数字化陆用武器系统中的建模、优化与控制

陈杰; 方浩; 辛斌; 邓方

doi:10.3724/SP.J.1004.2013.00943

数字化陆用武器系统中的建模、优化与控制

doi: 10.3724/SP.J.1004.2013.00943

陈杰^1,2, ,,
方浩^1,2,
辛斌^1,2,
邓方^1,2

1.
北京理工大学自动化学院北京 100081;
2.
复杂系统智能控制与决策重点实验室北京 100081

基金项目:

国家自然科学基金(61175112),国家自然科学基金重大国际合作项目(61120106010),国家杰出青年科学基金(60925011), 北京市教育委员会共建项目专项基金资助

详细信息

通讯作者:
陈杰

计量
- 文章访问数: 2850
- HTML全文浏览量: 116
- PDF下载量: 2616
- 被引次数: 0
出版历程
- 收稿日期: 2013-04-07
- 修回日期: 2013-04-09
- 刊出日期: 2013-07-20

Modeling, Optimization and Control in Ground-based Digital Weapon Systems

CHEN Jie^{1,2
, ,},
FANG Hao^1,2,
XIN Bin^1,2,
DENG Fang^1,2

1.
School of Automation, Beijing Institute of Technology, Beijing 100081;
2.
Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing 100081

Funds:

Supported by National Natural Science Foundation of China (61175112), Projects of Major International (Regional) Joint Research Program of National Natural Science Foundation of China (61120106010), National Science Fund for Distinguished Young Scholars (60925011), and Beijing Education Committee Cooperation Building Foundation Project

摘要

摘要: 从复杂一体化武器系统的体系结构设计与优化、一体化指挥控制中的优化与决策、高速多维度运动体的参数辨识与状态估计、多智能平台的协同控制、非线性随动系统建模与控制五个方面阐述了数字化陆用武器系统中涉及的的建模、优化与控制问题, 涵盖了陆用武器系统中的指挥控制、火力控制和武器平台的控制. 在对五个方面的国内外研究现状进行论述与分析的基础上, 指出需要进一步研究的问题和未来研究展望.
- 陆用武器系统 /
- 体系结构 /
- 建模与控制 /
- 优化与决策 /
- 协同控制
Abstract: An overview of the modeling, optimization and control issues regarding digital ground-based weapon systems is provided by reviewing the previous works along five research lines: 1) design and optimization of the architecture and hierarchy of complex integrated weapon systems; 2) optimization and decision-making in integrated command and control; 3) parameter identification and state estimation of multidimensional mobile targets; 4) coordinative control of multiple autonomous platforms; and 5) modeling and control of nonlinear servo systems. The survey covers the command and control, the fire control and the platform control involved in ground-based weapon systems. Based on an introduction and analysis of current research status along the five lines, the paper also points out several significant topics which deserve further research in the future.
- Ground-based weapon systems /
- architecture and hierarchy /
- modeling and control /
- optimization and decisionmaking /
- coordination control

HTML全文

参考文献(201)

[1]	Ye M, Li C F, Chen G H, Wu J. EECS: an energy efficient clustering scheme in wireless sensor networks. In: Proceedings of the 24th IEEE International Performance, Computing, and Communications Conference. New York, USA: IEEE, 2005. 535-540
[2]	[2] Xu Y, Heidemann J, Estrin D. Geography-informed energy conservation for ad hoc routing. In: Proceedings of the 7th ACM Annual International Conference on Mobile Computing and Networking. New York, USA: Association for Computing Machinery, 2001. 70-84
[3]	[3] Deng J, Han Y S, Heinzelman W B, Varshney P K. Balanced-energy sleep scheduling scheme for high-density cluster-based sensor networks. Computer Communications, 2005, 28(14): 1631-1642
[4]	[4] Hong X Y, Xu K X, Gfria M. Scalable routing protocols for mobile ad hoc networks. IEEE Network, 2002, 16(4): 11-20
[5]	[5] Zhang B X, Mouftah H T. Efficient grid-based routing in wireless multi-hop networks. In: Proceedings of the 10th IEEE Symposium on Computers and Communications (ISCC). Cartagena, Cobobia: Institute of Electrical and Electronics Engineers Inc, 2005. 367-372
[6]	[6] Deb B, Bhatnagar S, Nath B. A Topology Discovery Algorithm for Sensor Networks with Applications to Network Management, DCS Technical Report DCS-TR-441, Rutgers University, 2001
[7]	Xing Yun-Bing, Shi Hao-Shan, Zhao Hong-Gang. Improvement of LEACH protocol based on spare nodes for wireless sensor networks. Chinese Journal of Sensors and Actuators, 2007, 20(7): 1592-1596 (邢云冰, 史浩山, 赵洪钢. 基于备用节点的无线传感器网络LEACH协议的改进. 传感技术学报, 2007, 20(7): 1592-1596)
[8]	Jia Yong-Can, Liu Yu-Hua, Xu Kai-Hua, Gao Jing-Ju. Hierarchical clustering routing scheme based on LEACH in wireless sensor network. Computer Engineering, 2009, 35(11): 74-76 (贾永灿, 刘玉华, 许凯华, 高景菊. WSN 中基于 LEACH 的多层分簇路由方案. 计算机工程, 2009, 35(11): 74-76)
[9]	Xie Shan-Shan, Bai Guang-Wei, Cao Lei. Protocols of determining connected dominating sets based on region partition. Computer Engineering and Design, 2012, 33(4): 1319-1323 (谢珊珊, 白光伟, 曹磊. 基于区域划分的连通支配集协议. 计算机工程与设计, 2012, 33(4): 1319-1323)
[10]	Bian Yong-Zhao, Wang Jun, Yu Hai-Bin, Zhang Jian-Hua. Construction of fault tolerant connected dominating sets in WSN. Application Research of Computers, 2010, 27(1): 292 -294, 313 (卞永钊, 王军, 于海斌, 张建华. 无线传感器网络中具有容错能力的连通支配集构造算法. 计算机应用研究, 2010, 27(1): 292-294, 313)
[11]	Hong Zhen, Yu Li, Zhang Gui-Jun, Chen You-Rong. Topology construction based on minimum connected dominating set for wireless sensor networks. Journal of Electronics and Information Technology, 2012, 34(8): 2000-2006 (洪榛, 俞立, 张贵军, 陈友荣. 基于最小连通支配集的无线传感网拓扑构建研究. 电子与信息学报, 2012, 34(8): 2000-2006)
[12]	Llorca J, Kalantari M, Milner S D, Davis C C. A quadratic optimization method for connectivity and coverage control in backbone-based wireless networks. Ad Hoc Networks, 2009, 7(3): 614-621
[13]	Liu Quan-Long. Research on Complex Networks Dependability [Master dissertation], Beijing University of Posts and Telecommunications, China, 2007 (刘全龙. 复杂网络可靠性研究 [硕士学位论文], 北京邮电大学, 中国, 2007)
[14]	Holme P, Kim B J, Yoon C N, Han S K. Attack vulnerability of complex networks. Physical Review E, 2002, 65(5): 96-109
[15]	Cohen R, Erez K, Ben-Avraham D, Havlin S. Resilience of the internet to random breakdowns. Physical Review Letters, 2000, 85(21): 4626-4628
[16]	Vzquez A, Moreno Y. Resilience to damage of graphs with degree correlations. Physical Review E, 2003, 67(1): 95-101
[17]	Rozenfeld A F, Cohen R, Ben-Avraham D, Havlin S. Scale-free networks on lattices. Physical Review Letters, 2002, 89(21): 695-701
[18]	Ramanathan R, Rosales-Hain R. Topology control of multihop wireless networks using transmit power adjustment. In: Proceedings of IEEE INFOCOM 2000. Tel Aviv, Israel: IEEE, 2000. 404-413
[19]	Li N, Hou J C. Topology control in heterogeneous wireless networks: problems and solutions. In: Proceedings of IEEE INFOCOM 2004. Urbana, USA: IEEE, 2004. 232-243
[20]	Butterfield J, Dantu K, Gerkey B, Jenkins O C, Sukhatme G S. Autonomous biconnected networks of mobile robots. In: Proceedings of the 6th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks and Workshops. Berlin, Germany: IEEE, 2008. 640-646
[21]	Basu P, Redi J. Movement control algorithms for realization of fault-tolerant ad hoc robot networks. IEEE Network, 2004, 18(4): 36-44
[22]	Das S, Liu H, Nayak A, Stojmenović I. A localized algorithm for bi-connectivity of connected mobile robots. Telecommunication Systems, 2009, 40(3-4): 129-140
[23]	Xu Li, Frey H, Santoro N, Stojmenovic I. Localized sensor self-deployment for guaranteed coverage radius maximization. In: Proceedings of ICC 2009 - 2009 IEEE International Conference on Communications. Dresden, Germany: IEEE, 2009. 1-5
[24]	Casteigts A, Albert J, Chaumette S, Nayak A, Stojmenović I. Biconnecting a network of mobile robots using virtual angular forces. In: Proceedings of the 72nd IEEE Vehicular Technology Conference Fall. Ottawa, Canada: IEEE, 2010. 1-5
[25]	Liu H, Chu X W, Leung Y W, Du R. Simple movement control algorithm for bi-connectivity in robotic sensor networks. IEEE Journal on Selected Areas in Communications, 2010, 28(7): 994-1005
[26]	Wu Jun, Tan Yue-Jin. Study on measure of complex network invulnerability. Journal of Systems Engineering, 2005, 20(2): 128-131 (吴俊, 谭跃进. 复杂网络抗毁性测度研究. 系统工程学报, 2005, 20(2): 128-131)
[27]	Liu Xiao-Lin, Wang Neng. Study on measures of communication network invulnerability. Journal of Shanghai Normal University (Natural Sciences), 2006, 35(5): 38-41 (刘啸林, 王能. 通信网络抗毁性量度研究. 上海师范大学学报 (自然科学版), 2006, 35(5): 38-41)
[28]	Xing Qing-Hua, Liu Fu-Xian. Modeling on area air defense optimization deployment system. Systems Engineering and Electronics, 2006, 28(5): 712-715 (邢清华, 刘付显. 区域防空部署优化系统建模. 系统工程与电子技术, 2006, 28(5): 712-715)
[29]	Han Song-Chen, Shi De-Ping. Optimization for air-defense combat configuration via simulated annealing algorithm. Acta Aeronautica Et Astronautica Sinica, 1999, 20(5): 478 -480 (韩松臣, 石德平. 基于模拟退火算法的防空作战布局优化. 航空学报, 1999, 20(5): 478-480)
[30]	Liu Ming, Li Wei-Min, Wang Ying-Long, Liu Yi-Jing. Optimization of the regional air defense disposition based on genetic algorithms. Systems Engineering and Electronics, 2003, 25(2): 191-193 (刘铭, 李为民, 王颖龙, 刘毅静. 基于遗传算法的区域防空部署优化研究. 系统工程与电子技术, 2003, 25(2): 191-193)
[31]	Wang Zhong-Jie, Li Xia, Zhou Qi-Ming, Wan Fan-Bing. Study on decision-making problems in multi-constrained deploying a radar network system. Fire Control and Command Control, 2008, 33(12): 133-136 (王中杰, 李侠, 周启明, 万凡兵. 多约束条件的雷达组网系统部署决策问题. 火力与指挥控制, 2008, 33(12): 133-136)
[32]	Liu Jian. Optimum selection and improvement of disposition schemes for ground air-defence operation. Fire Control and Command Control, 2005, 30(2): 97-99 (刘健. 地面防空作战部署方案优选与改进方法. 火力与指挥控制, 2005, 30(2): 97-99)
[33]	Chen Jie, Chen Chen, Zhang Juan, Xin Bin. Deployment optimization for point air defense based on memetic algorithm. Acta Automatica Sinica, 2010, 36(2): 242-246 (陈杰, 陈晨, 张娟, 辛斌. 基于Memetic算法的要地防空优化部署方法. 自动化学报, 2010, 36(2): 242-246)
[34]	Tanergcl T, Maras H, Gencer C, Aygnes H. A decision support system for locating weapon and radar positions in stationary point air defence. Information Systems Frontiers, 2012, 14(2): 423-444
[35]	Karasakal O, Kandiller L, zdemirel N E. A branch and bound algorithm for sector allocation of a naval task group. Naval Research Logistics, 2011, 58(7): 655-669
[36]	Cai H P, Liu J X, Chen Y W, Wang H. Survey of the research on dynamic weapon-target assignment problem. Journal of Systems Engineering and Electronics, 2006, 17(3): 559-565
[37]	Athans M. Command and control (C2) theory: a challenge to control science. IEEE Transactions on Automatic Control, 1987, 32(4): 286-293
[38]	Cetin E, Esen S T. A weapon-target assignment approach to media allocation. Applied Mathematics and Computation, 2006, 175(2): 1266-1275
[39]	Hosein P A, Athans M. Preferential Defense Strategies. Part I: The Static Case, Technical Report LIPS-P-2002, MIT Laboratory for Information and Decision Systems with Partial Support, USA, 1990
[40]	Hosein P A, Athans M. Preferential Defense Strategies. Part II: The Dynamic Case, Technical Report LIPS-P-2003, MIT Laboratory for Information and Decision Systems with partial support, USA, 1990
[41]	Lloyd S P, Witsenhausen H S. Weapons allocation is NP-complete. In: Proceedings of the 1986 IEEE Summer Simulation Conference. Reno, Nevada, USA: IEEE, 1986. 1054- 1058
[42]	Cai Huai-Ping, Liu Jing-Xu, Chen Ying-Wu. On the Markov characteristic of dynamic weapon target assignment problem. Journal of National University of Defense Technology, 2006, 28(3): 125-127 (蔡怀平, 刘靖旭, 陈英武. 动态武器目标分配问题的马尔可夫性. 国防科技大学学报, 2006, 28(3): 125-127)
[43]	Chen Ying-Wu, Cai Huai-Ping, Xing Li-Ning. An improved algorithm of policies optimization of dynamic weapon target assignment problem. Systems Engineering - Theory Practice, 2007, 27(7): 160-165 (陈英武, 蔡怀平, 邢立宁. 动态武器目标分配问题中策略优化的改进算法. 系统工程理论与实践, 2007, 27(7): 160-165)
[44]	Yang Zu-Kuai, Liu Ding-Chen. Optimization model analysis of dynamic WTA based on Markov decision-making. Fire Control Command Control, 2003, 28(5): 25-27 (杨祖快, 刘鼎臣. 基于马尔柯夫决策过程动态WTA最优化模型分析. 火力与指挥控制, 2003, 28(5): 25-27)
[45]	Xin B, Chen J, Peng Z H, Dou L H, Zhang J. An efficient rule-based constructive heuristic to solve dynamic weapon-target assignment problem. IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans, 2011, 41(3): 598-606
[46]	Li J J, Cong R, Xiong J G. Dynamic WTA optimization model of air defense operation of warships' formation. Journal of Systems Engineering and Electronics, 2006, 17(1): 126-131
[47]	You Zhi-Feng, Li Yong, Liang Yu, Hao Hai-Yan. Analysis of the evolutionary group's decision-making mechanism for the large area air defence. Modern Defense Technology, 2005, 33(4): 14-17 (尤志锋, 李勇, 梁宇, 郝海燕. 大区域防空的进化群决策机制研究. 现代防御技术, 2005, 33(4): 14-17)
[48]	Galati D G, Simaan M A. Near-Nash targeting strategies for heterogeneous teams of autonomous combat vehicles. In: Proceedings of the 2008 SPIE 6962, Unmanned Systems Technology X. Orlando, FL, USA: SPIE, 2008. DOI: 10.1117/12.782108
[49]	Pan Shu-Shan, Wu Xiao-Yun, Ma Da-Wei, Qiao Yan-Ling. Weapon-target assignment based on the theory of rough sets. Journal of Projectiles, Rockets, Missiles and Guidance, 2005, 25(1): 56-59 (潘书山, 吴晓云, 马大为, 乔艳玲. 基于粗集理论的武器目标分配. 弹箭与制导学报, 2005, 25(1): 56-59)
[50]	He Zheng-Hong, Zhang Jin-Cheng. A firepower assignment model of aerial defense based on expert systems. Systems Engineering and Electronics, 2001, 23(7): 44-46 (贺正洪, 张金成. 基于专家系统的防空火力分配模型. 系统工程与电子技术, 2001, 23(7): 44-46)
[51]	Sahin M A, Leblebicioglu K. A standard expert system for weapon target assignment problem. In: Proceedings of the 2009 International Symposium on Performance Evaluation and Computer Telecommunication Systems. Ankara, Turkey: IEEE, 2009. 221-224
[52]	Xin B, Chen J, Zhang J, Fang H, Peng Z H. Hybridizing differential evolution and particle swarm optimization to design powerful optimizers: a review and taxonomy. IEEE Transactions on Systems, Man, and Cybernetics Part C: Applications and Reviews, 2012, 42(5): 744-767
[53]	Jackson K, Farbman M S. Trajectory reconstruction with a least squares sliding window (LSSW) filter. In: Proceedings of the 2007 AIAA Guidance, Navigation, and Control Conference. Hilton Head, SC, United States: AIAA, 2007. 2058 -2080
[54]	Janczak D, Sankowski M. Data fusion for ballistic targets tracking using least squares. AEU International Journal of Electronics and Communications, 2012, 66(6): 512-519
[55]	Farina A, Ristic B, Benvenuti D. Tracking a ballistic target: comparison of several nonlinear filters. IEEE Transactions on Aerospace and Electronic Systems, 2002, 38(3): 854-867
[56]	Du C P, Sun H D, Zhou D Y, Song B F. On ballistic parameter identification method based on ant colony algorithm. Electronics Optics and Control, 2008, 15(7): 4-6, 19
[57]	Dai Yao, Wang De-Hu, Ma Ye. Study of nonlinear ballistic parameters identification based on neural network contrary model. Journal of Ballistics, 2005, 17(1): 18-22 (戴耀, 汪德虎, 马野. 基于神经网络逆模型的非线性外弹道参数辨识. 弹道学报, 2005, 17(1): 18-22)
[58]	Wacholder E. A neural network-based optimization algorithm for the static weapon-target assignment problem. INFORMS Journal on Computing, 1989, 1(4): 232-246
[59]	Kang Ying-Jun, Li Wei-Min, Li Xu-Wu. A study of the optimal aerial defense firepower distribution based on HNN. Fire Control and Command Control, 2003, 28(6): 35-37 (康英军, 李为民, 李续武. Hopfield神经网络的防空火力最优分配问题. 火力与指挥控制, 2003, 28(6): 35-37)
[60]	Bertsekas D P, Homer M L, Logan D A, Patek S D, Sandell N S. Missile defense and interceptor allocation by neuro-dynamic programming. IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans, 2000, 30(1): 42-51
[61]	Cai Huai-Ping, Chen Ying-Wu, Xing Li-Ning. Research on dynamic weapon target assignment problem based on SVNTS algorithm. Computer Engineering and Applications, 2006, 42(31): 7-10, 22 (蔡怀平, 陈英武, 邢立宁. SVNTS算法的动态武器目标分配问题研究. 计算机工程与应用, 2006, 42(31): 7-10, 22)
[62]	Bogdanowicz Z R. A new efficient algorithm for optimal assignment of smart weapons to targets. Computers and Mathematics with Applications, 2009, 58(10): 1965-1969
[63]	Blodgett D E, Gendreau M, Guertin F, Potvin J Y, Sguin R. A tabu search heuristic for resource management in naval warfare. Journal of Heuristics, 2003, 9(2): 145-169
[64]	Madni A M, Andrecut M. Efficient heuristic approach to the weapon-target assignment problem. Journal of Aerospace Computing, Information, and Communication, 2009, 6(6): 405-414
[65]	Ahuja R K, Kumar A, Jha K C, Orlin J B. Exact and heuristic algorithms for the weapon-target assignment problem. Operations Research, 2007, 55(6): 1136-1146
[66]	Lee M Z. Constrained weapon-target assignment: enhanced very large scale neighborhood search algorithm. IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans, 2010, 40(1): 198-204
[67]	Malhotra A, Jain R K. Genetic algorithm for optimal weapon allocation in multilayer defence scenario. Defence Science Journal, 2001, 51(3): 285-293
[68]	Liu Mei, Zhao Gang, Quan Tai-Fan. New-type genetic algorithm for weapon-target assignment of the antiaircraft command system. Systems Engineering and Electronics, 2005, 27(3): 456-460 (刘梅, 赵刚, 权太范. 新型遗传算法在防空指挥系统目标分配中的应用. 系统工程与电子技术, 2005, 27(3): 456-460)
[69]	Wang Wei, Cheng Shu-Chang, Zhang Yu-Zhi. Research on approach for a type of weapon target assignment problem solving by genetic algorithm. Systems Engineering and Electronics, 2008, 30(9): 1708-1711 (王玮, 程树昌, 张玉芝. 基于遗传算法的一类武器目标分配方法研究. 系统工程与电子技术, 2008, 30(9): 1708-1711)
[70]	Lu H Q, Zhang H J, Zhang X J, Han R X. An improved genetic algorithm for target assignment, optimization of naval fleet air defense. In: Proceedings of the 6th World Congress on Intelligent Control and Automation. Dalian, China: IEEE, 2006. 3401-3405
[71]	Ma Hai-Tao, Zhao Wei-Dong. The fire distribution problem of ADG made up of AGM based on genetic algorithm. Fire Control and Command Control, 2006, 31(4): 36-38 (马海涛, 赵伟东. 基于遗传算法的弹炮混编防空群火力分配. 火力与指挥控制, 2006, 31(4): 36-38)
[72]	Wu Ling, Lu Fa-Xing, Jia Pei-Fa. Meta-level control of the anytime algorithm for the dynamic weapon-target allocation problem. Journal of Tsinghua University (Science and Technology), 2008, 48(S2): 1762-1765 (吴玲, 卢发兴, 贾培发. 动态武器目标分配问题中改进遗传算法的元级控制. 清华大学学报(自然科学版), 2008, 48(S2): 1762-1765)
[73]	Xiu Chun-Bo, Liu Xiang-Dong, Zhang Yu-He, Tang Yun-Yu. A chaos optimization algorithm for firepower distribution. Fire Control and Command Control, 2006, 31(1): 14- 16 (修春波, 刘向东, 张宇河, 唐运虞. 一种用于求解火力分配问题的混沌优化算法. 火力与指挥控制, 2006, 31(1): 14-16)
[74]	Deng Chang-Shou, Liang Chang-Yong. Hybrid coding differential evolution algorithm for weapon-target assignment problem. Application Research of Computers, 2009, 26(1): 74-76 (邓长寿, 梁昌勇. 求解武器--目标分配问题的混合编码差异演化算法. 计算机应用研究, 2009, 26(1): 74-76)
[75]	Zeng X P, Zhu Y L, Nan L, Hu K Y, Niu B, He X X. Solving weapon-target assignment problem using discrete particle swarm optimization. In: Proceedings of the 6th World Congress on Intelligent Control and Automation. Dalian, China: IEEE, 2006. 3562-3565
[76]	Wang L, Wang H Y, Qiu Z M. An improved artificial immune algorithm for solving weapon-target assignment problem. In: Proceedings of the 7th World Congress on Intelligent Control and Automation. Chongqing, China: IEEE, 2008. 8622-8625
[77]	Kwon O, Lee K, Kang D H, Park S. A branch-and-price algorithm for a targeting problem. Naval Research Logistics, 2007, 54(7): 732-741
[78]	Karasakal O. Air defense missile-target allocation models for a naval task group. Computers and Operations Research, 2008, 35(6): 1759-1770
[79]	Lee Z J, Su S F, Lee C Y. An immunity-based ant colony optimization algorithm for solving weapon-target assignment problem. Applied Soft Computing, 2002, 2(1): 39-47
[80]	Lee Z J, Su S F, Lee C Y. Efficiently solving general weapon-target assignment problem by genetic algorithms with greedy eugenics. IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics, 2003, 33(1): 113- 121
[81]	Fu T P, Liu Y S, Chen J H. Improved genetic and ant colony optimization algorithm for regional air defense WTA problem. In: Proceedings of the 1st International Conference on Innovative Computing, Information and Control. Beijing, China: IEEE, 2006. 226-229
[82]	Bisht S. Hybrid genetic-simulated annealing algorithm for optimal weapon allocation in multilayer defence scenario. Defence Science Journal, 2004, 54(3): 395-405
[83]	Khosla D, Nichols T. Hybrid evolutionary algorithms for network-centric command and control. In: Proceedings of SPIE 6249, Defense Transformation and Network-Centric Systems. Orlando, FL, USA: SPIE, 2006. DOI: 10.1117/12.782108
[84]	Wang Xiao-Yi, Hou Chao-Zhen, Yuan Ju-Mei, Guo Fei, Hao Wei. Modeling and optimization method on antiaircraft firepower allocation. Control and Decision, 2006, 21(8): 913- 917 (王小艺, 侯朝桢, 原菊梅, 郭飞, 郝伟. 防空火力分配建模及优化方法研究. 控制与决策, 2006, 21(8): 913-917)
[85]	Chen Hua-Dong, Wang Shu-Zong, Wang Hang-Yu. Research of firepower assignment with multi-launcher and multi-weapon based on a hybrid particle swarm optimization. Systems Engineering and Electronics, 2008, 30(5): 880-883 (陈华东, 王树宗, 王航宇. 基于混合粒子群算法的多平台多武器火力分配研究. 系统工程与电子技术, 2008, 30(5): 880-883)
[86]	Ding Zhu, Ma Da-Wei, Tang Ming-Duan, Zhang Xue-Feng. TSAPSO: a hybrid search algorithm of tabu search and annealing particle swarm optimization for weapon-target assignment. Journal of System Simulation, 2006, 18(9): 2480 -2483 (丁铸, 马大为, 汤铭端, 张学锋. 基于禁忌退火粒子群算法的火力分配. 系统仿真学报, 2006, 18(9): 2480-2483)
[87]	Chen J, Xin B, Peng Z H, Dou L H, Zhang J. Evolutionary decision-makings for the dynamic weapon-target assignment problem. Science in China Series F: Information Sciences, 2009, 52(11): 2006-2018
[88]	Xin B, Chen J, Zhang J, Dou L H, Peng Z H. Efficient decision makings for dynamic weapon-target assignment by virtual permutation and tabu search heuristics. IEEE Transactions on Systems, Man, and Cybernetics Part C: Applications and Reviews, 2010, 40(6): 649-662
[89]	Xin B, Chen J. An estimation of distribution algorithm with efficient constructive repair/improvement operator for the dynamic weapon-target assignment. In: Proceedings of the 31st Chinese Control Conference. Hefei, China: IEEE, 2012. 2346-2351
[90]	Grewal M S, Glover K. Identifiability of linear and nonlinear dynamical systems. IEEE Transactions on Automatic Control, 1976, 21(6): 833-837
[91]	Berntsen H E, Balchen J G. Identifiability of linear dynamical systems. In: Proceedings of the 3rd IFAC Symposium on Identification and System Parameter Estimation. The Hague, Delft, Netherlands: North Holland, 1973. 871-874
[92]	Siferd R E, Maybeck P S. Identifiability of nonlinear dynamical systems. In: Proceedings of the 21st IEEE Conference on Decision and Control. Orlando, FL, USA: IEEE, 1982. 1167-1171
[93]	Goodrich R L, Caines P E. Necessary and sufficient conditions for local second-order identifiability. IEEE Transactions on Automatic Control, 1979, 24(1): 125-127
[94]	Neemcov J. Structural identifiability of polynomial and rational systems. Mathematical Biosciences, 2010, 223(2): 83 -96
[95]	Evans N D, Chapman M J, Chappell M J, Godfrey K R. Identifiability of uncontrolled nonlinear rational systems. Automatica, 2002, 38(10): 1799-1805
[96]	Wang L Y, Yin G G, Zhang J F. Joint identification of plant rational models and noise distribution functions using binary-valued observations. Automatica, 2006, 42(4): 535- 547
[97]	Fliess M. Local realization of linear and nonlinear time-varying systems. In: Proceedings of the 21st IEEE Conference on Decision and Control. Orlando, FL, USA: IEEE, 1982. 733-738
[98]	Tunali E, Tarn T J. New results for identifiability of nonlinear systems. IEEE Transactions on Automatic Control, 1987, 32(2): 146-154
[99]	Joly-Blanchard G, Denis-Vidal L. Some remarks about an identifiability result of nonlinear systems. Automatica, 1998, 34(9): 1151-1152
[100]	Denis-Vidal L, Joly-Blanchard G. An easy to check criterion for (un)indentifiability of uncontrolled systems and its applications. IEEE Transactions on Automatic Control, 2000, 45(4): 768-771
[101]	Pohjanpalo H. System identifiability based on the power series expansion of the solution. Mathematical Biosciences, 1978, 41(1-2): 21-33
[102]	Walter E. Identifiability of State Space Models: with Applications to Transformation Systems. Berlin: Springer-Verlag, 1982
[103]	Denis-Vidal L, Joly-Blanchard G, Noiret C. Some effective approaches to check the identifiability of uncontrolled nonlinear systems. Mathematics and Computers in Simulation, 2001, 57(1-2): 35-44
[104]	Diop S, Fliess M. Nonlinear observability, identifiability, and persistent trajectories. In: Proceedings of the 30th IEEE Conference on Decision and Control. Brighton, UK: IEEE, 1991. 714-719
[105]	Ljung L, Glad T. On global identifiability for arbitrary model parametrizations. Automatica, 1994, 30(2): 265-276
[106]	Denis-Vidal L, Joly-Blanchard G. Equivalence and identifiability analysis of uncontrolled nonlinear dynamical systems. Automatica, 2004, 40(2): 287-292
[107]	Yates J W, Evans N D, Chappell M J. Structural identifiability analysis via symmetries of differential equations. Automatica, 2009, 45(11): 2585-2591
[108]	Strejc V. Least squares parameter estimation. Automatica, 1980, 16(5): 535-550
[109]	Mowery V. Least squares recursive differential-corection estimation in nonlinear problems. IEEE Transactions on Automatic Control, 1965, 10(4): 399-407
[110]	Kukreja S L, Kearney R E, Galiana H L. A least-squares parameter estimation algorithm for switched Hammerstein systems with applications to the VOR. IEEE Transactions on Biomedical Engineering, 2005, 52(3): 431-444
[111]	Vajda S, Valk P, Godfrey K R. Direct and indirect least squares methods in continuous-time parameter estimation. Automatica, 1987, 23(6): 707-718
[112]	Angeby J. Estimating signal parameters using the nonlinear instantaneous least squares approach. IEEE Transactions on Signal Processing, 2000, 48(10): 2721-2732
[113]	Goethals I, Pelckmans K, Suykens J A K, De Moor B. Identification of MIMO Hammerstein models using least squares support vector machines. Automatica, 2005, 41(7): 1263- 1272
[114]	Gao Jian, He Bing-Geng. Neural network for solving nonlinear least squares problem. Journal of Mathematics for Technology, 2002, 18(4): 29-31 (高坚, 贺秉庚. 用神经网络解非线性最小二乘问题. 工科数学, 2002, 18(4): 29-31)
[115]	Kalman R E. A new approach to linear filtering and prediction problems. Transactions of the ASME Journal of Basic Engineering, 1960, 82: 35-45
[116]	Chen Jie, Deng Fang, Chen Wen-Jie. Parameters identification from indirect data and its application in the identification of ballistic parameters. Transactions of Beijing Institute of Technology, 2007, 27(S1): 118-122 (陈杰, 邓方, 陈文颉. 基于间接数据的参数辨识及其在弹道模型中的应用. 北京理工大学学报, 2007, 27(S1): 118-122)
[117]	Deng Fang, Chen Jie, Bai Yong-Qiang. Identification of ballistic parameters based on virtual ballistic trajectory data from firing tables. In: Proceedings of the 29th Chinese Control Conference. Beijing, China: IEEE, 2010. 1236-1241 (邓方, 陈杰, 白永强. 基于射表虚拟弹道数据的弹道模型参数辨识. 第29届中国控制会议. 北京, 中国: IEEE, 2010. 1236-1241)
[118]	Reif K, Guenther S, Yaz E, Unbehauen R. Stochastic stability of the continuous-time extended Kalman filter. IEE Proceedings Control Theory and Applications, 2000, 147(1): 45 -52
[119]	Reif K, Guenther S, Yaz E, Unbehauen R. Stochastic stability of the discrete-time extended Kalman filter. IEEE Transactions on Automatic Control, 1999, 44(4): 714-728
[120]	Norgaard M, Poulsen N K, Ravn O. New developments in state estimation for nonlinear systems. Automatica, 2000, 36(11): 1627-1638
[121]	Bierman G J. Measurement updating using the U-D factorization. Automatica, 1976, 12(4): 375-382
[122]	Arasaratnam I, Haykin S. Square-root quadrature Kalman filtering. IEEE Transactions on Signal Processing, 2008, 56(6): 2589-2593
[123]	Schei T S. A finite-difference method for linearization in nonlinear estimation algorithms. Automatica, 1997, 33(11): 2053-2058
[124]	Zhou Dong-Hua, Xi Yu-Geng, Zhang Zhong-Jun. A suboptimal multiple fading extended Kalman filter. Acta Automatica Sinica, 1991, 17(6): 689-695 (周东华, 席裕庚, 张钟俊. 一种带多重次优渐消因子的扩展卡尔曼滤波器. 自动化学报, 1991, 17(6): 689-695)
[125]	Chen Jie, Deng Fang, Chen Wen-Jie, Ma Tao. Parameter identification of nonlinear system and its application based on strong tracking filter and wavelet transform. Control Theory and Applications, 2010, 27(6): 738-744 (陈杰, 邓方, 陈文颉, 马韬. 基于强跟踪滤波器及小波变换的非线性系统参数辨识及应用. 控制理论与应用, 2010, 27(6): 738-744)
[126]	Gordon N J, Salmond D J, Smith A F. Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F (Radar and Signal Processing), 1993, 140(2): 107-113
[127]	Arulampalam M S, Maskell S, Gordon N, Clapp T. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing, 2002, 50(2): 174-188
[128]	Julier S J, Uhlmann J K, Durrant-Whyte H F. A new approach for filtering nonlinear systems. In: Proceedings of the 1995 American Control Conference. Seattle, WA, USA: American Automatic Control Council, 1995. 1628-1632
[129]	Van der Merwe R. Sigma-point Kalman Filters for Probabilistic Inference in Dynamic State-space Models [Ph.D. dissertation], Oregon Health and Sciences University, USA, 2004
[130]	Sarkka S. On unscented Kalman filtering for state estimation of continuous-time nonlinear systems. IEEE Transactions on Automatic Control, 2007, 52(9): 1631-1641
[131]	Wan E A, Van der Merwe R. The unscented Kalman filter for nonlinear estimation. In: Proceedings of the 2000 IEEE Adaptive Systems for Signal Processing, Communications, and Control Symposium. Lake Louise, Alta., Canada: IEEE, 2000. 153-158
[132]	VanDyke M C, Schwartz J L, Hall C D. Unscented Kalman filtering for spacecraft attitude state and parameter estimation. Advances in the Astronautical Sciences, 2005, 119(Sup): 217-228
[133]	Julier S J, Uhlmann J K. Reduced sigma point filters for the propagation of means and covariances through nonlinear transformations. In: Proceedings of the 2002 American Control Conference. Anchorage, AK, USA: IEEE, 2002. 887- 892
[134]	Holmes S, Klein G, Murray D W. A square root unscented Kalman filter for visual monoSLAM. In: Proceedings of the 2008 IEEE International Conference on Robotics and Automation. Pasadena, CA, USA: IEEE, 2008. 3710-3716
[135]	Deng Zhi-Hong, Yan Li-Ping, Fu Meng-Yin. Multirate multisensor data fusion based on missing measurements. Systems Engineering and Electronics, 2010, 32(5): 886-890 (邓志红, 闫莉萍, 付梦印. 基于不完全观测数据的多速率多传感器数据融合. 系统工程与电子技术, 2010, 32(5): 886-890)
[136]	Zhao Wen-Ce, Pan Jian-Ping, Chen Wei-Li. The processing method of incomplete instrumentation data based on consideration of trajectory dynamic characteristics. Journal of Spacecraft TT C Technology, 2006, 25(6): 64-68 (赵文策, 潘建平, 陈伟利. 基于弹道动力特性考虑的不完全测量数据处理方法. 飞行器测控学报, 2006, 25(6): 64-68)
[137]	Wang Yuan-Yuan, Zhang Jun, Zhu Yan-Bo, Lin Xi. Asynchronous multi-rate sensor information fusion algorithm based on missing measurements. Journal of Huazhong University of Science and Technology (Nature Science Edition), 2009, 37(S1): 271-274 (王媛媛, 张军, 朱衍波, 林熙. 异步多速率传感器不完全观测信息融合算法. 华中科技大学学报 (自然科学版), 2009, 37(S1): 271- 274)
[138]	Wang Z D, Shen B, Liu X H. H filtering with randomly occurring sensor saturations and missing measurements. Automatica, 2012, 48(3): 556-562
[139]	Liang H Y, Zhou T. Robust state estimation for uncertain discrete-time stochastic systems with missing measurements. Automatica, 2011, 47(7): 1520-1524
[140]	Ma L F, Wang Z D, Hu J, Bo Y M, Guo Z. Robust variance-constrained filtering for a class of nonlinear stochastic systems with missing measurements. Signal Processing, 2010, 90(6): 2060-2071
[141]	Meng J, Egerstedt M. Distributed coordination control of multiagent systems while preserving connectedness. IEEE Transactions on Robotics, 2007, 23(4): 693-703
[142]	Zavlanos M M, Pappas G J. Potential fields for maintaining connectivity of mobile networks. IEEE Transactions on Robotics, 2007, 23(4): 812-816
[143]	Zavlanos M M, Jadbabaie A, Pappas G J. Flocking while preserving network connectivity. In: Proceedings of the 46th IEEE Conference on Decision and Control. New Orleans, LA, USA: IEEE, 2007. 2919-2923
[144]	Spanos D P, Murray R M. Motion planning with wireless network constraints. In: Proceedings of the 2005 American Control Conference. Portland, USA: IEEE, 2005. 87-92
[145]	Pereira G A S, Kumar V, Campos M F M. Closed loop motion planning of cooperating mobile robots using graph connectivity. Robotics and Autonomous Systems, 2008, 56(4): 373-384
[146]	Notarstefano G, Savla K, Bullo F, Jadbabaie A. Maintaining limited-range connectivity among second-order agents. In: Proceedings of the 2006 American Control Conference. Minneapolis, MN, USA: IEEE, 2006. 2124-2129
[147]	Zavlanos M M, Tahbaz-Salehi A, Jadbabaie A, Pappas G J. Distributed topology control of dynamic networks. In: Proceedings of the 2008 American Control Conference. Seattle, WA: IEEE, 2008. 2660-2665
[148]	Schuresko M, Corts J. Distributed motion constraints for algebraic connectivity of robotic networks. Journal of Intelligent and Robotic Systems, 2009, 56(1-2): 99-126
[149]	Mesbahi M. On state-dependent dynamic graphs and their controllability properties. IEEE Transactions on Automatic Control, 2005, 50(3): 387-392
[150]	Yoonsoo K, Mesbahi M. On maximizing the second smallest eigenvalue of a state-dependent graph Laplacian. IEEE Transactions on Automatic Control, 2006, 51(1): 116-120
[151]	Xiao L, Boyd S. Fast linear iterations for distributed averaging. Systems and Control Letters, 2004, 53(1): 65-78
[152]	Corts J, Martinez S, Bullo F. Robust rendezvous for mobile autonomous agents via proximity graphs in arbitrary dimensions. IEEE Transactions on Automatic Control, 2006, 51(8): 1289-1298
[153]	Schuresko M D, Corts J. Safe graph rearrangements for distributed connectivity of robotic networks. In: Proceedings of the 46th IEEE Conference on Decision and Control. New Orleans, LA: IEEE, 2007. 4602-4607
[154]	Gustavi T, Dimarogonas D V, Egerstedt M, Hu X M. Sufficient conditions for connectivity maintenance and rendezvous in leader-follower networks. Automatica, 2010, 46(1): 133-139
[155]	Su H S, Wang X F, Chen G R. A connectivity-preserving flocking algorithm for multi-agent systems based only on position measurements. International Journal of Control, 2009, 82(7): 1334-1343
[156]	Liu Zhi-Xin, Guo Lei. Connectivity and synchronization of multi-agent systems. In: Proceedings of the 25th Chinese Control Conference. Harbin, China: IEEE, 2006. 373-378 (刘志新, 郭雷. 多个体系统的连通与同步. 第25届中国控制会议. 哈尔滨,中国: IEEE, 2006. 373-378)
[157]	Li X L, Xi Y G. Distributed cooperative coverage and connectivity maintenance for mobile sensing devices. In: Proceedings of the 48th IEEE Conference on Decision and Control. Shanghai, China: IEEE, 2009. 7891-7896
[158]	Chen Shi-Ming, Fang Hua-Jing. Modeling and stability analysis of large-scale intelligent swarm. Control and Decision, 2005, 20(5): 490-494 (陈世明, 方华京. 大规模智能群体的建模及稳定性分析. 控制与决策, 2005,20(5): 490-494)
[159]	Mao Y T, Dou L H, Fang H, Liu H G. Distributed motion coordination for multi-agent systems with connectivity maintenance using backbone-based networks. In: Proceedings of the 18th IFAC World Congress. Milano, Italy: IFAC, 2011. 13588-13593
[160]	Yamaguchi H. A distributed motion coordination strategy for multiple nonholonomic mobile robots in cooperative hunting operations. Robotics and Autonomous Systems, 2003, 43(4): 257-282
[161]	Tanner H G, Jadbaaie A, Pappas G J. Flocking in teams of nonholonomic agents. In: Proceedings of the 2003 Block Island Workshop on Cooperative Control. Block Island, RI, USA: Springer-Verlag, 2002. 229-239
[162]	Chang D E, Marsden J E. Gyroscopic forces and collision avoidance with convex obstacles. New Trends in Nonlinear Dynamics and Control and Their Applications, 2004, 295(1): 145-159
[163]	Olfati-Saber R. Flocking for multi-agent dynamic systems: algorithms and theory. IEEE Transactions on Automatic Control, 2006, 51(3): 401-420
[164]	Mao Y T, Dou L H, Fang H, Liu H G, Cao H. Connectivity-preserving flocking of multi-agent systems with application to wheeled mobile robots. In: Proceedings of the 29th Chinese Control Conference. Beijing, China: IEEE, 2010. 4494 -4500
[165]	Waydo S, Murray R M. Vehicle motion planning using stream functions. In: Proceedings of the 2003 IEEE International Conference on Robotics and Automation. Taipei, China: IEEE, 2003. 2484-2491
[166]	Sullivan J, Waydo S, Campbell M. Using stream functions for complex behavior and path generation. In: Proceedings of the 2003 AIAA Guidance, Navigation, and Control Conference. Austin, Texas: AIAA, 2003. 3-5
[167]	Daily R, Bevly D M. Harmonic potential field path planning for high speed vehicles. In: Proceedings of the 2008 American Control Conference. Seattle, Washington: IEEE, 2008. 4609-4614
[168]	Lu Jun, Guan Zhi-Hong, Wang Hua. Multiple mobile robots swarming control model based on stream function. Robot, 2006, 28(3): 265-268, 274 (卢骏, 关治洪, 王华. 基于流函数的多移动机器人Swarming控制模型.机器人, 2006, 28(3): 265-268, 274)
[169]	Guo Teng-Fei, Wang Hong-Lun, Liang Xiao. Path planning based on stream function method for UAV. Tactical Missile Technology, 2011, (5): 27-32 (郭腾飞, 王宏伦, 梁宵. 基于流函数法的无人机航路规划. 战术导弹技术,2011, (5): 27-32)
[170]	Cao Meng-Lei, Wang Hong-Lun, Liang Xiao. Route planning for UAVs using improved stream function method. Electronics Optics and Control, 2012, 19(2): 1-4, 16 (曹梦磊, 王宏伦, 梁宵. 采用改进流函数法的无人机航路规划. 电光与控制,2012, 19(2): 1-4, 16)
[171]	Wang Q, Fang H, Chen J, Mao Y T, Dou L H. Flocking with obstacle avoidance and connectivity maintenance in multi-agent systems. In: Proceedings of the 51st IEEE Conference on Decision and Control. Hawaii, USA: IEEE, 2012. 4009- 4014
[172]	Yao B. High performance adaptive robust control of nonlinear systems: a general framework and new schemes. In: Proceedings of the 36th IEEE Conference on Decision and Control. New York, USA: IEEE, 1997. 2489-2494
[173]	Shahnazi R, Akbarzadeh-T M R. PI adaptive fuzzy control with large and fast disturbance rejection for a class of uncertain nonlinear systems. IEEE Transactions on Fuzzy Systems, 2008, 16(1): 187-197
[174]	Armstrong-Helouvry B. Stick slip and control in low-speed motion. IEEE Transactions on Automatic Control, 1993, 38(10): 1483-1496
[175]	Ren B B, Ge S S, Su C Y, Tong H L. Adaptive neural control for a class of uncertain nonlinear systems in pure-feedback form with hysteresis input. IEEE Transactions on Systems, Man, and Cybernetics -- Part B: Cybernetics, 2009, 39(2): 431-443
[176]	Zhou J, Meng J E, Zurada J M. Adaptive neural network control of uncertain nonlinear systems with nonsmooth actuator nonlinearities. Neurocomputing, 2007, 70(4-6): 1062-1070
[177]	Jang J O. Neural network saturation compensation for dc motor systems. IEEE Transactions on Industrial Electronics, 2007, 54(3): 1763-1767
[178]	Leonessa A, Haddad W M, Hayakawa T, Morel Y. Adaptive control for nonlinear uncertain systems with actuator amplitude and rate saturation constraints. International Journal of Adaptive Control and Signal Processing, 2009, 23(1): 73 -96
[179]	Boiko I, Fridman L, Pisano A, Usai E. Analysis of chattering in systems with second-order sliding modes. IEEE Transactions on Automatic Control, 2007, 52(11): 2085-2102
[180]	Khalil H K. Nonlinear Systems (Third edition). Beijing: Publishing House of Electronics Industry, 2002
[181]	van der Schaft A, Schumacher H. An Introduction to Hybrid Dynamical Systems. London: Springer-Verlag, 2000
[182]	Bacciotti A, Rosier L. Liapunov Functions and Stability in Control Theory (Second edition). Berlin Heidelberg: Springer, 2005
[183]	Krstic M, Kanellakopoulos I, Kokotovic P. Nonlinear and Adaptive Control Design. New York: John Wiley, 1995
[184]	Yao B, Tomizuka M. Adaptive robust control of MIMO nonlinear systems in semi-strict feedback forms. Automatica, 2001, 37(9): 1305-1321
[185]	Yao B, Tomizuka M. Adaptive robust control of SISO nonlinear systems in a semi-strict feedback form. Automatica, 1997, 33(5): 893-900
[186]	Wang D, Huang J. Neural network-based adaptive dynamic surface control for a class of uncertain nonlinear systems in strict-feedback form. IEEE Transactions on Neural Networks, 2005, 16(1): 195-202
[187]	Yang Z J, Nagai T, Kanae S, Wada K. Dynamic surface control approach to adaptive robust control of nonlinear systems in semi-strict feedback form. International Journal of Systems Science, 2007, 38(9): 709-724
[188]	Yao B, Xu L. Output feedback adaptive robust control of uncertain linear systems with disturbances. Journal of Dynamic Systems, Measurement, and Control, 2006, 128(4): 938-945
[189]	Yao B, Palmer A. Indirect adaptive robust control of SISO nonlinear systems in semi-strict feedback forms. In: Proceeding of the 15th IFAC Triennial World Congress. Barcelona, Spain: IFAC, 2002. 1050-1056
[190]	Yao B. Integrated direct/indirect adaptive robust control of SISO nonlinear systems in semi-strict feedback form. In: Proceedings of the 2003 American Control Conference. Piscataway, NJ, USA: IEEE, 2003. 3020-3025
[191]	Zhang G Z, Chen J, Li Z P. Adaptive robust control for servo mechanisms with partially unknown states via dynamic surface control approach. IEEE Transactions on Control Systems Technology, 2010, 18(3): 723-731
[192]	Karsenti L, Lamnabhi-Lagarrigue F, Bastin G. Adaptive control of nonlinear systems with nonlinear parameterization. System and Control Letters, 1996, 27(2): 87-97
[193]	Kojić A, Annaswamy A M. Adaptive control of nonlinearly parameterized systems with a triangular structure. Automatica, 2002, 38(1): 115-123
[194]	Yokoi K, Hung N V Q, Tuan H D, Hosoe S. Adaptive control design for nonlinearly parameterized systems with a triangular structure. Asian Journal of Control, 2007, 9(2): 121-132
[195]	Hung N V Q, Tuan H D, Narikiyo T, Apkarian P. Adaptive control for nonlinearly parameterized uncertainties in robot manipulators. IEEE Transactions on Control Systems Technology, 2008, 16(3): 458-468
[196]	Qu Z H, Hull R A, Wang J. Globally stabilizing adaptive control design for nonlinearly-parameterized systems. IEEE Transactions on Automatic Control, 2006, 51(6): 1073- 1079
[197]	Liu X B, Ortega R, Su H Y, Chu J. Immersion and invariance adaptive control of nonlinearly parameterized nonlinear systems. IEEE Transactions on Automatic Control, 2010, 55(9): 2209-2221
[198]	Li Z, Chen J, Zhang G, Gan M G. Adaptive robust control for DC motors with input saturation. IET Control Theory and Applications, 2011, 5(16): 1895-1905
[199]	Wu J L. Stabilizing controllers design for switched nonlinear systems in strict-feedback form. Automatica, 2009, 45(4): 1092-1096
[200]	Han T T, Ge S S, Lee T H. Adaptive neural control for a class of switched nonlinear systems. Systems and Control Letters, 2009, 58(2): 109-118
[201]	Zhang G Z, Chen J, Li Z P. Identifier-based adaptive robust control for servomechanisms with improved transient performance. IEEE Transactions on Industrial Electronics, 2010, 57(7): 2536-2547