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机器人故障探测诊断与容错控制及实验研究
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摘要
机器人在当今世界得到不断广泛而深入的应用,与此同时机器人系统安全性指标成为了人们在应用过程中关注的重要方面。机器人故障的发生具有很强的随机性,并且随着使用时间的增加,发生故障的频率会呈指数上升,使得对故障的探测、诊断以至容错方面的研究意义明显。针对机器人系统安全性方面的研究虽然已经有了一些的研究成果,但仍存在较多的关键问题需要解决,同时就系统中编码器和执行机构相关复杂故障的探测与诊断过程,仍需要行之有效的方法。另外,在机器人系统中驱动故障的容错方面,智能性和适应性也需要提高。针对这些机器人系统中安全相关的实际问题,本文开展了一下几方面的研究工作:
     (1)在机器人系统的故障探测方面,针对难以探测的一般机器人系统位置环反馈装置编码器的丢码、漏码故障,利用系统中冗余的电机转速信号,提出了基于主元分析法(PCA)的编码器故障探测方法。通过数据仿真证明了该方法的有效性和方便性;
     另外,考虑到一般机器人系统中电机工作于速度控制模式,当机器人系统发生扭矩突变故障时,伺服系统不出现报警的问题,提出了将机器人非线性动力学模型作为非线性核函数的思想,利用主元分析法探测位置信号与扭矩信号的相关性,并根据异常的不相关性探测故障的发生。该方法的有效性在X-Y平台与外界的碰撞故障探测过程中得到验证;
     同时,针对机器人系统中可能发生的机器与机器、机器与人等外界环境间的碰撞故障,提出了基于小波函数故障探测方法,能够反映出不同碰撞的性质,在以X-Y平台为对象的实验过程中验证了方法的有效性;
     (2)在基于机器学习的故障诊断方面,针对机器人系统中位置/转角与扭矩间的非线性关系,通过向机器人故障诊断算法中引入非线性支持向量机,解决对驱动失灵故障、连接类故障以及与外界碰撞故障的多模式故障诊断问题,并提出基于此思想的机器人系统一般故障诊断框架,以实际过程验证了有效性;
     (3)在机器人驱动系统故障的容错控制方面,针对输出的饱和故障,为了在有限的驱动能力下,保证系统稳定,同时兼顾系统的快速性,文中给出了基于模糊规则的主动故障容错方法,并通过Lyapunov理论证明了容错控制系统的全局稳定性,仿真试验证明了方法的有效性。
     上述故障探测、诊断及容错方法都考虑到了应用过程中的实用性和有效性,并以通用X-Y运动平台为对象进行了实验验证,个别方法通过Matlab软件进行仿真验证,为更好地解决机器人系统运行中的安全性相关问题提供了几种新的思路和方法。
Robots are applied in the world abroad and deeply. In this background, the index of robotic safty has becoming the important aspect during human use them. Faults in robotic system have very high randomicity. With the machines become dated, the fault occuring frequency rise exponentially. The importance of fault detection, diagnosis, and tolerant control is evident. Although there are some meaningful research results in the aspect of the safty in robotic system, many pivotal problems need effective solving methods. Especially some complex problems ask for available techniques, for example, the faults in encoder and actuator. In another aspect, the fault tolerance on systemic actuators demands higher intellegence and adaptability. Focusing on these practical problems about the safty of robotic system, the research results can be summarized as follows:
     (1) On the aspect of fault detection in robotic system, firstly, as the very important feedback equipment in postion loop, encoder could lose codes or pause codes. Benifiting from the redundant signal of motor velocity in normal robotic system, a PCA-based mehod was developped. With numerical simulation, the validity and conveniency was tested.
     And considering normal robot runs under the velocity mode of driver equipment, when the applied torque turns into unconventionality and works in its nominal area, servo equipment would not give off alarm. A thought of treating the robotic nonlinear dynamic model as nonlinear kerel function was presented, where using PCA to detecting the relativity of the output position signal and torque monitoring signal. According to the noncorrelation of unconventionality, fault can be detected. The efficiency of this method was tested when an X-Y motion platform impacts a roadblock.
     At the same time, aiming at the contingent collision fault, between machines or robot and outer circumstance, the wavelet-based fault detection method is given, from which property of the collision can be reflected. An experiment was developped to testing the efficiency of this method, where an X-Y platform impacted two different obstacles under same motion velocity.
     (2) The fault diagnosis method using thought of machine learning, considering the nonlinear property of position/angle and applied torque in robotic system, nonlinear support vecter machine was introduced to diagnosing the faults in robotic system, and the commonly fault diagnosing framework was developed for this system. This method was validated in the X-Y platform where different faults were simulated practically.
     (3) In order to implement fault tolerant control when robot driver equipment falls into saturation state, a method was advanced using the thought of fuzzy rule adjustment. Under it, the close-loop control system is stable and satisfying the requirement of speedability. The globle stability was proved under the Lyapunov sable theory. The effectiveness of this thought was validated on a two degree of freedom manipulators.
     All of the above fault detecting, fault diagnosis and fault tolerant control methods were developped under the thought of practicability and effectiveness. They were mainly validated on a classical X-Y motion control platform with open configuration. A few methods validated using Matlab because it was hard to simulating these types of faults. As a conclusion, this work gives some new thoughts and methods for solving the safty-related problems in robotic system.
引文
1王东署.工业机器人标定技术研究.东北大学工学博士论文.2006,2
    2徐玉如,庞永杰,甘永等.智能水下机器人技术展望.智能系统学报,2006,1(1):9-16
    3尚建忠.空间探测机器人移动机构及系统研究.华中科技大学工学博士论文.2006,4
    4张学文.管道机器人三轴差动式驱动单元设计与可靠性研究.吉林大学工学博士论文.2008,6
    5 Rowley H A, Baluja S, Kanade T. Neural network-based face detection. IEEE Trans. Pattern Analysis and Machine Intelligence,1998,20(1):23-38
    6张雪元.基于人工心理的服务机器人交互平台相关技术研究.北京科技大学工学博士论文.2007,12
    7张建民.机电一体化系统设计(第二版).北京:北京理工大学出版社,1996
    8张世翔.关于工业机器人的事故分析及其对策.工业安全与环保,2002,28(3):26-29
    9 Bernhard Wilpert. Safe, secure and ethical e-society. Annual Reviews in Control, 2006, (30): 255-259
    10 LaPorte T. R. Large technical systems, institutional surprises, and challenges to political legitimacy. Technology in Society,1994,16(3):269-288
    11 Rolf Isermann. Model-based fault-detection and diagnosis-status and applications. Annual Reviews in Control,2005,29:71-85
    12 V. Verma, R. Simmons. Scalable robot fault detection and identification. Robotics and Autonomous Systems, 2006, 54:184-191
    13周东华,叶银忠.现代故障诊断与容错控制.北京:清华大学出版社,2000
    14 R. Isermann, P. Balle. Trends in the application of model-based fault detection and diagnosis of technical processes. Control Engineering Practice,1997,5(5):709-719
    15 J. Carlson,R. Murphy. Reliability analysis of mobile robots. Proc. IEEE Int. Conf. on Robotics and Automation, 2003,274-281
    16 O. Pettersson. Execution monitoring in robotics: A survey. Robotics and Autonomous Systems, 2005, 53:73-88
    17胡政.机器人安全性工程研究综述.中国机械工程,2004,15(4):370-375
    18 V. Verma, G. Gordon,R. Simmons, et al. Real-Time Fault Diagnosis. IEEE Robotics & Automation Magazine,2004,11(2):56-66
    19 C. Bererton,P. Khosla. An analysis of cooperative repair capabilities in a team of robots. Proc. IEEE Int. Conf. on Robotics and Automation,2002,1:476-482
    20 S. Thrun, J. Langford,V. Verma. Risk sensitive particle filters. Neural Info. Processing Syst.,2002,14:961-968
    21 V. Verma, S. Thrun, R. Simmons. Variable resolution particle filter. In International Joint Conference of Artificial Intelligence, 2003
    22 R. Dearden,D. Clancy. Particle filters for real-time fault detection in planetary rovers. Proceedings of the Thirteenth International Workshop on Principles of Diagnosis, 2002,1-6
    23 X. Koutsoukos, J. Kurien, F. Zhao, Monitoring and Diagnosis of Hybrid Systems Using Particle Filtering Methods, International Symposium on Mathematical Theory of Networks and Systems, 2002
    24 N. de Freitas. Rao-Blackwellised particle filtering for fault diagnosis. Proceedings of the IEEE Aerospace Conference,2002,4:1767-1772
    25 A. Doucet, N.Freitas, K.. Murphy, et al. Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks. Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence, 2000,176-183
    26 F. Hutter. R.Dearden. The Gaussian particle filter for diagnosis of non-linear systems.Proc. Fifth IFAC Symp. on Fault Detection, Supervision, and Safety of Technical Processes, 2003
    27段琢华.基于自适应粒子滤波器的移动机器人故障诊断理论与方法研究.中南大学工学博士论文. 2007,5
    28段琢华,蔡自兴,于金霞等.基于粒子滤波器的移动机器人惯导传感器故障诊断.中南大学学报(自然科学版),2005,36(4):642-647
    29 P. Harmon, R. Maus,W. Morrissey. Expert Systems: Tools and Applications, New York:John Wiley and Sons, INC, 1988
    30 E. Shortliffe. MYCIN: Computer-Based Medical Consultations, New York: Elsevier, 1976
    31 J.S. Brown, R.R. Burton, J. de Kleer. Pedagogical, natural language and knowledge engineering techniques in SOPHIE I, II, and III. Intelligent Tutoring Systems., London, England: Academic Press, 1982
    32 S. McIlraith. Explanatory diagnosis: conjecturing actions to explain observations. Proc. Sixth International Conference on Principles of Knowledge Representation and Reasoning, Trento, Italy, 1998, 167-177
    33 D. Kleer, Johan, B.C. Williams. Diagnosis with Behavioral Modes. Proceedings of the International Joint Conference on Artificial Intelligence, Detroit, MI, 1989,1324-1330
    34 R. Kalman.A new approach to linear filtering and prediction problems. J. Basic Eng.,1960,82:34-45
    35 C. Hajiyev, F. Caliskan. Sensor/actuator fault diagnosis based on statistical analysis of innovation sequence and Robust Kalman Filtering.Aerosp. Sci. Technol., 2000, 4:415-422
    36 Ch.M. Gadzhiev(Hajiyev).Dynamic systems diagnosis based on Kalman filter updating sequences, Automat. Rem. Contr. 1992,(1) :147-150
    37 Ch.M.Gadzhiev (Hajiyev). Check of the generalized variance of the Kalman filter updating sequence in dynamic diagnosis. Automat. Rem. Contr.1994, 55(8):1165-1169
    38 Hajiyev Ch.M., Caliskan F., Fault detection in flight control systems via innovation sequence of Kalman filter.Progress in System and Robot Analysis and Control Design, Lecture Notes in Control and Information Sciences, London:Springer,1999, 63-74
    39胡士强,敬忠良.粒子滤波算法综述.控制与决策,2005,20(4):361-365
    40 P.M. Frank, B. Koppen-Seliger. New developments using AI in fault diagnosis, Eng. Appl. Artif. Intell.1997,10(1):3-14
    41 G.C. Luh, W.C. Cheng. Immune model-based fault diagnosis. Mathematics and Computers in Simulation, 2005,67:515-539
    42 L.N. de Castro, T. Jonathan. Artificial immune systems: a newcomputational intelligence approach. Berlin,Heidelberg, Springer-Verlag, 2002
    43 S.Y. Chang, C.R. Lin, C.T. Chang. A fuzzy diagnosis approach using dynamic fault trees.Chemical Engineering Science,2002, 57:2971-2985
    44周海英,董素荣.基于模糊推理图的故障诊断.计算机应用,2008,28(6):1582-1584
    45余愚,涂宏斌,周新建.基于模糊C均值技术的轴承故障诊断.机械设计与制造,2008, (3): 112-114
    46李锡江,刘荣,张厚祥等.基于模糊故障树法的清洗机器人安全性研究.北京航空航天大学学报,2004,30(4):344-348
    47李瑞莹,康锐.基于神经网络的故障率预测方法.航空学报,2008,29(2):357-362
    48王光研,许宝杰. RBF神经网络在旋转机械故障诊断中的应用.机械设计与制造,2008,(9):57-58
    49 C.T. Kowalski, O.K. Teresa. Neural networks application for induction motor faults diagnosis. Mathematics and Computers in Simulation,2003,63:435-448
    50 S. V. Nalinaksh, D. Satishkumar. Artificial neural network design for fault identification in a rotor-bearing system. Mechanism and Machine Theory,2001,36:157-175
    51 J.Roya, M. K. Gerald. A fuzzy neural network approach to machine condition monitoring. Computers & Industrial Engineering, 2003,45:323-330
    52 M.Ayoubi, R. Isermann. Neuro-fuzzy systems for diagnosis. Fuzzy Sets and Systems, 1997, 89:287-307
    53张崇刚,郭旭辉,黄昭婷等.模糊理论在故障诊断专家系统中的应用.中国测试技术,2008,34(5):122-125
    54张振飞,夏利民.基于神经网络的滚动轴承故障诊断智能方法.信息技术,2008,(8):53-55
    55陈果.粗糙集-遗传算法-神经网络集成分类器及其在转子故障诊断中的应用研究.中国机械工程,2008,19(1):85-90
    56 B. Samanta. Artificial neural networks and genetic algorithms for gear fault detection. Mechanical Systems and Signal Processing,2004,18:1273-1282
    57 V. K. Stefanidis,K. G. Margaritis .Algorithm Based Fault Tolerance: Review and experimental study. International Conference of Numerical Analysis and Applied Mathematics,Chalkis, Greece, 2004,1-8
    58 A. Noore.Real time fault tolerant control of robot manipulators.Mathematical and Computer modelling,2003,38,13-22
    59 Y. Ting, S. Tosunoglu, R. Freeman. Actuator Saturation Avoidance For Fault-Tolerant Robots. Proceedings of the 32nd Conference on Decision and Control, San Antonio,Taxas, 1993, 2125-2130
    60 Y. Ting, S. Tosunoglu, B. Fernndez. Control algorithms for fault-tolerant robots. Proceedings of the 1994 IEEE International Conference on Robotics and Automation, San Diego, California,1994,2:910-915
    61 A.A. Maciejewski.Fault tolerant properties of kinematically redundant manipulator. Proceedings of the IEEE International Conference on Robotics and Automation,13-18th May,1990,West Lafayette,USA,1990:638-642
    62 R.G.Roberts, A.A.Maciejewski. A local measure of fault tolerance for kinematieally redundant manipulators. IEEE Transactions on Robotics and Automation,1996,12(4):543-552
    63唐世明,张启先.冗余度机器人容错控制研究.机械工程学报,2000,36(7):34-38
    64陈伟海,武桢,丁希伦等.冗余度机器人动力学容错控制技术研究.北京航空航天大学学报,2000,26(6):726-730
    65李健,李剑锋,武桢等.冗余度机器人多关节故障的运动学容错性及其优化.机械工程学报,2002,38(7):111-115
    66赵京,魏珊珊.基于容错性能的冗余度机器人结构综合.机械工程学报,2007,43(10):82-87.
    67赵京,姚艳彬,冯登殿等.冗余度机器人容错操作中关节相对速度突变影响因素.北京工业大学学报,2007,33(12):1239-1245
    68 W. Chen, J. Jiang. Fault-tolerant control against stuck actuator faults. IEE Proceedings of Control theory and applications,2005,152(2):138-146
    69 Qingbo He, Fanrang Kong, Ruqiang Yan.Subspace-based gearbox condition monitoring by kernel principal component analysis.Mechanical Systems and Signal Processing,2007,21: 1755-1772
    70 M. Tamura, S. Tsujita. A study on the number of principal components and sensitivity of fault detection using PCA. Computers and Chemical Engineering,2007,31:1035-1046
    71 A. Widodo, B.S. Yang. Application of nonlinear feature extraction and support vector machines for fault diagnosis of induction motors. Expert Systems with Applications, 2007,33:241-250
    72 J. Lin, J. Jiang.Fault Detection and Analysis of Control Software for a Mobile Robot.Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications,2006, 1:862-866
    73 T. K.Czeslaw, O.K.Teresa.Neural networks application for induction motor faults diagnosis. Mathematics and Computers in Simulation,2003,63:435-448
    74 J.Roya, M. K.Gerald. A fuzzy neural network approach to machine condition monitoring. Computers & Industrial Engineering,2003,45:323-330
    75 Xiaoli Li, R. Du, Berend Denkena, et al. Tool Breakage Monitoring Using Motor Current Signals for Machine Tools With Linear Motors. IEEE Trans. Indus. Electro., 2005, 52(5): 1403-1408
    76 R. Yan, X.G. Robert.An efficient approach to machine health diagnosis based on harmonic wavelet packet transform. Robotics and Computer-Integrated Manufacturing,2005,21:291-301
    77郑大钟.线性系统理论.北京:清华大学出版社,1990:121-136
    78代颖.不确定机器人鲁棒自适应控制方法研究.西安交通大学博士论文.1997
    79 P.LaSalle. The Stability of Dynamical Systems. SIAM, Philadelphia,1976
    80申铁龙.机器人鲁棒控制基础.北京:清华大学出版社,2000
    81蔡自兴.机器人学.北京:清华大学出版社,2000
    82 Panasonic AC Servo Motor Driver MINAS A-series Operating Manual.Industrial and Appliance Motor Division, Motor Co., Matsushita Electric Industrial Co.,Ltd.,2004
    83 Venturcom, Inc. RTX? 5.5 Release Notes,2003
    84 Quanser Inc. WinCon 4.1:Hard Real-time Performance at your Fingertips User's Guide(Version 1.2),July 22nd, 2003
    85张亮,王继阳.Matlab与C/C++混合编程.北京:人民邮电出版社,2008
    86 Honghai Liu, George M. Coghill. A model-based approach to robot fault diagnosis. Knowledge-Based Systems, 2005, 18:225-233
    87 A.De Luca, R.Mattone. An identification scheme for robot actuator faults. IEEE/RSJ International Conference on Intelligent Robots and Systems, 2005, 1127-1131
    88 V.F. Filaretov, M.K. Vukobratovic, A.N. Zhirabok. Parity relation approach to fault diagnosis in manipulation robots. Mechatronics, 2003, 13:141-152
    89 V.F. Filaretov, M.K. Vukobratovic, A.N. Zhirabok. Observer-based fault diagnosis in manipulation robots. Mechatronics, 1999,9:929-939
    90 M. C. Pan, H.Van Brussel, P. Sas. Intelligent joint fault diagnosis of industrial robots. Mechanical Systems and Signal Processing,1998,12(4):571-588
    91 A.T. Vemuri, M.M. Polycarpou.Neural-network-based robust fault diagnosis in robotic systems. IEEE Trans. on Neural Networks, 1997,8(6):1410-1420
    92 A.T. Vemuri, M.M. Polycarpou,S. A. Diakourtis. Neural network based fault detection in robotic manipulators. IEEE Trans. on Robotics and Auto,1998,14(2):342-348
    93陈卫东,肖金壮,蔡建羡.一类特殊应用领域机器人控制策略的研究.制造业自动化, 2002, 24(12): 57-59
    94段磊强,周军,张铎等.用虚拟连杆构造冗余残差对多关节机械臂进行故障诊断.机器人, 2004,26(2):176-181
    95 Jonathon Shlens.A Tutorial on Principal Component Analysis. www.snl.salk.edu/~shlens/ pub/ notes/pca.pdf
    96 K. Pearson. On lines and planes of closest fit to systems of points in space. Philos. Mag. 1901,2:559-572
    97 B.M.Wise, N.B.Gallagher. The process chemometrics approach to process monitoring and fault detection. J. of Process Control, 1996,6(6):329-348
    98段琢华,蔡自兴,于金霞.未知环境中移动机器人故障诊断与容错控制技术综述.机器人, 2005,27(4):373-379
    99 Yokogawa Electric Corporation. DYNASERV Technical Information: Direct Drive Motor , Intelligent Drive . 1st Edition: 2004.04.01
    100 F. Reyes, A. Rosado. Polynomial family of PD-type controllers for robot manipulators. Control Engineering Practice, 2005,(13): 441-450
    101 P.F. Baldi, K. Hornik. Learning in linear neural networks: a survey. IEEE Trans. Neural Networks, 1995, 6(4): 837-857
    102 Ollero A, Boverie S, Goodall R, et al.Echatronics, robotics and components for automation and control-IFAC milestone report. Annual Reviews in Control, 2006,30:41-54
    103 Arnaz M, Robert X. PCA-based beature selection scheme for machine defect classification. IEEE Transactions on Instrumentation and Measurement, 2004, 53(6): 1517-1525
    104 Dragan K,Bram d J,Maarten S,et al. Modeling and Identification for High-Performance Robot Control: An RRR-Robotic Arm Case Study. IEEE Transaction on Control Systems Technology, 2004,12(6):904-919
    105 A. De Luca, R. Mattone. An adapt-and-detect actuator FDI scheme for robot manipulators. Proceedings of the 2004 IEEE International Conference on Robotics & Automation New Orleans, 2004, pp. 4975-4980
    106 Lames F. Kaiser. On a simple algorithm to calculate the‘energy’of a signal. International Conference on Acoustics, Speech, and Signal Processing, 1990, 1:381- 384
    107 Y. Meyer, S. Roques. Progress in wavelet analysis and applications, Frontières Ed.,1993
    108 I. Daubechies. Ten lectures on wavelets. CBMS, SIAM, 1994, 61:194-202
    109 W.Hardle, G. Kerkyacharian, D. Picard, et al. Wavelets approximation and statistical applications. Lecture Notes in Statistics, Springer Verlag, 1988
    110 H.K.Ahsan,T.H. Lai, K.B.Rezaul,et al. Wavelet-based feature extraction for support vector machines for screening balance impairments in the elderly. IEEE Trans. on Neural Sys. and Rehab. Eng.,2007,15(4):587-597
    111 Alexander Gammerman. Machine leaming: progress and prospects.Royal Holloway. University of London. Egham,UK,1996
    112 J.Nilsson. Introduction to machine learning.Stanford University Press,1996
    113罗瑜.支持向量机在机器学习中的应用研究.西南交通大学工学博士论文. 2007,10
    114 C. Cortes, V.M. Vapnik. Support vector networks. Machine Learning, 1995, 20: 273-297
    115 Jin Chen, Cheng Wang, Runsheng Wang. Combining support vector machines with a pairwise decision tree. IEEE Geoscience and Remote Sensing Letters,2008,5(3):409-413
    116 N. Takahashi,T. Nishi.Global convergence of decomposition learning methods for support vector machines. IEEE Trans. on Neural Networks, 2006,17(6):1362-1369
    117 Yumao Lu. Kernel optimization and distributed leatning algorithma for support vector machines.Doctor of philosophy dissertation, University of California, Los Angeles,2005
    118 Y.J. Lee,S.Y. Huang. Reduced support vector machines:a statistical theory. IEEE Trans. on Neural Networks, 2007,18(1):1-13
    119 S.M. Ross. Introduction to Probability and Statistics for Engineers and Scientists, Academic Press, San Diego, CA, USA,2000
    120 S. Haykin. Neural Networks: A Comprehensive Foundation. Prentice-Hall, Upper Saddle River, NJ, USA, 1999
    121 G.A. Kaminka, D.V. Pynadath, M. Tambe. Monitoring teams by overhearing: a multi-agent plan-recognition approach. J. Artif. Intell. Res.,2002,17:83-135
    122 M. de la Sen, J.J. Minambres, A.J. Garrido, et al. Basic theoretical results for expert systems: application to the supervision of adaptation transients in planar robots. Artif. Intell.,2004,152(2):173-211
    123 H. Ye, P. Zhang, S.X. Ding, et al. A time-frequency domain fault detection approach based on parity relation and wavelet transform. Proceedings of the IEEE International Conference on Decision and Control, Sydney, Australia, 2000,4156-4161
    124 O. Pettersson, L. Karlsson, A. Saffiotti. Model-free execution monitoring in behavior-based mobile robotics, in: Proceedings of the International Conference on Advanced Robotics (ICAR), Coimbra, Portugal, 2003, 864-869
    125 N. Ranganathan, M.I. Patel, R. Sathyamurthy. An intelligent system for failure detection and control in an autonomous underwater vehicle. IEEE Trans. Syst. Man Cybern. 2001, 31(6): 762-767
    126 G. Antonelli,F. Caccavale,C. Sansone,et al. Fault diagnosis for AUVs using support vector machines.Proceedings of the 2004 IEEE International Conference on Robotlcs & Automation, New Orleans, LA,2004,4486-4491
    127 A.Shigeo. Support vector machines for pattern classification. Springer-Verlag London Limited, 2005
    128 Nello Cristianini, John Shawe Tayoar.李国正译.支持向量机导论.北京:电子工业出版社,2004
    129邓乃扬,田英杰.数据挖掘中的新方法:支持向量机.北京:科学出版社,2004
    130陈兴辉.基于小波与支持向量机的滚动轴承故障诊断.太原理工大学工学硕士学位论文. 2006.5
    131毛志阳,陆爽.基于K-L变换和支持向量机的滚动轴承故障诊断.煤矿机械,2006,27(6): 1084-1086
    132 S. Montambault, C. M. Gosselin. Analysis of Underactuated Mechanical Grippers. ASME Journal of Mechanical Design, 2001, 123(3):367-374
    133何广平,陆震,王凤翔.欠驱动冗余度机器人运动优化控制.宇航学报, 2002, 23(5):16-20
    134 http://www.csie.ntu.edu.tw/%7Ecjlin/libsvm+zip
    135雷亚国,何正嘉,訾艳阳.基于混合智能新模型的故障诊断.机械工程学报,2008,44(7): 112-117
    136张龙,熊国良,柳和生等.基于时变自回归模型与支持向量机的旋转机械故障诊断方法.中国电机工程学报, 2007,27(9):99-103
    137方瑞明,郑力新,马宏忠等.基于MCSA和SVM的异步电机转子故障诊断.仪器仪表学报, 2007,28(2):252-257
    138 C. W. Hsu, C. C. Chang, C. J. Lin. A practical guide to support vector classification. http:// www.csie.ntu.edu.tw/~cjlin
    139李晓宇,张新峰,沈兰荪.支持向量机(SVM)的研究进展.测控技术,2006,25(5):7-12
    140 P.Xu, A.K.Chan. Suppor vector machines for multi-class signal classification with unbalanced samples. Proceedings of the Inernation Joint Conference on Neural Networks,2003,1116-1119
    141 S. Arimoto, F. Miyazaki.Stability and robustness of PD feedback control with gravity compensation for robot manipulator. In F. W. Paul, D. Youcef-Toumi (Eds.), Robotics: Theory and Practice, 1986,(3):67-72
    142 S. Purwar, I.N. Kar, A.N. Jha.Adaptive control of robot manipulators using fuzzy logic systems under actuator constrains.Fuzzy Sets and Systems, 2005, 152:651-664
    143 V. Santibanez, R. Kelly, and F. Reyes. A new set-point controller with bounded torque for robot manipulator. IEEE Trans. Industrial Electronics, 1998, 45(1):126-133
    144 L.A.Zadeh.Fuzzy sets. Information and Control, 1965,8:338-353
    145 L.A.Zadeh.Fuzzy logic. Computer, 1988,1(4):83-93
    146陶永华.新型PID控制及其应用.北京:机械工业出版社,2002
    147梁坚.一种新型在线实时自整定PID控制器.自动化与仪器仪表,1996,(3):32-35
    148 M.W. Spong, and M. Vidyasagar. Robotic dynamics and control, Wiley, New York, 1989
    149 C.C. Hang, K.J. Astr?m, and W.K. Ho. Refinements of the Ziegler-Nichols tuning formula. Proc. IEE, Pt.D.,1991,138:111-118
    150 J.G. Ziegler, and N.B. Nichols.Optimum settings for automatic controllers.Trans. ASME,1942, 64: 759-768
    151 G.J. Liu, A. Andrew, etc. Comparative study of robust saturation-based control of robot manipulators: Analysis and experiments. The International Journal of Robotics Research, 1996, 15(5):473-491

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