超仲，严新平．基于遗传算法的信息融合在摩擦学组合故障诊断中的应用．摩擦学学报，2001. No.3: 301～304
【64】P. J. Antsaldis. Neural networks in control systems. IEEE. Control Systems Magzine, Apr. 1990: 3～5
【65】T. A. Badgwell. Robust stability conditions for SISO model predictive control algorithms. Automatica, 1997. 33(7): 1357～1361
【66】Lee C C. Fuzzy logic in control systems: Fuzzy logic control(Part Ⅱ). IEEE Trans Syst. Man, Cybern, 1990. 20: 419～435
【67】D. Feng and B. H. Krogh. Satisficing Feedback Strategies for Local Navigation of Autonomous Mobile Robot. IEEE Transaction of Systems, Man and Cybernltics, 1990. 20(6): 1383～1395
【68】Buckley J J. Fuzzy logic controllers are universal approximations. Fuzzy Sets Syst, 1993. 58: 273～278
【69】T. Fukuda and T. Shibata. Theory and applications of neural networks industrial control systems. IEEE Trans. on Industrial Electronics, 1992. 39(6): 472～489
【70】Kim J M, Mendel J M. Fuzzy. basis functions: Comparisons with other basis functions. IEEE Trans Fuzzy Syst, 1995. 2(3): 158～168
【71】S. Horikawa, T. Furuhashi and Y. Uchikawa. On fuzzy modeling using fuzzy neural networks with the back-propagation algorithm. IEEE Trans. on Neural Networks, 1992. 3(5): 801～806
【72】T. C. Hu, A. B. Kahng and G. Robins. Optimal robust path planning in general environments. IEEE Trans. on Robotics and automation, 1993. 9(6): 775～784
【73】Wang L X. Stable adaptive fuzzy control of nonlinear systems. IEEE Trans Fuzzy Syst, 1993. 5(1): 146～155
[74] A. J. Kelly.A feedforward control approach to the local navigation problem for autonomous vehicles. CMU Robotics Institute Technical Report, 1994． CMU-RI-TR-94-17
[75] D. Langer, S.K.Rosenblatt, M. Hebert. An Integrated system for autonomous off-road navigation. In: Proceedings of the 1994 IEEE International Conference on Robotics and Automation. San Diego, USA, 1994: 414-419
[76] C. C. Lee. Fuzzy Logic in Control Systems: Fuzzy Logic Controller-Part I. IEEE Trans. on Systems. Man and Cybernetics, 1990． 20(2) : 404-418
[77] Ying H. A nonlinear fuzzy controller with linear control rules is the sum of a global two-dimensional multilevel relay and a local nonlinear proportional-integral controller [J] . Automatica, 1993． 29(2) : 499-505
[78] C. C. Lee. Fuzzy Logic in Control Systems: Fuzzy Logic Controller-Part Ⅱ. IEEE Trans. on Systems. Man and Cybernetics, 1990． 20(2) : 419-435
[79] B. Novel, G. Bastin and G. Campion. Modeling and control of non holonomic wheeled mobile robots. In: Proc. of the 1991． IEEE International ConJ-rence on Robotics and Automation. Sacramento. USA, Apr, 1991: 1130-1135
[80] IL C. Luo and M. G. Kay. Multisensor integration and fusion in intelligent systems. IEEE Trans, on Systems. Man ami Cybernetics, 1989． 19(5) : 901-931
[81] M. Maeda, Y. Maeda and S. Murakami. Fuzzy drive control of an autonomous mobile robot. Fuzzy Sets and Systems, 1991． 39: 195-204
[82] M. Nikravesh, A. E. Farell, T. G. Stanford. Dynamic neural network control for non-linear systems: optimal neural network structure and stability analysis. Chemical Engineering Journal, 1997． 68: 41-50
[83] M. Meng and A. C. Kak. Mobile robot navigation using neural networks and nonmetrical environment models. IEEE Control Systems, Oct., 1993: 30-39
[84] K. S. Narendra and K. Parthasarathy. Identification and control of dynamical systems using neural networks. IEEE Trans, on Neural Networks. 1990． 1(1) : 4-27
[85] S. Neusser and B. Hoefflinger. Parallel digital neural hardware for controller design. Mathematics and Computers in Simulation, 1996． 41(1-2) : 149-160
[86] Ying H.et al. Fuzzy control theory:the nonlinear case [J] . Automatica, 1990． 26(2) : 513-520
[87] D. H. Nguyen and B. Widrow. Neural networks for self-learning control systems. IEEE Control Systems Magzine, Apr, 1990: 18-23
[88] G. D. Nicolao, L. Magni and R. Scattolini. Stabilizing predictive control of nonlinear ARX Models. Automatics, 1997, 33(9) : 1691-1697
[89] B. Novel, G. Campion and G.Bastin. Control of Nonholonomic wheeled mobile robots by
state feedback linearization. The International Journal of Robotics Reasearch , 1995． 14(6) : 543-559
[90] Ju M S,et al. Design of adaptive fuzzy controls based on natural control laws [J] . Fuzzy Sets and System, 1996． 81(2) : 191-204
[91] H. Peng and M. Tomimaka. Preview control for vehicle lateral guidance in highway automation. Journal of Dynamic Systems. Measurement, and Control, Dec, 1993． 115: 679-686
[92] M. Piasecki. Mobile robot localization by fuzzy logic fusion of multisensor data. Robotics and Autonomous Systems, 1994． 12: 155-162
[93] D. A. Reece and S. Sharer. A computational model of driving for autonomous vehicles. CMU Technical Report, 1991． CMU-CS-91-122
[94] P. Reignier. Fuzzy logic techniques for mobile robot obstacle avoidance. Robotics and Autonomous Systems, 1994． 12: 143-153
[95] 吴超仲，王春燕，严新平，宋飞．自动公路系统磁道钉编码研究．2001海峡两岸智 能运输系统学术研讨会论文集，2002年5月，北京
[96] J. Theocharis and G. Vachtsevanos. Adaptive fuzzy neural networks as identifiers of discrete-time nonlinear dynamic systems. Journal of Intelligent and Robotic Systems, 1996． 17: 119-168
[97] C.Thorpe, M. Hebert, T. Kanade and S. Sharer. Toward autonomous driving: The CMU navlab Partt-Perception. IEEE Expert, Aug, 1991: 31-42
[98] Slotine J E, et al. Adaptive sliding controller synthesis for nonlinear systems [J] . Int. J.Control, 1986． 43(6) : 1631-1651
[99] C. Thorpe, M. Hebert, T Kanade and S. Shafer. Toward autonomous driving: The CMU navlab Part Ⅱ-Architecture and systems. IEEE Expert, Aug, 1991: 43-52
[100] C. Thorpe and T. Kanade. Third Annual Report for Perception for Outdoor Navigation. CMU Technical Report, 1992． CMU-RI-TR-92-16
[101] X. Yang,, M. Guo, K. He and B. Zhang. The Study on the Model of the Autonomous Mobile Robot Based on Neural Networks. In: Proceedings of 5th International Conference on Intelligent, 4utonomous Systems: Sapporo, Japan. Jun., 1998: 132-139
[102] Berenji H R and Khedkar P. Learning and turning fuzzy logic controllers through reinforcements. IEEE Trans.Neural Netwoks, 1992． 3(5) : 724-740
[103] Antsaklis P J. Intelligents learning control. IEEE Control Syst.Mag, 1995． 15(3) : 5-7
[104] 王立新著．自适应模糊系统与控制-设计与稳定性分析．北京：国防工业出版社， 1995
[105] Layne J R and Passino K M. Fuzzy model reference learning control [J] . .Int.& Fuzzy
Syst, 1996. 4(1): 33-47
【106】Carpenter G A, Grossberg S, et al. Fuzzy ART map: a neural network architecture for incremental supervised learning of analog multidimensional maps. IEEE Trans. Neural Netwoks, 1992. 3(5): 698～713
【107】吴超仲，王春燕，杨利兵，刘清彬，严新平，宋飞．智能公路系统车道保持模糊控制研究．公路交通科技，2002．Vol.19，No.2：131～133
【108】Jang J S R. Self-learning fuzzy controllers based on temporal back propagation, IEEE Trans. Neural Networks 1992. 3(5): 714～723
【109】Goldberg D E. Genetic algorithms and rule learning in dynamic system control. Proc. 1st Int. Conf. Genetic Algorithms, 1985: 8～15
【110】Lin C T and Lu Y C. A neural fuzzy system with fuzzy supervised learning. IEEE Trans. Syst., Man, and Cybern., 1906, 26(5): 744～763
【111】Parlos A, et al. An accelerated learning algorithm for multilayer perception networks. IEEE Trans. Neural Netwokrs, 1994. 5(3): 493～497
【112】梁化楼，戴贵亮．人工神经网络与遗传算法的结合：进展与展望．电子学报，1995．23(10)：265～273
【113】刘增良，刘有才．模糊逻辑与神经网络理论研究与探索．北京：北京航空航天大学出版社，1996，189～217
【114】Chen S B, Wu L and Wang Q L. Self-learning fuzzy neural networks for control of uncertain systems with time delays. IEEE Trans. Syst., Man, and Cybern., 1997. 27(1): 142～148
【115】Heskes T and Wiegerinck W. A theoretical comparison of batch-mode, on-line, cyclic and almost-cyclic learning. IEEE Trans. Neural Netwoks, 1996. 7(4): 919～924
【116】Karayiannis N B and Venetsanopoulos A N. Efficient learning algorithms for neural networks (ELEANNE). IEEE Trans. Syst., Man, and Cybem., 1993. 23(5): 1372～1383
【117】廖俊，林建亚，任德祥．模糊神经网络快速学习算法的研究．控制与决策，1997．12(5)：606～609
【118】乔长阁．用于数字式细胞神经网络设计的学习算法．电子科学学刊，1997．19(3)：403～406
【119】周旭东，王国栋．双向规则小脑模型神经网络CMAC学习控制．控制与决策，1997，12(2)
【120】Wu J C, et al. A sliding-model approach to fuzzy control design [J]. IEEE Trans. on Control Systems Technology, 1996. 4(2): 141～151
【121】罗中，谢永斌，朱重光．CMAC学习过程收敛性的研究．自动化学报，1997．23(4)：455～461
【122】Ishibuchi H, Fujioka R and Tanaka H. Neural networks that learn from fuzzy if-then
rules. IEEE Trans.Fuzzy Systems, 1993. 1(2): 85～97
【123】章云等．一种广义模糊神经网络的参数解藕学习算法．控制与决策，1997．12(5)：622～624
【124】Lofti A and Tsoi A C. Learning fuzzy inference systems using an adaptive membership function scheme. IEEE Trans.Syst.,Man,and Cybern., 1996. 26(2): 326～331
【125】Luciano A M and Savastano M. Fuzzy identification of systems with unsupervised learning. IEEE Trans. Syst., Man, and Cybern, 1997. 27(1): 138～141
【126】Kwang W A and Passino K M. Dynamically focused fuzzy learning control. IEEE Trans. Syst., Man, and Cybern, 1996. 26(1): 53～74
【127】吴超仲，严新平．变区域多层遗传算法的研究．计算机工程与科学，2001．Vol.23，No.3：66～69
【128】Qiao L, et al. Self-supervised learning algorithm of environment recognition in driving vehicle. IEEE Trans.Syst.,Man,and Cybern., 1996. 26(6): 843～850
【129】Yang J, Xu Y and Chen C S. Hi, man action learning via Hidden Markov model. IEEE Trans. Syst., Man, and Cybern, 1997. 27(1): 34～44
【130】Rouveird C and Alberts P. A knowledge-level model of a configurable learning system. IEEE Expert Intell. Syst. & Their Appli, 1996. 11(4): 50～58
【131】陈善本等．不确定对象的人工神经网络自学习控制方法．自动化学报，1997．23(1)：112～115
【132】Luo Z. On the convergence of LMS algorithm with adaptive learning rate for linear feedforword netwokrs. Nerual Computation, 1991. 3(2): 226～245
【133】吴超仲，严新平．一种多层遗传算法的提出与实现．武汉交通科技大学学报，2000．Vol.24，No.4：383～387
【134】Lin C T and Lee C S G. Reinforcement structure/parameter leraning for neural-network-based fuzzy logic control systems. IEEE Trans.Fuzzy Systems, 1994. 2(1): 46～63
【135】Lin C J and Lin C T. Reinforcement learning for an ART-based fuzzy adaptive learning control network. IEEE Trans.Neural Networks, 1996. 7(3): 709～731
【136】吴超仲，杨利兵，严新平．自动公路系统及其信息融合模型研究．第四届全国交通运输领域青年学术会议论文集．北京：中国民航出版社，2001．1003～1006
【137】Carpenter G A, Grossberg S and Rosen D B. Fuzzy ART: fast stable learning and categorization of analog patterns by an adaptive resonance system. Neural Netwoks, 1991. 4(5): 759～771
【138】Lin C J and Lin C T. An ART-based fuzzy adaptive learning control network. Proc. IEEE Int. Conf. Fuzzy System, Orlando, Florida, 1994: 1～6
【139】WU Chao-zhong, WANG Chun-yan, YAN Xin-ping, SONG Fei. Information Fusion
of Lateral Displacement Measurement in Automated Steering Control System. 9th World Congress on Intelligent Transport Systems
【140】Tiang T and li Y. Generalized Defuzzification Strategies and their parameter learning procedures. IEEE Trans.Fuzzy Systems, 1996. 4(1): 64～71
【141】Choi D I and Park S H. Self-Creating and organizing neural network. IEEE Trans. Neural Networks, 1994. 5(4): 561～575
【142】Saridis G N. Analytic formulation of the principle of increasing precision with decreasing intelligence for intelligent machines. Automatica, 1989. 25(3): 461-467
【143】Mode M C and Saridis G N. A Boltzmann machine for the organization of intelligent machines. IEEE Trans.Syst..Man.and Cybern, 1990. 20(5): 1094～1102
【144】蔡金狮．动力学系统辨识与建模．北京：国防工业出版社，1991，16～18
【145】孙增圻．智能控制理论与技术．北京：清华大学出版社，1997
【116】齐国光，郭木河，马少平，归圣华．轮式机器人最优控制的研究．机器人，1996．18(1)：21～27
【147】石海东．室外移动机器人规划决策技术的研究．[硕士学位论文]．北京：清华大学计算机科学与技术系，1996
【148】郭木河，何克，忠陶西平，敖旭辉．室外智能机器人的局部规划与导航控制方法．清华大学学报，1995．35(5)：7～13
【149】余志生．汽车理论(第3版)．北京：机械工业出版社，2000
【150】杨利兵．智能公路系统的车辆横向控制试验研究．[硕士学位论文]．武汉：武汉理工大学能源与动力工程学院，2002
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |