非完整移动机器人系统的智能鲁棒控制研究
|
摘要
轮式移动机器人系统是一种典型的多输入多输出的非完整约束动力学系统,同时也属于欠驱动非线性系统。一般的光滑反馈控制律无法应用于这样的系统,尤其在受负载变化、摩擦、外加干扰等不确定因素影响时,更难以找到一种通用的、效果较好的控制方法实现其运动控制。因此,不确定性非完整移动机器人系统的控制问题已经引起了国内外控制界的高度重视和广泛关注。
本文对国内外关于非完整性轮式移动机器人运动控制方面的研究成果进行了深入的分析,总结了常用的方法及现存的问题,并在此基础上完成了非完整性轮式移动机器人的轨迹跟踪控制,并对基于轨迹跟踪拓展的机器人编队控制进行了研究。主要开展了以下工作：
首先,全面地介绍了非完整约束及非完整动力学系统的概念,建立了典型的非完整轮式移动机器人的数学模型,描述了其基本的性质及运动控制形式。针对具有负载变化及外加干扰的非完整轮式移动机器人轨迹跟踪控制问题,进行了滑模控制设计,利用滑模控制克服机器人的参数与非参数不确定性；并在此基础上提出了非完整移动机器人的自适应模糊滑模动力学控制算法。其中,采用自适应分流运动学控制解决了由于大范围的初始位姿偏差变化而引起的速度跳变问题；同时,通过带有自适应调节算法的模糊控制来调节滑模控制的增益,增强了算法对随机不确定性的适应能力,并消除了滑模控制中的输入抖振现象。
其次,进一步对无精确模型的非完整移动机器人的轨迹跟踪问题进行研究,基于径向基神经网络的万能逼近特性,将神经网络与滑模控制相结合,设计了一种双自适应神经滑模混合控制律,采用一个自适应神经网络逼近机器人系统的未建模部分,另一个自适应神经网络用来调节滑模开关控制的增益部分,达到消除抖振的同时实现了无精确模型的移动机器人精确的轨迹跟踪控制。
再次,提出了一种基于遗传优化的机器人递归模糊神经滑模控制。采用分段自适应变异概率改进了实数编码遗传算法的变异操作,利用含此变异操作的遗传算法实现了运动学控制器参数的优化选取。通过所设计的多输入多输出的动态递归模糊神经网络对系统动态非线性不确定部分进行在线估计,使不确定性估计误差大大减小；通过与自适应鲁棒控制器结合应用,在克服移动机器人不确定性干扰、滑模控制消抖方面取得了很好的效果,保障了高精度的轨迹跟踪。
最后,将对单机器人的轨迹跟踪扩展到对多非完整移动机器人的编队控制。根据Leader-follower的基本原理,同时考虑单机器人动力学及编队动力学二者的不确定性对编队控制的影响,分动力学不含与含有驱动器模型两种情况进行了控制器设计。针对前者,提出了一种基于神经网络的移动机器人编队自适应滑模控制,该方法采用径向基神经网络与滑模控制相结合的方式,既去除了滑模控制的抖振,也克服了单机器人及编队动力学不确定性对编队的干扰；针对后者含驱动器动力学的移动机器人编队自适应控制问题,通过Backstepping方法将含驱动器动力学的机器人动力学部分引入到控制中来,用RBF神经网络对编队中所存在的多种不确定性进行了建模,实现了含驱动器动力学的多非完整移动机器人的编队控制。
Wheeled mobile robot is not only a typical multiple-input multiple-output dynamic system with nonholonomic constraints but also an underactuated nonlinear system. The common smooth feedback control law can not apply to this kind of systems, especially when the system is affected by load variation, friction, external disturbances and other uncertainties, it is very difficult to find a common and effective control approach to achieve motion control. Therefore, the control problem of nonholonomic mobile robot system with uncertainties has attracted great attention and concern of domestic and foreign experts in control field.
In this dissertation, domestic and overseas researches on motion control of nonholonomic wheeled mobile robot are analyzed thoroughly, and the common methods and existing problems are summarized. Based on the former studies, tracking control of nonholonomic wheeled mobile robot is implemented, and formation control of multiple robots based on trajectory tracking is studied. Main work of this paper includes the following issues:
Firstly, the concepts of the nonholonomic constraints and dynamic nonholonomic systems are introduced comprehensively, and a typical nonholonomic wheeled mobile robot model which describes the basic properties and forms of motion control is established. As for the trajectory tracking control problem of nonholonomic wheeled mobile robot with load changes and external interference, the sliding mode controller which overcomes the robot parameters and non-parametric uncertainty is designed; the adaptive fuzzy sliding mode dynamics control algorithm of nonholonomic mobile robot is proposed based on that. Adaptive shunting kinematics is applied to solve the problem of controller speed jump caused by large changes in the initial position error; at the same time, the gain of sliding mode control is adjusted by using adaptive fuzzy control algorithm, which not only enhances the ability of random uncertainty adaption, but also eliminates the input chattering of sliding mode control.
Secondly, further research is conducted on trajectory tracking of nonholonomic mobile robot without an accurate model. Based on the analyses of universal approximation properties of the neural network radial basis neural network, a double-adaptive neural sliding mode hybrid control law is designed through combining the neural network with sliding mode control. An adaptive neural network model is applied to approximate the unmodeled part of the robot system, and another adaptive neural network is used to adjust the gain of sliding mode switch control, which eliminate the chattering of sliding mode control and achieve a precise tracking control of the mobile robot without an accurate model.
Thirdly, an adaptive dynamic recurrent fuzzy neural network sliding control algorithm based on genetic optimization is proposed. Segmented adaptive mutation operator is applied to improve the mutation operation, and the optimal parameters of the kinematics controller are selected by using genetic algorithm including the above mutation operation. The dynamic multi-input multi-output recurrent fuzzy neural network is designed to achieve online estimation of dynamic nonlinear uncertain part of mobile robot system, which makes the estimation error of the uncertainty reduce greatly; the uncertainty interference of the mobile robot are decreased excellently and the input chattering of sliding mode control is eliminated through combining the designed neural network with the adaptive robust controller, hereby ensuring the fine accuracy of trajectory tracking.
Finally, the studies are extended from trajectory tracking of a single robot to formation control of multiple nonholonomic mobile robots. Based on the basic principles of leader-follower, taking account of the impact on the formation control caused by the uncertainty of single robot's dynamics and the formation dynamics, the controller is designed separately based on the conditions that actuator mode included in the dynamics model and not included. As for dynamic model, an adaptive sliding mode formation control which combines neural network with sliding mode control is proposed to remove the chattering of sliding mode control and overcome the disturbances of formation caused by the uncertainties of a single robot and formation dynamics. The other case is adaptive formation control problem of mobile robot including actuator dynamics. By using backstepping methodology, the dynamics of the robot including actuator model is introduced to the controller. RBF neural network is used to model uncertainties of formation control including changes in the load, friction (random change), external disturbances and unmodeled part of the formation dynamics. The formation control of multi-nonholonomic mobile robot including actuator dynamics is implemented.
本文对国内外关于非完整性轮式移动机器人运动控制方面的研究成果进行了深入的分析,总结了常用的方法及现存的问题,并在此基础上完成了非完整性轮式移动机器人的轨迹跟踪控制,并对基于轨迹跟踪拓展的机器人编队控制进行了研究。主要开展了以下工作：
首先,全面地介绍了非完整约束及非完整动力学系统的概念,建立了典型的非完整轮式移动机器人的数学模型,描述了其基本的性质及运动控制形式。针对具有负载变化及外加干扰的非完整轮式移动机器人轨迹跟踪控制问题,进行了滑模控制设计,利用滑模控制克服机器人的参数与非参数不确定性；并在此基础上提出了非完整移动机器人的自适应模糊滑模动力学控制算法。其中,采用自适应分流运动学控制解决了由于大范围的初始位姿偏差变化而引起的速度跳变问题；同时,通过带有自适应调节算法的模糊控制来调节滑模控制的增益,增强了算法对随机不确定性的适应能力,并消除了滑模控制中的输入抖振现象。
其次,进一步对无精确模型的非完整移动机器人的轨迹跟踪问题进行研究,基于径向基神经网络的万能逼近特性,将神经网络与滑模控制相结合,设计了一种双自适应神经滑模混合控制律,采用一个自适应神经网络逼近机器人系统的未建模部分,另一个自适应神经网络用来调节滑模开关控制的增益部分,达到消除抖振的同时实现了无精确模型的移动机器人精确的轨迹跟踪控制。
再次,提出了一种基于遗传优化的机器人递归模糊神经滑模控制。采用分段自适应变异概率改进了实数编码遗传算法的变异操作,利用含此变异操作的遗传算法实现了运动学控制器参数的优化选取。通过所设计的多输入多输出的动态递归模糊神经网络对系统动态非线性不确定部分进行在线估计,使不确定性估计误差大大减小；通过与自适应鲁棒控制器结合应用,在克服移动机器人不确定性干扰、滑模控制消抖方面取得了很好的效果,保障了高精度的轨迹跟踪。
最后,将对单机器人的轨迹跟踪扩展到对多非完整移动机器人的编队控制。根据Leader-follower的基本原理,同时考虑单机器人动力学及编队动力学二者的不确定性对编队控制的影响,分动力学不含与含有驱动器模型两种情况进行了控制器设计。针对前者,提出了一种基于神经网络的移动机器人编队自适应滑模控制,该方法采用径向基神经网络与滑模控制相结合的方式,既去除了滑模控制的抖振,也克服了单机器人及编队动力学不确定性对编队的干扰；针对后者含驱动器动力学的移动机器人编队自适应控制问题,通过Backstepping方法将含驱动器动力学的机器人动力学部分引入到控制中来,用RBF神经网络对编队中所存在的多种不确定性进行了建模,实现了含驱动器动力学的多非完整移动机器人的编队控制。
Wheeled mobile robot is not only a typical multiple-input multiple-output dynamic system with nonholonomic constraints but also an underactuated nonlinear system. The common smooth feedback control law can not apply to this kind of systems, especially when the system is affected by load variation, friction, external disturbances and other uncertainties, it is very difficult to find a common and effective control approach to achieve motion control. Therefore, the control problem of nonholonomic mobile robot system with uncertainties has attracted great attention and concern of domestic and foreign experts in control field.
In this dissertation, domestic and overseas researches on motion control of nonholonomic wheeled mobile robot are analyzed thoroughly, and the common methods and existing problems are summarized. Based on the former studies, tracking control of nonholonomic wheeled mobile robot is implemented, and formation control of multiple robots based on trajectory tracking is studied. Main work of this paper includes the following issues:
Firstly, the concepts of the nonholonomic constraints and dynamic nonholonomic systems are introduced comprehensively, and a typical nonholonomic wheeled mobile robot model which describes the basic properties and forms of motion control is established. As for the trajectory tracking control problem of nonholonomic wheeled mobile robot with load changes and external interference, the sliding mode controller which overcomes the robot parameters and non-parametric uncertainty is designed; the adaptive fuzzy sliding mode dynamics control algorithm of nonholonomic mobile robot is proposed based on that. Adaptive shunting kinematics is applied to solve the problem of controller speed jump caused by large changes in the initial position error; at the same time, the gain of sliding mode control is adjusted by using adaptive fuzzy control algorithm, which not only enhances the ability of random uncertainty adaption, but also eliminates the input chattering of sliding mode control.
Secondly, further research is conducted on trajectory tracking of nonholonomic mobile robot without an accurate model. Based on the analyses of universal approximation properties of the neural network radial basis neural network, a double-adaptive neural sliding mode hybrid control law is designed through combining the neural network with sliding mode control. An adaptive neural network model is applied to approximate the unmodeled part of the robot system, and another adaptive neural network is used to adjust the gain of sliding mode switch control, which eliminate the chattering of sliding mode control and achieve a precise tracking control of the mobile robot without an accurate model.
Thirdly, an adaptive dynamic recurrent fuzzy neural network sliding control algorithm based on genetic optimization is proposed. Segmented adaptive mutation operator is applied to improve the mutation operation, and the optimal parameters of the kinematics controller are selected by using genetic algorithm including the above mutation operation. The dynamic multi-input multi-output recurrent fuzzy neural network is designed to achieve online estimation of dynamic nonlinear uncertain part of mobile robot system, which makes the estimation error of the uncertainty reduce greatly; the uncertainty interference of the mobile robot are decreased excellently and the input chattering of sliding mode control is eliminated through combining the designed neural network with the adaptive robust controller, hereby ensuring the fine accuracy of trajectory tracking.
Finally, the studies are extended from trajectory tracking of a single robot to formation control of multiple nonholonomic mobile robots. Based on the basic principles of leader-follower, taking account of the impact on the formation control caused by the uncertainty of single robot's dynamics and the formation dynamics, the controller is designed separately based on the conditions that actuator mode included in the dynamics model and not included. As for dynamic model, an adaptive sliding mode formation control which combines neural network with sliding mode control is proposed to remove the chattering of sliding mode control and overcome the disturbances of formation caused by the uncertainties of a single robot and formation dynamics. The other case is adaptive formation control problem of mobile robot including actuator dynamics. By using backstepping methodology, the dynamics of the robot including actuator model is introduced to the controller. RBF neural network is used to model uncertainties of formation control including changes in the load, friction (random change), external disturbances and unmodeled part of the formation dynamics. The formation control of multi-nonholonomic mobile robot including actuator dynamics is implemented.
引文
[1]Brockett R W. Asymptotic stability and feedback stabilization in differential geometric control theory. Boston:Birkhauiser,1983:181-208P
[2]Kanayama Y, Kimura Y, Miyazaki F, et al. A stable tracking control method for an autonomous mobile robot. Proceedings 1990 IEEE International Conference on Robotics and Automation, Cincinnati, OH, USA 1990:384-389P
[3]Murray R M, Walsh G, Sastry S S. Stabilization and tracking for nonholonomic control systems using time-varying state feedback. IFAC Nonlinear control systems design, M.Fliess,Ed.Bordeaux,1993:109-114P
[4]Walsh G, Tilbury D, Sastry S, et al. Stabilization of trajectories for systems with nonholonomic constraints, IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 1994,39(1):216-222P
[5]Oelen W, Van Amerongen J. Robust tracking control of two-degrees-of-freedom mobile robots. Control Engineering Practice.1994:333-340P
[6]Fliess M, Levine J, Martion P, et al. Design of trajectory stabilizing feedback for driftless flat systems. Proceedings of 3rd European control conf. Rome,1995: 1882-1887P
[7]Samson C, Ait-Abderrahim K. Feedback control of a nonholonomic wheeled cart in Cartesian space. Proceedings of IEEE International Conference on Robotics and Automation Sacramento,CA,1136-1141P
[8]Jiang Z P, Nijmeijer H. Tracking control of mobile robots:a case study in backstepping. Automatica.1997,33(7):1393-1399P
[9]吴卫国,陈辉堂,王月娟.移动机器人的全局轨迹跟踪控制.自动化学报.2001,27(3)：326-331页
[10]徐俊艳,张培仁.非完整轮式移动机器人轨迹跟踪控制研究.中国科学技术大学学报.2004,34(3)：376-380页
[11]Jiang Z P, Nijmeijer H. Recursive technique for tracking control of nonholonomic systems in chained form. IEEE Transactons on automatic control,1999,44(2):265-279P
[12]Jiang Z P. Lyapunov design of global state and output feedback trackers for non-holomic control systems. International Journal of Control,2000,73(9):744-761P
[13]Lefeber E, Robertsson A, Nijmeijer H. Linear controllers for exponential tracking of systems in chained-form. International Journal of Robust and Nonlinear Control, 2000,10(4):243-263P
[14]Aneke N P I, Nijimejier H, De Jager AG. Tracking control of second-order chained-form systems by cascade backstepping. International Journal of Robust and Nonlinear Control,2003,13(2):95-115P
[15]Cao K C, Tian Y P.A time-varying cascaded design for trajectory tracking control of nonholonomic systems.International Journal of Control,2007,80(3):416-429P
[16]Lee T C, Jiang Z P. New cascade approach for global p-exponential tracking of underactuated ships. IEEE Trans. On Automatic control,2004,49(12):2297-2303P
[17]Dong W J, Guo Y. Nonlinear tracking control of underactuated surface vessel. American Control Conference. Portland,2005:4351-4356P
[18]Sun S L. Designing approach on trajectory-tracking control of mobile robot. Robotics and Computer-Integrated Manufacturing.2005:81-85P
[19]Fierro R, Lewis F L. Control of a nonholonomic mobile robot:backstepping kinematics into dynamics. Journal of Robotic Systems,1997,14(3):149-163P
[20]Lefeber E, Nijmeijer H. Adaptive tracking control of nonholonomic systems:an robots. Proceedings of the 38th IEEE Conference on Decision and Control(CDC),Phoenix, AZ,USA,1999:2094-2099P
[21]Lee T C, Lee C H, Teng C C, Adaptive tracking control of nonholonomic mobile robots by computed torque. Proceedings of the 38th IEEE Conference on Decision and Control, Phoenix, AZ, USA, Vol.2.1999:1254-1259P
[22]Dong W J, Xu W L. Adaptive tracking control of uncertain nonholonomic dynamic system, IEEE Transactions on Automatic Control,2001,46(3):450-454P
[23]Dong W J, Huo W, Tso S K, et al. Tracking control of uncertain dynamic nonholonomic system and its application to wheeled mobile robots, IEEE Transactions on Robotics and Automation.2000,16(6):870-874P
[24]Dong W J, Huo W. Adaptive stabilization of dynamic nonholonomic chained systems with uncertainty, Proceedings of the 36th IEEE Conference on Decision and Control, Vol.3,1997:2362-2367P
[25]Farzad Pourboghrat, Mattias P. Karlsson. Adaptive control of dynamic mobile robots with nonholonomic constraints, Computers and Electrical Engineering.2002,28(4): 241-253P
[26]Fukao T, Nakagawa H, Adachi N. Adaptive tracking control of a nonholonomic mobile robot, IEEE Transaction on Robotics and Automation,2000,16(5):609-615P
[27]David G,Wilson, Rush D.Robinett,Ⅲ, Robust adaptive backstepping control for a nonholonomic mobile robot, Proceedings of 2001 IEEE International Conference on Systems, Man, and Cybernetics,,Vol.5,2001:3241-3245P
[28]Kozlowski K, Majchrzak J. A backstepping approach to control a nonholonomic mobile robot. Proceedings of 2002 IEEE International Conference on Robotics and Automation, Vol.4,2002:3972-3977P
[29]Khoshnam Shojaei, Alireza Mohammad Shahri, Ahmadreza Tarakameh,et al. Adaptive trajectory tracking control of a differential drive wheeled mobile robot. Robotica, June 3,2010:1-12P
[30]Huang J T. Adaptive tracking control design for high-order nonholonomic mobile robot systems. Proceedings of the Seventh International Conference on Machine Learning and Cybernetics, Kunming,2008:1866-1871P
[31]Park B S, Yoo S J, Park J B, et al. Adaptive tracking control of nonholonomic mobile robots considering actuator dynamics:Dynamic surface design approach. Proceedings of 2009 American Control Conference (ACC-09), Hyatt Regency Riverfront, St. Louis, MO, USA 2009,3860-3864P
[32]Park B S, Yoo S J, Park J B, et al. A simple adaptive control approach for trajectory tracking of electrically driven nonholonomic mobile robots, IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY,2010,18(5),1199-1205P
[33]Chacal J A, Sira-ramirez H. On the sliding mode control of wheeled mobile robots.1994 IEEE International Conference on Systems, Man, and Cybernetics. Humans, Information and Technology. Vol.2,1994:1938-1943P
[34]Solea R, Filipescu A, Minzu V, et al. Sliding-mode trajectory-tracking control for a four-wheel-steering vehicle.2010 8th IEEE International Conference on Control and Automation Xiamen, China, June 9-11,2010:382-387P
[35]Solea R, Filipescu A, Stamatescu G. Sliding-mode real-time mobile platform control in the presence of uncertainties. Joint 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference Shanghai, P.R. China,2009:7747-7752P
[36]Yang J M, Choi I H, Kim J H, Sliding mode control of a nonholonomic wheeled mobile robot for trajectory tracking, Proceedings of the 1998 IEEE International Conference on Robotics & Automation Leuven, Belgium,Vol.4,1998:2983-2988P
[37]Yang J M, Kim J H.Sliding mode control for trajectory tracking of nonholonomic wheeled mobile robots, IEEE Transaction on Robotics and Automation,1999,15(3): 609-615P
[38]Kim M S, Shin J H, Hong S G, et al, Designing a robust adaptive dynamic controller for nonholonomic mobile robots under modeling uncertainty and disturbances, Mechatronics,Vol.13,2003:507-519P
[39]Kim M S, Shin J H, Lee, J J. Design of a robust adaptive controller for a mobile robot, Proceedings of 2000 IEEE/RSJ International Conference on IROS, Vol.3,2000: 1816-1821P
[40]闫茂德,吴青云,贺昱曜.非完整移动机器人德自适应滑模轨迹跟踪控制.系统仿真学报.2007,19(3)：579-584页
[41]Chwa D Y, Seo J H., Kim P, et al. Sliding mode tracking control of nonholonomic wheeled mobile robots, Proceedings of the American Control Conference, Anchorage,AK,2002:1145-1147P
[42]Chwa D Y. Sliding-Mode Tracking Control of Nonholonomic Wheeled Mobile Robots in Polar Coordinates. IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY,2004,12(4):637-644P
[43]Park B S, Yoo S J, Park J B, et al. Adaptive neural sliding mode control of nonholonomic wheeled mobile robots with model uncertainty. IEEE Transactions on Control Systems Technology.2009,17(1):207-214P
[44]Corradini M L, Orlando G, Control of mobile robots with uncertainties in the dynamical model:a discrete time sliding mode approach with experimental results, Control Engineering Practice.2002,10(1):23-34P
[45]晁红敏,胡跃明.动态滑模控制及其在移动机器人输出跟踪中的应用.控制与决策.2001,16(5)：565-568页
[46]Li C K, Chao H M, Output tracking of uncertain robot systems via high order sliding mode technique, Electronics Letters.2002,38(10):487-489P
[47]Hamerlain M, Alalouche T. Sliding mode control applied to mobile robot: continuous-time and Discrete-time cases. Proceedings of First International Symposium on Control, Communications and Signal Processing, Hammamet, Tunisia. 2004:15-20P
[48]Hamerlain F, Achour K, Floquet T, et al. Higher order sliding mode control of wheeled mobile robots in the presence of sliding effects. Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference 2005 Seville, Spain,2005:1959-1963P
[49]Ge L Z, Li R F, Zhao L J. A nonlinear trajectory tracking algorithm design based on second-order sliding mode control for mobile robots. Proceedings of the 2007 IEEE International Conference of Robotics and Biomimetics. Sanya, China,2007:1184-1189.
[50]Filipescu Adrian, Stancu Alexandru, Filipescu Silviu. et al. Real-time sliding-mode adaptive control of a mobile platform wheeled mobile robot.Proceedings of the 17th World Congress, International Federation of Automatic Control, IFAC Seoul, Korea, Republic of,2008:4393-4399P
[51]Hung N, Im J S, Jeong S K, et al. Design of a sliding mode controller for an automatic guided vehicle and its implementation. International Journal of Control, Automation, and Systems.2010,8(1):81-90P
[52]Hwang C K, Chen B S, Chang Y T. Combination of kinematical and robust dynamical controllers for mobile robotics tracking control:(Ⅰ) optimal H8 control. Proceedings of the 2004 IEEE International Conference on Control Applications Taipei, Taiwan, 2004:1205-1210P
[53]Hwang C K, Chen B S, Chang Y T. Combination of kinematical and robust dynamical controllers for mobile robotics tracking control:(Ⅱ) Suboptimal a control.Proceedings of the 2004 IEEE International Conference on Control Applications Taipei, Taiwan, 2004:1211-1216P
[54]Chen H, Wang J, Gao X Q, et al. A moving horizon H8 tracking scheme for wheeled mobile robots with actuator constraints. Proceedings of 6th IFAC Symposium on Nonlinear Control Systems.2004:783-788P
[55]Chen H, Ma M M, Wang H, et al. Moving horizon H8 tracking control of wheeled mobile robots with actuator saturation, IEEE Transactions on Control Systems Technology.2009,17(2):449-457P
[56]董国华,祝晓才,刘振等.不确定曲面上轮式移动机器人鲁棒轨迹跟踪.南京理工大学学报(自然科学版)2008,32(1)：18-27页
[57]Yoshihiko Miyasato. Adaptive H8 control of nonholonomic mobile robot based on inverse optimality.2008 American control conference, Westin Seattle Hotel, Seattle, Washington, USA. June,2008:3524-3529 P
[58]Fierro R, Lewis F L. Practical point stabilization of a nonholonomic mobile robot using neural networks, Proceedings of the 35th Conference on Decision and Control, Kobe, Japan, December,1996:1722-1717P
[59]Fierro R, Lewis F L. Control of a nonholonomic mobile robot using neural networks, Neural Networks,IEEE Transactions on,1998,9(4):589-600P
[60]De Oliveira V M, De Pieri E R, Lages W F. Feedforward control of a mobile robot using a neural network,2000 IEEE International Conference on Systems, Man and Cybernetics, Vol.5,2000:3342-3347P
[61]De Sousa Junior, C Hemerly, E M, Neural network-based controllers for mobile robots, Proceedings of Sixth Brazilian Symposium on Neural Networks,2000:50-55P
[62]Hu T M, Yang S X. An efficient neural controller for a nonholonomic mobile robot, Proceedings IEEE International Symposium on Computational Intelligence in Robotics and Automation.2000:461-466P
[63]Hu T M, Yang S X, Wang F J, et al. A neural network controller for a nonholonomic mobile robot with unknown robot parameters, IEEE International Conference on ICRA'02., Vol.4,2002:3540-3545P
[64]Corradini M L, Ippoliti G, Longhi S. Neural networks based control of mobile robots: development and experimental validation. Journal of Robotic Systems,2003,20(10): 587-600P
[65]De Sousa C, Jt Hemerly E M. Galvao R K H. Adaptive control for mobile robot using wavelet networks, IEEE Transactions on Systems, Man and Cybernetics, Part B, 2002,32(4):493-504P
[66]Purwar S, Gupta N, Kar IN. Trajectory tracking control of mobile robots using wavelet networks.22nd IEEE International Sysposium on Intlligent Control Part of IEEE Multi-conference on Systems and Control. Singapore,2007:550-555P
[67]Ye J. Adaptive control of nonlinear PID-based analog neural networks for a nonholonomic mobile robot. Neurocomputing,71,2008:1561-1565P
[68]Kwan C, Lewis F L. Robust backstepping control of Nonlinear Systems using neural networks. IEEE Transaction on systems, man, cybernetics-part A:systems and humans. 2000,30(6):753-765P
[69]Kim S H, Park C K, Harashima F. Adaptive fuzzy controller design for trajectory tracking of a 2 D.O.F. wheeled mobile robot using genetic algorithm. Proceedings of IEEE International Conference on Intelligent Robots and System. Victoria, B.C., Canada 1998:1584-1589P
[70]Kim S H, Park C K, Harashima F. A self-organized fuzzy controller for wheeled mobile robot using an evolutionary algorithm. IEEE Transactions on Industrial Electronics. 2001,48(2):467-474P
[71]Lee T H, Lam H K, Leung F H, et al. A practical fuzzy logic controller for the path tracking of wheeled mobile robots. IEEE Control Systems Magazine,2003, 23(2):60-65P
[72]Raimon F M., Melluso M, Ciancimino L S. A new kinematic and dynamic direct adaptive fuzzy control of constrained mobile wheeled vehicles.10th IEEE International Conference on Emerging Technologies and Factory Automation, Vol.2,2005:181-188P
[73]Das T, Kar I N. Development of an adaptive fuzzy logic based control law for a mobile robot with an uncalibrated camera system. Proceedings of the 1st International Conference on Pattern Recognition and Machine Intelligence, Kolkata, India, 2005:25-331P
[74]Das T, Kar I N. Design and implementation of an adaptive fuzzy logic-based controller for wheeled mobile robots. IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL.14, NO.3, MAY 2006:501-510P
[75]高兴泉,陈虹.基于T-S模型的轮式移动机器人轨迹跟踪控制.控制理论与应用,2007,24(6)：873-878页
[76]Keighobadi J, Menhaj M B, Kabganian M. Feedback-linearization and fuzzy controllers for trajectory tracking of wheeled mobile robots. Kybernetes,2010,39(1): 83-106P
[77]Hwang C L, Chang L J, Yu Y S. Network-based fuzzy decentralized sliding-mode control for car-like mobile robots.IEEE Transactions on Industrial Electronics,2007, 54(1):574-585P
[78]Hsiao M Y, Chen C Y, Tsai S H, et al. Combined interval type-2 fuzzy kinematic and dynamic controls of the wheeled mobile robot with adaptive sliding-mode technique. Proceedings of 2009 IEEE International Conference on Fuzzy Systems,2009:706-711P
[79]Wang P K C. Navigation strategies for multiple autonomous mobile robots moving in formation. Journal of Robotic Systems.1991,8(2):177-195P
[80]Jaydev P. Desai, Jim Ostrowski, Vijay Kumar.Controlling formations of multiple mobile robots. Proceedings of the 1998 IEEE International Conference on Robotics & Automation.Leuven, Belgium May 1998:2864-2869P
[81]Desai J P, Ostrowski J P, Kumar V. Modeling and control of formations of nonholonomic mobile robots. IEEE Transactions on Robotics and Automation,2001, 17(6):905-908P
[82]Anderson B D O, Yu C B, Dasgupta S, et al. Control of a three-coleader formation in the plane. Systems & Control Letters,2007:56(9-10):573-578P
[83]Fujimori A, Fujimoto T, Bohatcs G. Distributed leader-follower navigation of mobile robots.2005 International Conference on Control and Automation (ICCA2005). Budapest, Hungary.2005:960-965P
[84]曹志强,张斌,谭民.基于行为的多移动机器人实时队形保持.高技术通信,2001,10：74-77页
[85]石桂芬,方华京.基于相邻矩阵的多机器人编队容错控制.华中科技大学学报,2005,33(3)：39-42页
[86]Sisto M, Gu D B. A fuzzy leader-follower approach to formation control of multiple mobile robots. International Conference on Intelligent Robots and Systems. 2006:2515-2510P
[87]Shao J, Xie G, Wang L. Leader-following formation control of multiple mobile vehicles. IET Control Theory and Applications,2007,1(2):545-552P
[88]Lewis M A, Tan K H. High precision formation control of mobile robots using virtual structures, Autonomous Robots,1997,4(4):387-403P
[89]Do K D, Pan J. Nonlinear formation control of unicycle-type mobile robots. Robotics and Autonomous Systems,2007,55(3):191-204P
[90]Van den Broek T H A, Van de Wouw N, Nijmeijer H. Formation control of unicycle mobile robots:a virtual structure approach. Joint 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference Shanghai, P.R. China,2009: 8328-8333P
[91]Li Q, Jiang Z P. Formation tracking control of unicycle teams with collision avoidance. 47th IEEE Conference on Decision and Control.2008:496-501P
[92]贾秋玲,闫建国,王新民.基于势函数的多机器人系统的编队控制.机器人,2006,28(2)：111-114页
[93]Balch T, Arkin R C. Behavior-based formation control for multirobot team. IEEE Transaction on Robotics and Automation,1998,14(6):926-939P
[94]苏治宝,陆际联.多移动机器人队形控制的研究方法.机器人,2003,25(1)：88-91页
[95]Parker L E. ALLIANCE:An architecture for fault-tolerant multirobot cooperation. IEEE Transaction on Robotics and Automation,1998,14(2):220-240P
[96]Saad E M, Awadalla M H, Hamdy A M, et al. Robot formations using local sensing and limited range vision.25th National Radio Science Conference, Tanta, Egypt,2008:1-8P
[97]Lawton J R T, Beard R W, Young B J. A decentralized approach to formation maneuvers. IEEE Transactions on Robotics and Automation,2003,19(6):993-941P
[98]Cao Z Q, Xie L J, Zhang B. Formation constrained multi-robot system in unknown environments.Proceedings of the IEEE International Conference on Robotics and Automation.Taipei,China,2003:735-740P
[99]Monteiro S, Bicho E. A dynamical systems approach to behavior based formation control. Proceedings of the IEEE International Conference on Robotics and Automation. Washington, DC,2002:2606-2611P
[100]Liu Z Q, Kubota N. Hybrid learning approach based on multi-objective behavior coordination for multiple robots. Proceedings of the 2007 IEEE International Conference on Mechatronics and Automation, Harbin, China,2007:204-209P
[101]Khatib. Real-time obstacle avoidance for manipulators and mobile robots. International Journal of Robotics Research.1986,5(1):290-298P
[102]Dudenhoeffer D D, Jones M P. A formation behavior for large-scale micro-robot force deployment. Proceedings of the 2000 Winter Simulation Conference. New York, 2000:972-982P
[103]Reif J H, Wang H. Social potential fields:a distributed behavioral control for autonomous robots. Robotics and Autonomous Systems 1999,27(3):171-194P
[104]Leonard N E, Fiorelli E. Virtual leaders, artificial potentials and coordinated control of groups. Proceedings of the 40th IEEE International Conference on Decision and Control. Orlando,USA,Vol.3,2001:2968-2973P
[105]Ogren P, Fiorelli E, Leonard N E. Formations with a mission:stable coordination of vehicle group maneuvers. Proceedings of the 15th International Symposium on Mathematical Theory of Networks and Systems. Notre Dame,2002:1-15P
[106]Staber R O, Murray R M. Distributed cooperative control of multiple vehicle formations using structural potential functions. Proceedings of the 15th IFAC World Congress. Barcelona,2002:1243-1251P
[107]Baras J S, Tan X B, Hovareshti P. Decentralized control of autonomous vehicles. Proceedings of the 42nd IEEE Conference on Decision and Control. Hawaii,Vol.2, 2003:1532-1537P
[108]Ge S S, Fua C H, Lim K W. Multi-robot formations:queues and artificial potential trenches. Proceedings of the IEEE International Conference on Robotics and Automation. New Orleans,2004,2004(4):3345-3350P
[109]Liang Y, Lee H H. Decentralized formation control and obstacle avoidance for multiple robots with nonholonomic constraints. Proceedings of the 2006 American Control Conference. Minneapolis,2006:5596-5601P
[110]Desai J P. A graph theoretic approach for modeling mobile robot team formation. Journal of Robotic Systems,2002,19(11):511-525P
[111]Fierro R, Das A K.A modular architecture for formation control, Third International Workshop on Robot Motion and Control,2002:285-290P
[112]Dong W J, Farrell Jay A. Decentralized cooperative control of multiple nonholonomic dynamic systems with uncertainty. Automatica,2009,45(3):706-710P
[113]Dunbar W B. Model predictive control:Extension to coordinated multi-vehicle formations and real-time implementation. Pasadena:California Institute of Technology, 2001
[114]Dunbar W B, Murra R M. Model predictive control of coordinated multi-vehicle formations. Proceedings of the IEEE Conference on Decision and Control. Las Vegas, Vol.4,2002:4631-4636P
[115]Wesselowski K, Fierro R. A dual-mode model predictive controller for robot formations. Proceedings of the 42nd IEEE Conference on Decision and Control. Maui, Hawaii USA, Vol.4,2003:3615-3620P
[116]Xie F, Fierro R. On motion coordination of multiple vehicles with nonholonomic constraints. Proceedings of the 2007 American Control Conference.New York, USA, 2007:1888-1893P
[117]Kanjanawanishkul K, Zell A. A model-predictive approach to formation control of omnidirectional mobile robots.2008 IEEE/RSJ International Conference on Intelligent Robots and Systems. Nice, France,2008:2771-2776P
[118]Xie F, Fierro R. Stabilization of nonholonomic robot formations:a first-state contractive model predictive control approach. Journal of Computing and Information Technology.2009,17(1):37-50P
[119]Suqihara K, Suzuki I. Distributed algorithms formation of geometric patterns with many mobile robots. Journal of Robotic Systems,1996,13 (3):127-139P
[120]Young B J, Beard R W, Kelsey J M. A control scheme for improving multi-vehicle formation maneuvers. Proceedings of the American Control Conference. Arlington, USA, Vol.2,2001:709-709P
[121]王醒策,张汝波,顾国昌.基于强化学习的多机器人编队方法研究.计算机工程.2002,28(6)：15-17页
[122]Kon K, Fukushima H, Matsuno F. Multi-vehicle formation control based on branch-and-bound method compatible with collision avoidance problem. Proceedings of IEEE/RSJ International Intelligent Robots and systems,2007:3771-3776P
[123]Ren W. Consensus strategies for cooperative control of vehicle formations, IET Control Theory& Applications,2007,1(2):505-512P
[124]Kolmanovsky I, McClamroch N H. Developments in nonholonomic control problems. IEEE Control Systems Magazine,1995,15(6):20-36P
[125]Das T, Kar I N, Chaudhury S. Simple neuron-based adaptive controller for a nonholonomic mobile robot including actuator dynamics. Neurocomputing,2006, 29(16-18):2140-2151P
[126]Jean-Jacques, E. Slotine, Weiping Li.应用非线性控制.程代展等译.机械工业出版社,北京,2006:82-83页
[127]Jiang Z P, Pomet J B.Combining backstepping and time-varying techniques for a new set of adaptive controllers. Proceedings of the 33rd IEEE Conference on Decision and Control, Lake Buena Vista, FL,Vol.3,1994:2207-2212P
[128]胡跃明编著.非线性控制系统理论与应用,第2版.国防工业出版社.北京,2005：188-190页
[129]Dong W J, Kuhnert K D. Robust adaptive control of nonholonomic mobile robot with parameter and nonparameter uncertainties. IEEE Transactions on Robotics, 2005,21(2):261-266P
[130]Yuan G, Yang S X., Mitlal G S. Tracking control of a mobile robot using a neural dynamics based approach. Proceedings of the 2001 IEEE International Conference on Robotics and Automation. Seoul, Korea,2001:163-168P
[131]Liu S R, Zhang H D, Yang S X., et aL Dynamic control of a mobile robot using an adaptive neurodynamics and sliding mode strategy. Proceedings of the 5th World Congress on Intelligent Control and Automation. Hangzhou, P.R. China.2004: 5007-5011P
[132]刘金琨著.滑模变结构控制MATLAB仿真.清华大学出版社,北京.2005：35-36页
[133]Slotine J J, Sastry S S.Tracking control of nonlinear systems using sliding surfaces with application to robot manipulator. International Journal of Control.1983,38(2): 465-492P
[134]Grossberg S. Nonlinear neural networks:principles, mechanisms, and architectures. Neural Networks,1988,1(1):17-61P
[135]Kuo T C, Hong B W, Huang Y J, et al. Adaptive fuzzy controller design for robotic manipulators with sliding mode control. Proceedings of 2008 IEEE International Conference on Fuzzy Systems(FUZZ 2008). Hong Kong, China.2008:581-586P
[136]刘金国,王越超,李斌等.灾难救援机器人研究现状、关键性能及展望.机械工程学报.2006,42(12)：1-12页
[137]宋小康,谈大龙,吴镇炜等.全地形轮式移动机器人运动学建模与分析.机械工程学报.2008,44(6)：148-154页
[138]岳李勇,谢巍.含有驱动器模型的移动机器人自适应跟踪控制.控制理论与应用.2008,25(6)：1001-1006页
[139]Yu S H, Liu S, Xu H. Adaptive fuzzy trajectory tracking control of uncertain nonholonomic mobile robots. Proceedings of 6th IEEE International Conference on Industrial Informatics. Daejeon, South Korea. Piscataway:IEEE,2008:481-486P
[140]骆德渊,刘荣,李纪文.采用模糊逻辑的移动机器人轨迹跟踪.电子科技大学学报,2008,37(6)：943-946P
[141]Martins N A, Bertol D, Lombardi W, et al. Trajectory tracking of a nonholonomic mobile robot with parametric and nonparametric uncertainties:A proposed neural control. Proceedings of 16th Mediterranean Conference on Control and Automation. Congress Centre, Ajaccio, France. Piscataway:IEEE,2008:315-320P
[142]李世华,田玉平.非完整移动机器人的有限时间跟踪控制算法研究.控制与决策.2005,20(7)：750-754P
[143]马海涛,王永.输入饱和约束下的非完整移动机器人轨迹跟踪控.中国科学技术大学学报.2009,39(5)：499-503页
[144]Hu H, Woo P Y. Fuzzy supervisory sliding-mode and neural-network control for robotic manipulators. IEEE Transactions of Industrial Electronics.2006,53(3): 929-940P
[145]Ak A G, Cansever G. Three link robot control with fuzzy sliding mode controller based on RBF neural network. Proceedings of the 2006 IEEE International Symposium on Intelligent Control. Munich, Germany. Piscataway:IEEE,2006:2719-2724P
[146]Liu J K, He Y Z. Fuzzy global sliding mode control based on genetic algorithm and its application for flight simulator servo system. Chinese Journal of Mechanical Engineering,2007,20(3):13-17P
[147]林壮.欠驱动水平机械臂滑模变结构控制研究.哈尔滨工程大学博士论文.2007：47-79页
[148]Hsu C F, Cheng K H. Recurrent fuzzy-neural approach for nonlinear control using dynamic structure learning scheme. Neurocomputing,2008,71:3447-3459P
[149]王小平,曹立明编著.遗传算法：理论、应用与软件实现.西安：西安交通大学出版社：2002
[150]Goldberg D E.Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley,1989
[151]Michalewicz Z, Janikow C Z, Krawczyk J B. A modified genetic algorithm for optimal control problems. Computers & Mathematics with Applications,1992,23(12): 83-94P
[152]田景文等,人工神经网络算法研究及应用.北京理工大学出版社,2006.7：52-75页
[153]沈猛.轮式移动机器人导航控制与路径规划研究.西北工业大学博士论文.2006： 82-83页
[154]杨甜甜,刘志远,陈虹,等.移动机器人编队控制的现状与问题.智能系统学报.2007,2(4)：21-27页
[155]苏治宝,陆际联.多移动机器人队形控制的研究方法.机器人.2003,25(1)：88-91页
[156]Das A, Fierro R, Kumar V, et al. A vision-based formation control framework. IEEE Transactions on Robotics and Automation,2002,18(5):813-825P
[157]Li X H, Xiao J and Cai Z. Backstepping-based multiple-mobile robots formation control. Proceedings of 2005 IEEE/ RSJ International Conference on Intelligent Robots and Systems, Edmonton, Alta, Canada,2005:887-892P
[158]Breivik M, Subbotin M and Fossen T. Guided formation control for wheeled mobile robots. Proceedings of 9th International Conference on Control, Automation, Robotics and Vision, IEEE, Singapore,2006:1-7P
[159]Do K D. Formation-tracking control of unicycle-type mobile robots with limited sensing ranges. IEEE Transactions on Control Systems Technology,2008,16(3): 527-538P
[160]Dierks T and Jagannathan S. Neural network control of mobile robot formations using RISE feedback. IEEE Transactions on Systems, Man, and Cybernetics-PART B: Cybernetics,2009,39(2):332-347P
[161]Dierks T, Jagannathan S. Control of nonholonomic mobile robot formations: backstepping kinematics into dynamics. Proceedings of 16th IEEE International Conference on Control Applications Part of IEEE Multi-conference on Systems and Control, Singapore,2007:94-99P
[162]Dierks T and Jagannathan S. Neural network control of mobile robot formations using RISE feedback. IEEE Transactions on Systems, Man, and Cybernetics-PART B: Cybernetics,2009,39(2):332-347P
[2]Kanayama Y, Kimura Y, Miyazaki F, et al. A stable tracking control method for an autonomous mobile robot. Proceedings 1990 IEEE International Conference on Robotics and Automation, Cincinnati, OH, USA 1990:384-389P
[3]Murray R M, Walsh G, Sastry S S. Stabilization and tracking for nonholonomic control systems using time-varying state feedback. IFAC Nonlinear control systems design, M.Fliess,Ed.Bordeaux,1993:109-114P
[4]Walsh G, Tilbury D, Sastry S, et al. Stabilization of trajectories for systems with nonholonomic constraints, IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 1994,39(1):216-222P
[5]Oelen W, Van Amerongen J. Robust tracking control of two-degrees-of-freedom mobile robots. Control Engineering Practice.1994:333-340P
[6]Fliess M, Levine J, Martion P, et al. Design of trajectory stabilizing feedback for driftless flat systems. Proceedings of 3rd European control conf. Rome,1995: 1882-1887P
[7]Samson C, Ait-Abderrahim K. Feedback control of a nonholonomic wheeled cart in Cartesian space. Proceedings of IEEE International Conference on Robotics and Automation Sacramento,CA,1136-1141P
[8]Jiang Z P, Nijmeijer H. Tracking control of mobile robots:a case study in backstepping. Automatica.1997,33(7):1393-1399P
[9]吴卫国,陈辉堂,王月娟.移动机器人的全局轨迹跟踪控制.自动化学报.2001,27(3)：326-331页
[10]徐俊艳,张培仁.非完整轮式移动机器人轨迹跟踪控制研究.中国科学技术大学学报.2004,34(3)：376-380页
[11]Jiang Z P, Nijmeijer H. Recursive technique for tracking control of nonholonomic systems in chained form. IEEE Transactons on automatic control,1999,44(2):265-279P
[12]Jiang Z P. Lyapunov design of global state and output feedback trackers for non-holomic control systems. International Journal of Control,2000,73(9):744-761P
[13]Lefeber E, Robertsson A, Nijmeijer H. Linear controllers for exponential tracking of systems in chained-form. International Journal of Robust and Nonlinear Control, 2000,10(4):243-263P
[14]Aneke N P I, Nijimejier H, De Jager AG. Tracking control of second-order chained-form systems by cascade backstepping. International Journal of Robust and Nonlinear Control,2003,13(2):95-115P
[15]Cao K C, Tian Y P.A time-varying cascaded design for trajectory tracking control of nonholonomic systems.International Journal of Control,2007,80(3):416-429P
[16]Lee T C, Jiang Z P. New cascade approach for global p-exponential tracking of underactuated ships. IEEE Trans. On Automatic control,2004,49(12):2297-2303P
[17]Dong W J, Guo Y. Nonlinear tracking control of underactuated surface vessel. American Control Conference. Portland,2005:4351-4356P
[18]Sun S L. Designing approach on trajectory-tracking control of mobile robot. Robotics and Computer-Integrated Manufacturing.2005:81-85P
[19]Fierro R, Lewis F L. Control of a nonholonomic mobile robot:backstepping kinematics into dynamics. Journal of Robotic Systems,1997,14(3):149-163P
[20]Lefeber E, Nijmeijer H. Adaptive tracking control of nonholonomic systems:an robots. Proceedings of the 38th IEEE Conference on Decision and Control(CDC),Phoenix, AZ,USA,1999:2094-2099P
[21]Lee T C, Lee C H, Teng C C, Adaptive tracking control of nonholonomic mobile robots by computed torque. Proceedings of the 38th IEEE Conference on Decision and Control, Phoenix, AZ, USA, Vol.2.1999:1254-1259P
[22]Dong W J, Xu W L. Adaptive tracking control of uncertain nonholonomic dynamic system, IEEE Transactions on Automatic Control,2001,46(3):450-454P
[23]Dong W J, Huo W, Tso S K, et al. Tracking control of uncertain dynamic nonholonomic system and its application to wheeled mobile robots, IEEE Transactions on Robotics and Automation.2000,16(6):870-874P
[24]Dong W J, Huo W. Adaptive stabilization of dynamic nonholonomic chained systems with uncertainty, Proceedings of the 36th IEEE Conference on Decision and Control, Vol.3,1997:2362-2367P
[25]Farzad Pourboghrat, Mattias P. Karlsson. Adaptive control of dynamic mobile robots with nonholonomic constraints, Computers and Electrical Engineering.2002,28(4): 241-253P
[26]Fukao T, Nakagawa H, Adachi N. Adaptive tracking control of a nonholonomic mobile robot, IEEE Transaction on Robotics and Automation,2000,16(5):609-615P
[27]David G,Wilson, Rush D.Robinett,Ⅲ, Robust adaptive backstepping control for a nonholonomic mobile robot, Proceedings of 2001 IEEE International Conference on Systems, Man, and Cybernetics,,Vol.5,2001:3241-3245P
[28]Kozlowski K, Majchrzak J. A backstepping approach to control a nonholonomic mobile robot. Proceedings of 2002 IEEE International Conference on Robotics and Automation, Vol.4,2002:3972-3977P
[29]Khoshnam Shojaei, Alireza Mohammad Shahri, Ahmadreza Tarakameh,et al. Adaptive trajectory tracking control of a differential drive wheeled mobile robot. Robotica, June 3,2010:1-12P
[30]Huang J T. Adaptive tracking control design for high-order nonholonomic mobile robot systems. Proceedings of the Seventh International Conference on Machine Learning and Cybernetics, Kunming,2008:1866-1871P
[31]Park B S, Yoo S J, Park J B, et al. Adaptive tracking control of nonholonomic mobile robots considering actuator dynamics:Dynamic surface design approach. Proceedings of 2009 American Control Conference (ACC-09), Hyatt Regency Riverfront, St. Louis, MO, USA 2009,3860-3864P
[32]Park B S, Yoo S J, Park J B, et al. A simple adaptive control approach for trajectory tracking of electrically driven nonholonomic mobile robots, IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY,2010,18(5),1199-1205P
[33]Chacal J A, Sira-ramirez H. On the sliding mode control of wheeled mobile robots.1994 IEEE International Conference on Systems, Man, and Cybernetics. Humans, Information and Technology. Vol.2,1994:1938-1943P
[34]Solea R, Filipescu A, Minzu V, et al. Sliding-mode trajectory-tracking control for a four-wheel-steering vehicle.2010 8th IEEE International Conference on Control and Automation Xiamen, China, June 9-11,2010:382-387P
[35]Solea R, Filipescu A, Stamatescu G. Sliding-mode real-time mobile platform control in the presence of uncertainties. Joint 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference Shanghai, P.R. China,2009:7747-7752P
[36]Yang J M, Choi I H, Kim J H, Sliding mode control of a nonholonomic wheeled mobile robot for trajectory tracking, Proceedings of the 1998 IEEE International Conference on Robotics & Automation Leuven, Belgium,Vol.4,1998:2983-2988P
[37]Yang J M, Kim J H.Sliding mode control for trajectory tracking of nonholonomic wheeled mobile robots, IEEE Transaction on Robotics and Automation,1999,15(3): 609-615P
[38]Kim M S, Shin J H, Hong S G, et al, Designing a robust adaptive dynamic controller for nonholonomic mobile robots under modeling uncertainty and disturbances, Mechatronics,Vol.13,2003:507-519P
[39]Kim M S, Shin J H, Lee, J J. Design of a robust adaptive controller for a mobile robot, Proceedings of 2000 IEEE/RSJ International Conference on IROS, Vol.3,2000: 1816-1821P
[40]闫茂德,吴青云,贺昱曜.非完整移动机器人德自适应滑模轨迹跟踪控制.系统仿真学报.2007,19(3)：579-584页
[41]Chwa D Y, Seo J H., Kim P, et al. Sliding mode tracking control of nonholonomic wheeled mobile robots, Proceedings of the American Control Conference, Anchorage,AK,2002:1145-1147P
[42]Chwa D Y. Sliding-Mode Tracking Control of Nonholonomic Wheeled Mobile Robots in Polar Coordinates. IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY,2004,12(4):637-644P
[43]Park B S, Yoo S J, Park J B, et al. Adaptive neural sliding mode control of nonholonomic wheeled mobile robots with model uncertainty. IEEE Transactions on Control Systems Technology.2009,17(1):207-214P
[44]Corradini M L, Orlando G, Control of mobile robots with uncertainties in the dynamical model:a discrete time sliding mode approach with experimental results, Control Engineering Practice.2002,10(1):23-34P
[45]晁红敏,胡跃明.动态滑模控制及其在移动机器人输出跟踪中的应用.控制与决策.2001,16(5)：565-568页
[46]Li C K, Chao H M, Output tracking of uncertain robot systems via high order sliding mode technique, Electronics Letters.2002,38(10):487-489P
[47]Hamerlain M, Alalouche T. Sliding mode control applied to mobile robot: continuous-time and Discrete-time cases. Proceedings of First International Symposium on Control, Communications and Signal Processing, Hammamet, Tunisia. 2004:15-20P
[48]Hamerlain F, Achour K, Floquet T, et al. Higher order sliding mode control of wheeled mobile robots in the presence of sliding effects. Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference 2005 Seville, Spain,2005:1959-1963P
[49]Ge L Z, Li R F, Zhao L J. A nonlinear trajectory tracking algorithm design based on second-order sliding mode control for mobile robots. Proceedings of the 2007 IEEE International Conference of Robotics and Biomimetics. Sanya, China,2007:1184-1189.
[50]Filipescu Adrian, Stancu Alexandru, Filipescu Silviu. et al. Real-time sliding-mode adaptive control of a mobile platform wheeled mobile robot.Proceedings of the 17th World Congress, International Federation of Automatic Control, IFAC Seoul, Korea, Republic of,2008:4393-4399P
[51]Hung N, Im J S, Jeong S K, et al. Design of a sliding mode controller for an automatic guided vehicle and its implementation. International Journal of Control, Automation, and Systems.2010,8(1):81-90P
[52]Hwang C K, Chen B S, Chang Y T. Combination of kinematical and robust dynamical controllers for mobile robotics tracking control:(Ⅰ) optimal H8 control. Proceedings of the 2004 IEEE International Conference on Control Applications Taipei, Taiwan, 2004:1205-1210P
[53]Hwang C K, Chen B S, Chang Y T. Combination of kinematical and robust dynamical controllers for mobile robotics tracking control:(Ⅱ) Suboptimal a control.Proceedings of the 2004 IEEE International Conference on Control Applications Taipei, Taiwan, 2004:1211-1216P
[54]Chen H, Wang J, Gao X Q, et al. A moving horizon H8 tracking scheme for wheeled mobile robots with actuator constraints. Proceedings of 6th IFAC Symposium on Nonlinear Control Systems.2004:783-788P
[55]Chen H, Ma M M, Wang H, et al. Moving horizon H8 tracking control of wheeled mobile robots with actuator saturation, IEEE Transactions on Control Systems Technology.2009,17(2):449-457P
[56]董国华,祝晓才,刘振等.不确定曲面上轮式移动机器人鲁棒轨迹跟踪.南京理工大学学报(自然科学版)2008,32(1)：18-27页
[57]Yoshihiko Miyasato. Adaptive H8 control of nonholonomic mobile robot based on inverse optimality.2008 American control conference, Westin Seattle Hotel, Seattle, Washington, USA. June,2008:3524-3529 P
[58]Fierro R, Lewis F L. Practical point stabilization of a nonholonomic mobile robot using neural networks, Proceedings of the 35th Conference on Decision and Control, Kobe, Japan, December,1996:1722-1717P
[59]Fierro R, Lewis F L. Control of a nonholonomic mobile robot using neural networks, Neural Networks,IEEE Transactions on,1998,9(4):589-600P
[60]De Oliveira V M, De Pieri E R, Lages W F. Feedforward control of a mobile robot using a neural network,2000 IEEE International Conference on Systems, Man and Cybernetics, Vol.5,2000:3342-3347P
[61]De Sousa Junior, C Hemerly, E M, Neural network-based controllers for mobile robots, Proceedings of Sixth Brazilian Symposium on Neural Networks,2000:50-55P
[62]Hu T M, Yang S X. An efficient neural controller for a nonholonomic mobile robot, Proceedings IEEE International Symposium on Computational Intelligence in Robotics and Automation.2000:461-466P
[63]Hu T M, Yang S X, Wang F J, et al. A neural network controller for a nonholonomic mobile robot with unknown robot parameters, IEEE International Conference on ICRA'02., Vol.4,2002:3540-3545P
[64]Corradini M L, Ippoliti G, Longhi S. Neural networks based control of mobile robots: development and experimental validation. Journal of Robotic Systems,2003,20(10): 587-600P
[65]De Sousa C, Jt Hemerly E M. Galvao R K H. Adaptive control for mobile robot using wavelet networks, IEEE Transactions on Systems, Man and Cybernetics, Part B, 2002,32(4):493-504P
[66]Purwar S, Gupta N, Kar IN. Trajectory tracking control of mobile robots using wavelet networks.22nd IEEE International Sysposium on Intlligent Control Part of IEEE Multi-conference on Systems and Control. Singapore,2007:550-555P
[67]Ye J. Adaptive control of nonlinear PID-based analog neural networks for a nonholonomic mobile robot. Neurocomputing,71,2008:1561-1565P
[68]Kwan C, Lewis F L. Robust backstepping control of Nonlinear Systems using neural networks. IEEE Transaction on systems, man, cybernetics-part A:systems and humans. 2000,30(6):753-765P
[69]Kim S H, Park C K, Harashima F. Adaptive fuzzy controller design for trajectory tracking of a 2 D.O.F. wheeled mobile robot using genetic algorithm. Proceedings of IEEE International Conference on Intelligent Robots and System. Victoria, B.C., Canada 1998:1584-1589P
[70]Kim S H, Park C K, Harashima F. A self-organized fuzzy controller for wheeled mobile robot using an evolutionary algorithm. IEEE Transactions on Industrial Electronics. 2001,48(2):467-474P
[71]Lee T H, Lam H K, Leung F H, et al. A practical fuzzy logic controller for the path tracking of wheeled mobile robots. IEEE Control Systems Magazine,2003, 23(2):60-65P
[72]Raimon F M., Melluso M, Ciancimino L S. A new kinematic and dynamic direct adaptive fuzzy control of constrained mobile wheeled vehicles.10th IEEE International Conference on Emerging Technologies and Factory Automation, Vol.2,2005:181-188P
[73]Das T, Kar I N. Development of an adaptive fuzzy logic based control law for a mobile robot with an uncalibrated camera system. Proceedings of the 1st International Conference on Pattern Recognition and Machine Intelligence, Kolkata, India, 2005:25-331P
[74]Das T, Kar I N. Design and implementation of an adaptive fuzzy logic-based controller for wheeled mobile robots. IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL.14, NO.3, MAY 2006:501-510P
[75]高兴泉,陈虹.基于T-S模型的轮式移动机器人轨迹跟踪控制.控制理论与应用,2007,24(6)：873-878页
[76]Keighobadi J, Menhaj M B, Kabganian M. Feedback-linearization and fuzzy controllers for trajectory tracking of wheeled mobile robots. Kybernetes,2010,39(1): 83-106P
[77]Hwang C L, Chang L J, Yu Y S. Network-based fuzzy decentralized sliding-mode control for car-like mobile robots.IEEE Transactions on Industrial Electronics,2007, 54(1):574-585P
[78]Hsiao M Y, Chen C Y, Tsai S H, et al. Combined interval type-2 fuzzy kinematic and dynamic controls of the wheeled mobile robot with adaptive sliding-mode technique. Proceedings of 2009 IEEE International Conference on Fuzzy Systems,2009:706-711P
[79]Wang P K C. Navigation strategies for multiple autonomous mobile robots moving in formation. Journal of Robotic Systems.1991,8(2):177-195P
[80]Jaydev P. Desai, Jim Ostrowski, Vijay Kumar.Controlling formations of multiple mobile robots. Proceedings of the 1998 IEEE International Conference on Robotics & Automation.Leuven, Belgium May 1998:2864-2869P
[81]Desai J P, Ostrowski J P, Kumar V. Modeling and control of formations of nonholonomic mobile robots. IEEE Transactions on Robotics and Automation,2001, 17(6):905-908P
[82]Anderson B D O, Yu C B, Dasgupta S, et al. Control of a three-coleader formation in the plane. Systems & Control Letters,2007:56(9-10):573-578P
[83]Fujimori A, Fujimoto T, Bohatcs G. Distributed leader-follower navigation of mobile robots.2005 International Conference on Control and Automation (ICCA2005). Budapest, Hungary.2005:960-965P
[84]曹志强,张斌,谭民.基于行为的多移动机器人实时队形保持.高技术通信,2001,10：74-77页
[85]石桂芬,方华京.基于相邻矩阵的多机器人编队容错控制.华中科技大学学报,2005,33(3)：39-42页
[86]Sisto M, Gu D B. A fuzzy leader-follower approach to formation control of multiple mobile robots. International Conference on Intelligent Robots and Systems. 2006:2515-2510P
[87]Shao J, Xie G, Wang L. Leader-following formation control of multiple mobile vehicles. IET Control Theory and Applications,2007,1(2):545-552P
[88]Lewis M A, Tan K H. High precision formation control of mobile robots using virtual structures, Autonomous Robots,1997,4(4):387-403P
[89]Do K D, Pan J. Nonlinear formation control of unicycle-type mobile robots. Robotics and Autonomous Systems,2007,55(3):191-204P
[90]Van den Broek T H A, Van de Wouw N, Nijmeijer H. Formation control of unicycle mobile robots:a virtual structure approach. Joint 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference Shanghai, P.R. China,2009: 8328-8333P
[91]Li Q, Jiang Z P. Formation tracking control of unicycle teams with collision avoidance. 47th IEEE Conference on Decision and Control.2008:496-501P
[92]贾秋玲,闫建国,王新民.基于势函数的多机器人系统的编队控制.机器人,2006,28(2)：111-114页
[93]Balch T, Arkin R C. Behavior-based formation control for multirobot team. IEEE Transaction on Robotics and Automation,1998,14(6):926-939P
[94]苏治宝,陆际联.多移动机器人队形控制的研究方法.机器人,2003,25(1)：88-91页
[95]Parker L E. ALLIANCE:An architecture for fault-tolerant multirobot cooperation. IEEE Transaction on Robotics and Automation,1998,14(2):220-240P
[96]Saad E M, Awadalla M H, Hamdy A M, et al. Robot formations using local sensing and limited range vision.25th National Radio Science Conference, Tanta, Egypt,2008:1-8P
[97]Lawton J R T, Beard R W, Young B J. A decentralized approach to formation maneuvers. IEEE Transactions on Robotics and Automation,2003,19(6):993-941P
[98]Cao Z Q, Xie L J, Zhang B. Formation constrained multi-robot system in unknown environments.Proceedings of the IEEE International Conference on Robotics and Automation.Taipei,China,2003:735-740P
[99]Monteiro S, Bicho E. A dynamical systems approach to behavior based formation control. Proceedings of the IEEE International Conference on Robotics and Automation. Washington, DC,2002:2606-2611P
[100]Liu Z Q, Kubota N. Hybrid learning approach based on multi-objective behavior coordination for multiple robots. Proceedings of the 2007 IEEE International Conference on Mechatronics and Automation, Harbin, China,2007:204-209P
[101]Khatib. Real-time obstacle avoidance for manipulators and mobile robots. International Journal of Robotics Research.1986,5(1):290-298P
[102]Dudenhoeffer D D, Jones M P. A formation behavior for large-scale micro-robot force deployment. Proceedings of the 2000 Winter Simulation Conference. New York, 2000:972-982P
[103]Reif J H, Wang H. Social potential fields:a distributed behavioral control for autonomous robots. Robotics and Autonomous Systems 1999,27(3):171-194P
[104]Leonard N E, Fiorelli E. Virtual leaders, artificial potentials and coordinated control of groups. Proceedings of the 40th IEEE International Conference on Decision and Control. Orlando,USA,Vol.3,2001:2968-2973P
[105]Ogren P, Fiorelli E, Leonard N E. Formations with a mission:stable coordination of vehicle group maneuvers. Proceedings of the 15th International Symposium on Mathematical Theory of Networks and Systems. Notre Dame,2002:1-15P
[106]Staber R O, Murray R M. Distributed cooperative control of multiple vehicle formations using structural potential functions. Proceedings of the 15th IFAC World Congress. Barcelona,2002:1243-1251P
[107]Baras J S, Tan X B, Hovareshti P. Decentralized control of autonomous vehicles. Proceedings of the 42nd IEEE Conference on Decision and Control. Hawaii,Vol.2, 2003:1532-1537P
[108]Ge S S, Fua C H, Lim K W. Multi-robot formations:queues and artificial potential trenches. Proceedings of the IEEE International Conference on Robotics and Automation. New Orleans,2004,2004(4):3345-3350P
[109]Liang Y, Lee H H. Decentralized formation control and obstacle avoidance for multiple robots with nonholonomic constraints. Proceedings of the 2006 American Control Conference. Minneapolis,2006:5596-5601P
[110]Desai J P. A graph theoretic approach for modeling mobile robot team formation. Journal of Robotic Systems,2002,19(11):511-525P
[111]Fierro R, Das A K.A modular architecture for formation control, Third International Workshop on Robot Motion and Control,2002:285-290P
[112]Dong W J, Farrell Jay A. Decentralized cooperative control of multiple nonholonomic dynamic systems with uncertainty. Automatica,2009,45(3):706-710P
[113]Dunbar W B. Model predictive control:Extension to coordinated multi-vehicle formations and real-time implementation. Pasadena:California Institute of Technology, 2001
[114]Dunbar W B, Murra R M. Model predictive control of coordinated multi-vehicle formations. Proceedings of the IEEE Conference on Decision and Control. Las Vegas, Vol.4,2002:4631-4636P
[115]Wesselowski K, Fierro R. A dual-mode model predictive controller for robot formations. Proceedings of the 42nd IEEE Conference on Decision and Control. Maui, Hawaii USA, Vol.4,2003:3615-3620P
[116]Xie F, Fierro R. On motion coordination of multiple vehicles with nonholonomic constraints. Proceedings of the 2007 American Control Conference.New York, USA, 2007:1888-1893P
[117]Kanjanawanishkul K, Zell A. A model-predictive approach to formation control of omnidirectional mobile robots.2008 IEEE/RSJ International Conference on Intelligent Robots and Systems. Nice, France,2008:2771-2776P
[118]Xie F, Fierro R. Stabilization of nonholonomic robot formations:a first-state contractive model predictive control approach. Journal of Computing and Information Technology.2009,17(1):37-50P
[119]Suqihara K, Suzuki I. Distributed algorithms formation of geometric patterns with many mobile robots. Journal of Robotic Systems,1996,13 (3):127-139P
[120]Young B J, Beard R W, Kelsey J M. A control scheme for improving multi-vehicle formation maneuvers. Proceedings of the American Control Conference. Arlington, USA, Vol.2,2001:709-709P
[121]王醒策,张汝波,顾国昌.基于强化学习的多机器人编队方法研究.计算机工程.2002,28(6)：15-17页
[122]Kon K, Fukushima H, Matsuno F. Multi-vehicle formation control based on branch-and-bound method compatible with collision avoidance problem. Proceedings of IEEE/RSJ International Intelligent Robots and systems,2007:3771-3776P
[123]Ren W. Consensus strategies for cooperative control of vehicle formations, IET Control Theory& Applications,2007,1(2):505-512P
[124]Kolmanovsky I, McClamroch N H. Developments in nonholonomic control problems. IEEE Control Systems Magazine,1995,15(6):20-36P
[125]Das T, Kar I N, Chaudhury S. Simple neuron-based adaptive controller for a nonholonomic mobile robot including actuator dynamics. Neurocomputing,2006, 29(16-18):2140-2151P
[126]Jean-Jacques, E. Slotine, Weiping Li.应用非线性控制.程代展等译.机械工业出版社,北京,2006:82-83页
[127]Jiang Z P, Pomet J B.Combining backstepping and time-varying techniques for a new set of adaptive controllers. Proceedings of the 33rd IEEE Conference on Decision and Control, Lake Buena Vista, FL,Vol.3,1994:2207-2212P
[128]胡跃明编著.非线性控制系统理论与应用,第2版.国防工业出版社.北京,2005：188-190页
[129]Dong W J, Kuhnert K D. Robust adaptive control of nonholonomic mobile robot with parameter and nonparameter uncertainties. IEEE Transactions on Robotics, 2005,21(2):261-266P
[130]Yuan G, Yang S X., Mitlal G S. Tracking control of a mobile robot using a neural dynamics based approach. Proceedings of the 2001 IEEE International Conference on Robotics and Automation. Seoul, Korea,2001:163-168P
[131]Liu S R, Zhang H D, Yang S X., et aL Dynamic control of a mobile robot using an adaptive neurodynamics and sliding mode strategy. Proceedings of the 5th World Congress on Intelligent Control and Automation. Hangzhou, P.R. China.2004: 5007-5011P
[132]刘金琨著.滑模变结构控制MATLAB仿真.清华大学出版社,北京.2005：35-36页
[133]Slotine J J, Sastry S S.Tracking control of nonlinear systems using sliding surfaces with application to robot manipulator. International Journal of Control.1983,38(2): 465-492P
[134]Grossberg S. Nonlinear neural networks:principles, mechanisms, and architectures. Neural Networks,1988,1(1):17-61P
[135]Kuo T C, Hong B W, Huang Y J, et al. Adaptive fuzzy controller design for robotic manipulators with sliding mode control. Proceedings of 2008 IEEE International Conference on Fuzzy Systems(FUZZ 2008). Hong Kong, China.2008:581-586P
[136]刘金国,王越超,李斌等.灾难救援机器人研究现状、关键性能及展望.机械工程学报.2006,42(12)：1-12页
[137]宋小康,谈大龙,吴镇炜等.全地形轮式移动机器人运动学建模与分析.机械工程学报.2008,44(6)：148-154页
[138]岳李勇,谢巍.含有驱动器模型的移动机器人自适应跟踪控制.控制理论与应用.2008,25(6)：1001-1006页
[139]Yu S H, Liu S, Xu H. Adaptive fuzzy trajectory tracking control of uncertain nonholonomic mobile robots. Proceedings of 6th IEEE International Conference on Industrial Informatics. Daejeon, South Korea. Piscataway:IEEE,2008:481-486P
[140]骆德渊,刘荣,李纪文.采用模糊逻辑的移动机器人轨迹跟踪.电子科技大学学报,2008,37(6)：943-946P
[141]Martins N A, Bertol D, Lombardi W, et al. Trajectory tracking of a nonholonomic mobile robot with parametric and nonparametric uncertainties:A proposed neural control. Proceedings of 16th Mediterranean Conference on Control and Automation. Congress Centre, Ajaccio, France. Piscataway:IEEE,2008:315-320P
[142]李世华,田玉平.非完整移动机器人的有限时间跟踪控制算法研究.控制与决策.2005,20(7)：750-754P
[143]马海涛,王永.输入饱和约束下的非完整移动机器人轨迹跟踪控.中国科学技术大学学报.2009,39(5)：499-503页
[144]Hu H, Woo P Y. Fuzzy supervisory sliding-mode and neural-network control for robotic manipulators. IEEE Transactions of Industrial Electronics.2006,53(3): 929-940P
[145]Ak A G, Cansever G. Three link robot control with fuzzy sliding mode controller based on RBF neural network. Proceedings of the 2006 IEEE International Symposium on Intelligent Control. Munich, Germany. Piscataway:IEEE,2006:2719-2724P
[146]Liu J K, He Y Z. Fuzzy global sliding mode control based on genetic algorithm and its application for flight simulator servo system. Chinese Journal of Mechanical Engineering,2007,20(3):13-17P
[147]林壮.欠驱动水平机械臂滑模变结构控制研究.哈尔滨工程大学博士论文.2007：47-79页
[148]Hsu C F, Cheng K H. Recurrent fuzzy-neural approach for nonlinear control using dynamic structure learning scheme. Neurocomputing,2008,71:3447-3459P
[149]王小平,曹立明编著.遗传算法：理论、应用与软件实现.西安：西安交通大学出版社：2002
[150]Goldberg D E.Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley,1989
[151]Michalewicz Z, Janikow C Z, Krawczyk J B. A modified genetic algorithm for optimal control problems. Computers & Mathematics with Applications,1992,23(12): 83-94P
[152]田景文等,人工神经网络算法研究及应用.北京理工大学出版社,2006.7：52-75页
[153]沈猛.轮式移动机器人导航控制与路径规划研究.西北工业大学博士论文.2006： 82-83页
[154]杨甜甜,刘志远,陈虹,等.移动机器人编队控制的现状与问题.智能系统学报.2007,2(4)：21-27页
[155]苏治宝,陆际联.多移动机器人队形控制的研究方法.机器人.2003,25(1)：88-91页
[156]Das A, Fierro R, Kumar V, et al. A vision-based formation control framework. IEEE Transactions on Robotics and Automation,2002,18(5):813-825P
[157]Li X H, Xiao J and Cai Z. Backstepping-based multiple-mobile robots formation control. Proceedings of 2005 IEEE/ RSJ International Conference on Intelligent Robots and Systems, Edmonton, Alta, Canada,2005:887-892P
[158]Breivik M, Subbotin M and Fossen T. Guided formation control for wheeled mobile robots. Proceedings of 9th International Conference on Control, Automation, Robotics and Vision, IEEE, Singapore,2006:1-7P
[159]Do K D. Formation-tracking control of unicycle-type mobile robots with limited sensing ranges. IEEE Transactions on Control Systems Technology,2008,16(3): 527-538P
[160]Dierks T and Jagannathan S. Neural network control of mobile robot formations using RISE feedback. IEEE Transactions on Systems, Man, and Cybernetics-PART B: Cybernetics,2009,39(2):332-347P
[161]Dierks T, Jagannathan S. Control of nonholonomic mobile robot formations: backstepping kinematics into dynamics. Proceedings of 16th IEEE International Conference on Control Applications Part of IEEE Multi-conference on Systems and Control, Singapore,2007:94-99P
[162]Dierks T and Jagannathan S. Neural network control of mobile robot formations using RISE feedback. IEEE Transactions on Systems, Man, and Cybernetics-PART B: Cybernetics,2009,39(2):332-347P
© 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 |