基于不确定机器人几类智能控制策略的研究
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摘要
机器人的控制问题无论在理论界还是工程界多年来一直倍受人们的关注。当机器人系统模型是精确知道的时候，反馈线性化技术可以很好的解决其控制问题，然而现实的操作过程中机器人动力学模型的各个参数可能发生变化，同时还受到环境干扰和负载变化等许多不确定因素的影响，因此有必要对现有的控制方法加以改进。
本论文以具有完整动力学模型的机器人系统，即不确定机器人系统为研究对象，在现有文献的基础上，重点探讨了基于智能算法的各种鲁棒控制策略。
本论文第1章介绍了机器人的发展概况和机器人控制理论概况；第2章给出控制器设计所需的数学知识和机器人动力学模型；第3章提出一种模糊自适应控制与滑模监督项相结合的控制方法，其中模糊控制以其简单的规则集和隶属度函数有效的补偿了系统的不确定性，然后利用一个低抖振的滑模监督项消除了模糊控制系统的逼近误差；第4章提出了基于Backstepping方法的不确定机器人模糊神经网络控制，这里模糊神经网络学习了系统理想的反馈线性化控制律，并采用一个鲁棒项补偿了模糊神经网络的学习误差，整个控制器的设计过程都是基于Backstepping设计方法，有效的保证了系统的全局稳定性；第5章针对工业机器人仅有关节位置测量的情况，提出了基于RBF神经网络的滑模观测器来观测关节的速度信号，将滑模方法融入观测器的设计提高了其抗干扰能力，这里RBF神经网络补偿了机器人系统的各种不确定性，避免了回归矩阵的计算和对惯性矩阵先验知识的要求，并充分考虑控制器与观测器的相互影响，保证了重构的速度信号能代替实际的速度信号用到控制策略的反馈回路中，仿真结果证明了所提方法的正确性。
The control problems of robotic manipulators have received great attention in theoretical and engineering for many years. When the robot model is exactly known, the technique of feedback linearization in nonlinear systems can solve the problem very well. However, the parameters of dynamic model of robotic manipulators may also be subject to change when the manipulator goes about its task. Meanwhile, the system can be influenced by uncertainties such as external disturbance and payload change. Therefore it is necessary to improve these existing control methods
In this dissertation, the system of robotic manipulators with entire dynamic model, namely, the robotic system with uncertainties is regarded as controlled plant. The various robust control schemes based intelligent algorithms are developed using the references available.
The first chapter of dissertation gives a brief description about the developing situation and control theory of robot. The second chapter introduces necessary mathematic knowledge of controller design and the dynamic model of robot. The third chapter put forward a fuzzy adaptive control method combining sliding supervisory control term. The fuzzy controller associated with simple rule base and membership function effectively compensate the system uncertainties. Afterward utilizing a sliding supervisory control term which produces a low chattering eliminate the approaching error of fuzzy control system. The fourth chapter bring forward the fuzzy neural network control of uncertain robot using backstepping method. Here fuzzy neural network is used to learn the ideal feedback linearization control law, and through adopting a robust term compensate learning error via fuzzy neural network. The overall designing process of controller is based on backstepping method and the whole stability of system is effectively guaranteed.
The fifth chapter introduces a sliding observer using RBF neural network to observe the speed signal aiming at only having position measurement in industrial robot. The observer combining sliding method improve the ability of resisting disturbance. RBF neural network is used to compensate various uncertainties of robot. In addition, the method avoid the computation of regressor matrix and the demand of the priori knowledge of inertia matrix. Taking into account the dynamic interactions between the observer and controller dynamics, it can be ensured that the fictitious speed signal can substitute the real speed signal and is employed in the feedback loop. Simulation results prove the effectiveness of proposed methods.
本论文以具有完整动力学模型的机器人系统，即不确定机器人系统为研究对象，在现有文献的基础上，重点探讨了基于智能算法的各种鲁棒控制策略。
本论文第1章介绍了机器人的发展概况和机器人控制理论概况；第2章给出控制器设计所需的数学知识和机器人动力学模型；第3章提出一种模糊自适应控制与滑模监督项相结合的控制方法，其中模糊控制以其简单的规则集和隶属度函数有效的补偿了系统的不确定性，然后利用一个低抖振的滑模监督项消除了模糊控制系统的逼近误差；第4章提出了基于Backstepping方法的不确定机器人模糊神经网络控制，这里模糊神经网络学习了系统理想的反馈线性化控制律，并采用一个鲁棒项补偿了模糊神经网络的学习误差，整个控制器的设计过程都是基于Backstepping设计方法，有效的保证了系统的全局稳定性；第5章针对工业机器人仅有关节位置测量的情况，提出了基于RBF神经网络的滑模观测器来观测关节的速度信号，将滑模方法融入观测器的设计提高了其抗干扰能力，这里RBF神经网络补偿了机器人系统的各种不确定性，避免了回归矩阵的计算和对惯性矩阵先验知识的要求，并充分考虑控制器与观测器的相互影响，保证了重构的速度信号能代替实际的速度信号用到控制策略的反馈回路中，仿真结果证明了所提方法的正确性。
The control problems of robotic manipulators have received great attention in theoretical and engineering for many years. When the robot model is exactly known, the technique of feedback linearization in nonlinear systems can solve the problem very well. However, the parameters of dynamic model of robotic manipulators may also be subject to change when the manipulator goes about its task. Meanwhile, the system can be influenced by uncertainties such as external disturbance and payload change. Therefore it is necessary to improve these existing control methods
In this dissertation, the system of robotic manipulators with entire dynamic model, namely, the robotic system with uncertainties is regarded as controlled plant. The various robust control schemes based intelligent algorithms are developed using the references available.
The first chapter of dissertation gives a brief description about the developing situation and control theory of robot. The second chapter introduces necessary mathematic knowledge of controller design and the dynamic model of robot. The third chapter put forward a fuzzy adaptive control method combining sliding supervisory control term. The fuzzy controller associated with simple rule base and membership function effectively compensate the system uncertainties. Afterward utilizing a sliding supervisory control term which produces a low chattering eliminate the approaching error of fuzzy control system. The fourth chapter bring forward the fuzzy neural network control of uncertain robot using backstepping method. Here fuzzy neural network is used to learn the ideal feedback linearization control law, and through adopting a robust term compensate learning error via fuzzy neural network. The overall designing process of controller is based on backstepping method and the whole stability of system is effectively guaranteed.
The fifth chapter introduces a sliding observer using RBF neural network to observe the speed signal aiming at only having position measurement in industrial robot. The observer combining sliding method improve the ability of resisting disturbance. RBF neural network is used to compensate various uncertainties of robot. In addition, the method avoid the computation of regressor matrix and the demand of the priori knowledge of inertia matrix. Taking into account the dynamic interactions between the observer and controller dynamics, it can be ensured that the fictitious speed signal can substitute the real speed signal and is employed in the feedback loop. Simulation results prove the effectiveness of proposed methods.
引文
1 蔡自兴.机器人学.北京:清华大学出版社, 2000:2-3
2 白井良明.机器人工程.王棣棠.北京:科学出版社, 2001:1-6
3 王灏, 毛宗源.机器人的智能控制方法.北京:国防工业出版社, 2002:1-2
4 熊有伦.机器人学.北京:机械工业出版社, 1993:1-9
5 Angeles J. On The Numerical Solution for The Inverse Kinematics Problem. Int. J. of Robotics Research, 1985,4(2):21-37
6 罗志增, 蒋静坪.机器人感觉与多信息融合.北京:机械工业出版社, 2002:1-2
7 世界机器人最新统计数据.机器人技术与应用, 2001,(1):6-10
8 黄真, 孔令富, 方跃法.并联机器人机构学理论及控制.北京：机械工业出版社， 1997:3-4
9 K. W. Grace and J. E. Colgate. A 6-DOF Micromanipulator for Ophthalmic Surgery. IEEE Conf. on Rob. & Auto., 1993:630-635
10 L G Wang, C C A. Chen. Combined Optimization Method for Solving The Inverse Kinematics Problem of Mechanical Manipulators. IEEE Trans. on Robotic and Auto., 1991,7(4):213-220
11 K. Kazerounian. On The Numerical Inverse Kinematics of Robotic Manipulators. ASME Mechanisms Transmissions Autom at Design, 1987,109(5):8-13
12 W. T. Miller, F. H. Glanz and L. G. Kraft. Application of A General Learning Algorithm to The Control of Robotic Manipulator. Int. J. of Robotics Research, 1987,6(2):84-98
13 Z. H.Man, I. M. Palaniswam. Robust Tracking Control for Rigid Robotic Manipulators. IEEE Trans on Automatic Control, 1994,39(1):456-463
14 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
15 M. Spong, M. Vidyasagar. Robust Linear Compensator Design for Nonlinear Robotic Control. IEEE Trans. on Robotic and Auto., 1987,3(8):345-351
16 H. Asda. Representation and Learning of Nonlinear Compliance Using Neural Network. IEEE Trans. on Robotics and Automation, 1993(6):863-867
17 K. K. D. Young. Controller Design for A Manipulator Using Theory of Variable Structure Systems. IEEE Trans. on system, man and Cybenetic, 1978,8(2):110-128
18 Y. Stepanenko, Y. Cao and Y. S. Chun. Variable Structure Control of Robotic Manipulator with PID Sliding Surface. Int. J. of Robust and Nonlinear Control, 1998,12(8):79-90
19 K. S. Yeung, Y. P. Chen. A New Controller Design for A Manipulator Using Theory of Variable Structure System. IEEE Trans. on Auto. Contrl., 1988,35(2):995-1003
20 Y. F. Chen, T. Mita and S. Wakui. A New and Simple Algorithm for Sliding Mode Trajectory Control of Robot. IEEE Trans. on Automatic Control, 1990,35(7):828-829
21 Man et al. A Robust MIMO Terminal Sliding Mode Control Scheme for Rigid Robotic Manipulators. IEEE Trans. on Automatic Control, 1994,39(8):2462-2469
22 J. J. E. Slotine. The Robust Control of Robotic Manipulators. Int. J. of Robot and Research, 1985,12(4):49-54
23 J. Doyle, K. Glover. State-Space Solutions to Standard and Control Problem. IEEE Trans. on Automatic Control, 1989,34(8):831-847
24 A. Isidori, W. Kang. Control Via Measurement Feedback for General Nonlinear System. IEEE Trans on Automatic Control, 1995,40(3):831-847
25 A. vanderschaft. Gain Analysis of Nonlinear Systems and Nonlinear State Feedback Control. IEEE Trans. on Automatic Control, 1992,37(6):770-784
26 申铁龙. 控制理论与应用.北京：清华大学出版社,1996
27 W. L. Stout, M. E. Sawan. Application of Theory to Robot Manipulators Control. Proceeding of the Conference on Control Application, 1992:148-183
28 Borsen Chen. A Nonlinear Control Design In Robotic Systems Under Parameter Perturbation and External Disturbance. Int. J. of Robot Control, 1994,59(2):439-461
29 B. Danilo. High Precision Position Control by Catisian Trajectory Feedback and Connectinist Inverse Dynamic Feedback.IJCNN,1988:213-220
30 M. Leahy. Neural Networks Payload Estimation for Adaptive Robot Control. IEEE Trans. on Neural Network, 1991,13(2):45-53
31 W. Miler. Realtime Dynamic Control of An Industrial Manipulator Using A Neural Network Based Learning Controller. IEEE Trans. on Robot and Automation, 1990,5(1):1-9
32 M. Jodan. Interminate Motor Skill Learning Problems, Attention and Performance. MIT Press, 1989
33 Kawoto. Trajectory Formation of Arm Movement by Cascade Neural Network Model Based on Minimum Torque Change Criterion, Biol. Cybern., 1990:483-489
34 A. Guez. Neural Morphic Architecture Adaptive Robot Control: A Preliminary Analysis. Proceeding IEEE Int. Conf. on Neural Network, 1987,45(3):1056-1062
35 H. Asda. Representation and Learning of Nonlinear Compliance Using Neural Network. IEEE Trans. on Robotics and Automation, 1993,12(6):863-867
36 A. A. M. Karakasoglu. A Recurrent Neural Network Based Adaptive Variable Structure Model-Following Control of Robot. Automatica, 1995,31(10):1495-1507
37 K. K. Watanabe. A Nonlinear Robust Control Using Fuzzy Reasoning and Its Application to A Robot Manipulator. Journal of Intelligent and Robotic Systems, 1997,20(5):275-294
38 金耀初.机器人神经模糊控制.机器人.1995,(3):171-176
39 郑大钟.线性系统理论.北京:清华大学出版社, 1990:121-136
40 J. J. Slotine. Applied Nonlinear Control. Englewood Cliffs,NJ: Prentice Hall, 1991
41 P. LaSalle. The Stability of Dynamical Systems. SIAM, Philadelphia, 1976
42 申铁龙.机器人鲁棒控制基础.北京:清华大学出版社, 2000
43 王洪斌, 吴健珍, 杨香兰. Simulink(S-Function)在复杂控制系统中的应用. 系统仿真学报, 2001(13):131-132
44 G. C. Hwang and S. C. Lin. A Stability Approach to Fuzzy Control Design for Nonlinear Systems. Fuzzy Sets Syst., 1992,48(1):279-287
45 J. C. Wu and T. S. Liu. A Sliding Mode Approach to Fuzzy Control Design. IEEE Trans. Contr. Syst. Tech., 1996,4(3):141-148
46 Y. Ohtani and T. Yoshimura. Fuzzy Control of A Manipulator Using The Concept of Sliding Mode. Int. J. Syst. Sci., 1996,27(3):179-186
47 F. Y. Hsu and L. C. Fu. Adaptive Robust Fuzzy Control for Robot Manipulators. in Proc. Int. Conf. Robotics Automat., 1994,1(2):649-654
48 Slotine and W. Li. Composite Adaptive Control of Robot Manipulators. Automatica, 1989,25(2):509-519
49 H. A. Malki. New Design and Stability Analysis of Fuzzy Proportion-Derivative Control Systems. IEEE Trans. Fuzzy Syst., 1994,2(3):245-254
50 D. Misir. Design and Analysis of A Fuzzy Proportional-Integral-Derivative Controller. Fuzzy Sets Syst., 1995,79(6):97-102
51 L. X. Wang. Adaptive Fuzzy Systems and Control: Design and Stability Analysis. Englewood Cliffs,NJ:Prentice-Hall,1994
52 E. Kemalettin, K. Okyay and S. Asif. Fuzzy Adaptive Sliding Mode Control of A Direct Drive Robot. Robotics and Autonomous Systems, 1996,19:215-227
53 B. Yao and M. Tomizuka. Comparative Experiments of Robust and Adaptive Control with New Robust Adaptive Controllers for Robot Manipulators. Proc. of the IEEE Conference on Decision and Control, 1994:1290-1295
Y. G. Niu, C. W. Yang. An Adaptive Neural Robust Controller for Trajectory Tracking of Robot. Control Theory and Applications,
54 2000,17(6):924-928
55 C. T. Lin. Neural Fuzzy Systems New Jersey:Prentice-Hall,1996
56 Y. Q. Zhang, A. Kandel. Compensatory Neurofuzzy Systems with Fast Learning Algorithms. IEEE Trans. Neural Networks, 1998,9(2):83-105
57 F. J. Lin, W. J. Hwang and R. J. Wai. A Supervisory Fuzzy Neural Network Control System for Tracking Periodic Inputs. IEEE Trans. Fuzzy Systems, 1999,17(1):41-52
58 Y. G. Leu, T. T. Lee. Observer-Based Adaptive Fuzzy-Neural Control for Unknown Nonlinear Dynamical Systems. IEEE Trans. System Man and Cybernetics, 1999,29(5):583-591
59 Y. G. Leu, W. Y. Wang. Robust Adaptive Fuzzy-Neural Controllers for Uncertain Nonlinear Systems. IEEE Trans. Robot and Auto., 1999,15(5):805-817
60 H. J. Shieh and K. K. Shyu. Nonlinear Sliding-Mode Torque Control with Adaptive Backstepping Approach for Induction Motor Drive. IEEE Trans. Ind. Electron, 1999,46(2):380-389
61 F. J. Lin and C. C. Lee. Adaptive Backstepping Control for Linear Induction Motor Drive to Track Periodic Reference. IEE Proceeding Electronic Power App. 2000,147(6):449-458
62 K. L. Kristic, M. Kanellakopoulos. Nonlinear and Adaptive Control Design. New Jersey:Prentice Hall, 1995
63 S. Nicosia, P. Tomei. Robot Control by Using Only Joint Position Measurements. IEEE Trans. Automatic Control, 1990,35(9):1058-1061
64 C. Canudas, N. F. Wit. Trajectory Tracking in Robot Manipulators via Nonlinear Estimated State Feedback. IEEE Trans. Robotics and Automation, 1992,8(1):138-144
65 J. J. Slotine and E. A. Misawa. Sliding Observers for Nonlinear Systems. ASME-J. of Dynamics Syst. Meas. Control, 1987,109(2):245-252
66 C. Canudas, N. Fixot. Robot Control via Robust Estimated State Feedback. IEEE Trans. Automatic Control, 1991,36(12):1497-1501
C. Canudas, N. Fixot. Adaptive Control of Robot Manipulators via Velocity Estimated Feedback. IEEE Trans. Automatic Control,
67 1992,3(6):1234-1237
68 W. Zhu, H. Chen and Z. Zhong. A Variable Structure Robot Control Algorithm with An Observer. IEEE Trans. Robotics and Automation, 1992,8(4):486-492
69 Y. H. Kim, F. L. Lewis. Neural Network Output Feedback Control of Robot Manipulators. IEEE Trans. Robotics and Automation, 1999,15(2):301-309
70 K. Hornik, M. Stinchcombe and H. White. Universal Approximation of An Unknown Mapping and Its Derivatives Using Multiplayer Feedforword Neural Networks, 1990,20(3):551-560
71 C. Canudas and J. J. Slotine. Sliding Observers for Robot Manipulators. Automatica, 1991,27(5):859-864
2 白井良明.机器人工程.王棣棠.北京:科学出版社, 2001:1-6
3 王灏, 毛宗源.机器人的智能控制方法.北京:国防工业出版社, 2002:1-2
4 熊有伦.机器人学.北京:机械工业出版社, 1993:1-9
5 Angeles J. On The Numerical Solution for The Inverse Kinematics Problem. Int. J. of Robotics Research, 1985,4(2):21-37
6 罗志增, 蒋静坪.机器人感觉与多信息融合.北京:机械工业出版社, 2002:1-2
7 世界机器人最新统计数据.机器人技术与应用, 2001,(1):6-10
8 黄真, 孔令富, 方跃法.并联机器人机构学理论及控制.北京：机械工业出版社， 1997:3-4
9 K. W. Grace and J. E. Colgate. A 6-DOF Micromanipulator for Ophthalmic Surgery. IEEE Conf. on Rob. & Auto., 1993:630-635
10 L G Wang, C C A. Chen. Combined Optimization Method for Solving The Inverse Kinematics Problem of Mechanical Manipulators. IEEE Trans. on Robotic and Auto., 1991,7(4):213-220
11 K. Kazerounian. On The Numerical Inverse Kinematics of Robotic Manipulators. ASME Mechanisms Transmissions Autom at Design, 1987,109(5):8-13
12 W. T. Miller, F. H. Glanz and L. G. Kraft. Application of A General Learning Algorithm to The Control of Robotic Manipulator. Int. J. of Robotics Research, 1987,6(2):84-98
13 Z. H.Man, I. M. Palaniswam. Robust Tracking Control for Rigid Robotic Manipulators. IEEE Trans on Automatic Control, 1994,39(1):456-463
14 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
15 M. Spong, M. Vidyasagar. Robust Linear Compensator Design for Nonlinear Robotic Control. IEEE Trans. on Robotic and Auto., 1987,3(8):345-351
16 H. Asda. Representation and Learning of Nonlinear Compliance Using Neural Network. IEEE Trans. on Robotics and Automation, 1993(6):863-867
17 K. K. D. Young. Controller Design for A Manipulator Using Theory of Variable Structure Systems. IEEE Trans. on system, man and Cybenetic, 1978,8(2):110-128
18 Y. Stepanenko, Y. Cao and Y. S. Chun. Variable Structure Control of Robotic Manipulator with PID Sliding Surface. Int. J. of Robust and Nonlinear Control, 1998,12(8):79-90
19 K. S. Yeung, Y. P. Chen. A New Controller Design for A Manipulator Using Theory of Variable Structure System. IEEE Trans. on Auto. Contrl., 1988,35(2):995-1003
20 Y. F. Chen, T. Mita and S. Wakui. A New and Simple Algorithm for Sliding Mode Trajectory Control of Robot. IEEE Trans. on Automatic Control, 1990,35(7):828-829
21 Man et al. A Robust MIMO Terminal Sliding Mode Control Scheme for Rigid Robotic Manipulators. IEEE Trans. on Automatic Control, 1994,39(8):2462-2469
22 J. J. E. Slotine. The Robust Control of Robotic Manipulators. Int. J. of Robot and Research, 1985,12(4):49-54
23 J. Doyle, K. Glover. State-Space Solutions to Standard and Control Problem. IEEE Trans. on Automatic Control, 1989,34(8):831-847
24 A. Isidori, W. Kang. Control Via Measurement Feedback for General Nonlinear System. IEEE Trans on Automatic Control, 1995,40(3):831-847
25 A. vanderschaft. Gain Analysis of Nonlinear Systems and Nonlinear State Feedback Control. IEEE Trans. on Automatic Control, 1992,37(6):770-784
26 申铁龙. 控制理论与应用.北京：清华大学出版社,1996
27 W. L. Stout, M. E. Sawan. Application of Theory to Robot Manipulators Control. Proceeding of the Conference on Control Application, 1992:148-183
28 Borsen Chen. A Nonlinear Control Design In Robotic Systems Under Parameter Perturbation and External Disturbance. Int. J. of Robot Control, 1994,59(2):439-461
29 B. Danilo. High Precision Position Control by Catisian Trajectory Feedback and Connectinist Inverse Dynamic Feedback.IJCNN,1988:213-220
30 M. Leahy. Neural Networks Payload Estimation for Adaptive Robot Control. IEEE Trans. on Neural Network, 1991,13(2):45-53
31 W. Miler. Realtime Dynamic Control of An Industrial Manipulator Using A Neural Network Based Learning Controller. IEEE Trans. on Robot and Automation, 1990,5(1):1-9
32 M. Jodan. Interminate Motor Skill Learning Problems, Attention and Performance. MIT Press, 1989
33 Kawoto. Trajectory Formation of Arm Movement by Cascade Neural Network Model Based on Minimum Torque Change Criterion, Biol. Cybern., 1990:483-489
34 A. Guez. Neural Morphic Architecture Adaptive Robot Control: A Preliminary Analysis. Proceeding IEEE Int. Conf. on Neural Network, 1987,45(3):1056-1062
35 H. Asda. Representation and Learning of Nonlinear Compliance Using Neural Network. IEEE Trans. on Robotics and Automation, 1993,12(6):863-867
36 A. A. M. Karakasoglu. A Recurrent Neural Network Based Adaptive Variable Structure Model-Following Control of Robot. Automatica, 1995,31(10):1495-1507
37 K. K. Watanabe. A Nonlinear Robust Control Using Fuzzy Reasoning and Its Application to A Robot Manipulator. Journal of Intelligent and Robotic Systems, 1997,20(5):275-294
38 金耀初.机器人神经模糊控制.机器人.1995,(3):171-176
39 郑大钟.线性系统理论.北京:清华大学出版社, 1990:121-136
40 J. J. Slotine. Applied Nonlinear Control. Englewood Cliffs,NJ: Prentice Hall, 1991
41 P. LaSalle. The Stability of Dynamical Systems. SIAM, Philadelphia, 1976
42 申铁龙.机器人鲁棒控制基础.北京:清华大学出版社, 2000
43 王洪斌, 吴健珍, 杨香兰. Simulink(S-Function)在复杂控制系统中的应用. 系统仿真学报, 2001(13):131-132
44 G. C. Hwang and S. C. Lin. A Stability Approach to Fuzzy Control Design for Nonlinear Systems. Fuzzy Sets Syst., 1992,48(1):279-287
45 J. C. Wu and T. S. Liu. A Sliding Mode Approach to Fuzzy Control Design. IEEE Trans. Contr. Syst. Tech., 1996,4(3):141-148
46 Y. Ohtani and T. Yoshimura. Fuzzy Control of A Manipulator Using The Concept of Sliding Mode. Int. J. Syst. Sci., 1996,27(3):179-186
47 F. Y. Hsu and L. C. Fu. Adaptive Robust Fuzzy Control for Robot Manipulators. in Proc. Int. Conf. Robotics Automat., 1994,1(2):649-654
48 Slotine and W. Li. Composite Adaptive Control of Robot Manipulators. Automatica, 1989,25(2):509-519
49 H. A. Malki. New Design and Stability Analysis of Fuzzy Proportion-Derivative Control Systems. IEEE Trans. Fuzzy Syst., 1994,2(3):245-254
50 D. Misir. Design and Analysis of A Fuzzy Proportional-Integral-Derivative Controller. Fuzzy Sets Syst., 1995,79(6):97-102
51 L. X. Wang. Adaptive Fuzzy Systems and Control: Design and Stability Analysis. Englewood Cliffs,NJ:Prentice-Hall,1994
52 E. Kemalettin, K. Okyay and S. Asif. Fuzzy Adaptive Sliding Mode Control of A Direct Drive Robot. Robotics and Autonomous Systems, 1996,19:215-227
53 B. Yao and M. Tomizuka. Comparative Experiments of Robust and Adaptive Control with New Robust Adaptive Controllers for Robot Manipulators. Proc. of the IEEE Conference on Decision and Control, 1994:1290-1295
Y. G. Niu, C. W. Yang. An Adaptive Neural Robust Controller for Trajectory Tracking of Robot. Control Theory and Applications,
54 2000,17(6):924-928
55 C. T. Lin. Neural Fuzzy Systems New Jersey:Prentice-Hall,1996
56 Y. Q. Zhang, A. Kandel. Compensatory Neurofuzzy Systems with Fast Learning Algorithms. IEEE Trans. Neural Networks, 1998,9(2):83-105
57 F. J. Lin, W. J. Hwang and R. J. Wai. A Supervisory Fuzzy Neural Network Control System for Tracking Periodic Inputs. IEEE Trans. Fuzzy Systems, 1999,17(1):41-52
58 Y. G. Leu, T. T. Lee. Observer-Based Adaptive Fuzzy-Neural Control for Unknown Nonlinear Dynamical Systems. IEEE Trans. System Man and Cybernetics, 1999,29(5):583-591
59 Y. G. Leu, W. Y. Wang. Robust Adaptive Fuzzy-Neural Controllers for Uncertain Nonlinear Systems. IEEE Trans. Robot and Auto., 1999,15(5):805-817
60 H. J. Shieh and K. K. Shyu. Nonlinear Sliding-Mode Torque Control with Adaptive Backstepping Approach for Induction Motor Drive. IEEE Trans. Ind. Electron, 1999,46(2):380-389
61 F. J. Lin and C. C. Lee. Adaptive Backstepping Control for Linear Induction Motor Drive to Track Periodic Reference. IEE Proceeding Electronic Power App. 2000,147(6):449-458
62 K. L. Kristic, M. Kanellakopoulos. Nonlinear and Adaptive Control Design. New Jersey:Prentice Hall, 1995
63 S. Nicosia, P. Tomei. Robot Control by Using Only Joint Position Measurements. IEEE Trans. Automatic Control, 1990,35(9):1058-1061
64 C. Canudas, N. F. Wit. Trajectory Tracking in Robot Manipulators via Nonlinear Estimated State Feedback. IEEE Trans. Robotics and Automation, 1992,8(1):138-144
65 J. J. Slotine and E. A. Misawa. Sliding Observers for Nonlinear Systems. ASME-J. of Dynamics Syst. Meas. Control, 1987,109(2):245-252
66 C. Canudas, N. Fixot. Robot Control via Robust Estimated State Feedback. IEEE Trans. Automatic Control, 1991,36(12):1497-1501
C. Canudas, N. Fixot. Adaptive Control of Robot Manipulators via Velocity Estimated Feedback. IEEE Trans. Automatic Control,
67 1992,3(6):1234-1237
68 W. Zhu, H. Chen and Z. Zhong. A Variable Structure Robot Control Algorithm with An Observer. IEEE Trans. Robotics and Automation, 1992,8(4):486-492
69 Y. H. Kim, F. L. Lewis. Neural Network Output Feedback Control of Robot Manipulators. IEEE Trans. Robotics and Automation, 1999,15(2):301-309
70 K. Hornik, M. Stinchcombe and H. White. Universal Approximation of An Unknown Mapping and Its Derivatives Using Multiplayer Feedforword Neural Networks, 1990,20(3):551-560
71 C. Canudas and J. J. Slotine. Sliding Observers for Robot Manipulators. Automatica, 1991,27(5):859-864