挠性卫星姿态机动变结构神经网络控制方法研究
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摘要
卫星姿态控制系统是一个耦合的不确定非线性系统。在轨运行的卫星不可避免地受到模型参数不确定性和各种干扰力矩的影响,这些不确定性的存在使挠性卫星大角度姿态机动的控制问题进一步复杂化。因此,为了完成姿态控制任务,需要所设计的控制律具有较高的鲁棒性。本文就是在这种背景下,从理论和应用两个方面对卫星姿态控制系统的控制算法进行了深入的研究。主要完成了以下几个方面的工作:
首先,滑模控制有很多优点,如鲁棒性好、计算量小、实时性好、响应速度快等。因此,用滑模控制方法来控制挠性卫星姿态,用饱和函数代替符号函数来消除抖振现象。
其次,用滑模变结构进行挠性卫星姿态机动控制,用神经网络补偿不确定性,提高系统鲁棒性。
采用传统的小脑神经网络来逼近不确定性,此网络不容易产生局部极小现象。针对传统小脑神经网络实时性差,泛化能力不够好的缺点,研究了高斯基函数的小脑神经网络,运用高斯基函数代替了传统小脑神经网络量化的0或者1。针对高斯基函数小脑神经网络计算量大,应用不方便,研究了基于超立方体子空间的快速学习算法,大大提高了网络的学习速度。
以上的网络都需要知道输入的具体范围,这样对网络的应用有一定的限制,自组织小脑神经网络不需要知道输入范围,可根据输入值自动更新节点数和权值。这样对于挠性卫星姿态控制系统参数的变化能通过设计来满足精度要求。
Satellite attitude control system is a coupled uncertain nonlinear system. For any on-orbit satellite, it is inevitable to be influenced by some kinds of uncertain parameters and disturbance torques, which makes the attitude control problem further complicated. Therefore, to accomplish attitude control mission, it is necessary to design attitude control laws with high robustness. On this background, this thesis investigated attitude control algorithms for satellite attitude control system in detail, from both theoretical and applicable aspects, and applied the proposed control schemes to certain satellite control system. The main contents of this thesis are as follows.
First, Sliding mode control has many advantages, such as high robustness, easy calculation, good real-time characteristic, quick response characteristic. The paper use this theory to realize the attitude maneuver control of flexible satellite,using saturation function replace sign function to Eliminate Buffeting Phenomenon.
Second, the paper use sliding mode to control the satellite, using neural networks to compensate the uncertainty, which is proved by Lyapunov stability theory.
The paper use tradition CMAC neural network method to approach uncertain function, which can solve the local minimum phenomenon. However, the tradition CMAC has bad real-time and generalization characteristic, soGaussian function CMAC, which can quantified as a continuous gaussian function, is proposed to replace the tradition CMAC. Despite of this, gaussian function CMAC is complex to calculation, a fast algorithm CMAC is a new way to improve computing speed.
All of the above neural networks, the range of the input signal must be awared of in advance, which will limit the application of the network. However, it is not necessaryto know the range of the input signal using self-organization CMAC neural network, which can update of structure and weights automatically. It is more convenient and intelligent.So, for the changes in parameters, this flexible satellite attitude control system can be designed to meet the accuracy requirements.
首先,滑模控制有很多优点,如鲁棒性好、计算量小、实时性好、响应速度快等。因此,用滑模控制方法来控制挠性卫星姿态,用饱和函数代替符号函数来消除抖振现象。
其次,用滑模变结构进行挠性卫星姿态机动控制,用神经网络补偿不确定性,提高系统鲁棒性。
采用传统的小脑神经网络来逼近不确定性,此网络不容易产生局部极小现象。针对传统小脑神经网络实时性差,泛化能力不够好的缺点,研究了高斯基函数的小脑神经网络,运用高斯基函数代替了传统小脑神经网络量化的0或者1。针对高斯基函数小脑神经网络计算量大,应用不方便,研究了基于超立方体子空间的快速学习算法,大大提高了网络的学习速度。
以上的网络都需要知道输入的具体范围,这样对网络的应用有一定的限制,自组织小脑神经网络不需要知道输入范围,可根据输入值自动更新节点数和权值。这样对于挠性卫星姿态控制系统参数的变化能通过设计来满足精度要求。
Satellite attitude control system is a coupled uncertain nonlinear system. For any on-orbit satellite, it is inevitable to be influenced by some kinds of uncertain parameters and disturbance torques, which makes the attitude control problem further complicated. Therefore, to accomplish attitude control mission, it is necessary to design attitude control laws with high robustness. On this background, this thesis investigated attitude control algorithms for satellite attitude control system in detail, from both theoretical and applicable aspects, and applied the proposed control schemes to certain satellite control system. The main contents of this thesis are as follows.
First, Sliding mode control has many advantages, such as high robustness, easy calculation, good real-time characteristic, quick response characteristic. The paper use this theory to realize the attitude maneuver control of flexible satellite,using saturation function replace sign function to Eliminate Buffeting Phenomenon.
Second, the paper use sliding mode to control the satellite, using neural networks to compensate the uncertainty, which is proved by Lyapunov stability theory.
The paper use tradition CMAC neural network method to approach uncertain function, which can solve the local minimum phenomenon. However, the tradition CMAC has bad real-time and generalization characteristic, soGaussian function CMAC, which can quantified as a continuous gaussian function, is proposed to replace the tradition CMAC. Despite of this, gaussian function CMAC is complex to calculation, a fast algorithm CMAC is a new way to improve computing speed.
All of the above neural networks, the range of the input signal must be awared of in advance, which will limit the application of the network. However, it is not necessaryto know the range of the input signal using self-organization CMAC neural network, which can update of structure and weights automatically. It is more convenient and intelligent.So, for the changes in parameters, this flexible satellite attitude control system can be designed to meet the accuracy requirements.
引文
1施少范.国外对地观测卫星高精度姿态控制系统研究.上海航天报. 2000,6:49~53
2 H. Bittner, E. Bruderle. Closed Loop Dynamic Testing of the Intelsat, Attitude Determination and Control Subsystem Hardware. IFAC Symposium on Automatic Control in Space. 2007, 2(2):2~5
3李勇,魏春岭.卫星自主导航技术发展综述.航天控制. 2002, (2): 70~74
4 W. D. Deininger. Space Technology Three: Mission Overview and Spacecraft Concept Description. Astronautica. 2003,52:455~465
5张勇.航天器模拟实验系统设计与研究.哈尔滨工业大学硕士论文. 2008
6刘亚静.三轴稳定卫星姿态控制方法研究.哈尔滨工业大学硕士论文. 2008
7吴宏鑫.全系数自适应控制理论与应用[M].国防工业出版社. 1990
8吴宏鑫,刘一武,刘忠汉,解永春.特征建模与挠性结构的控制[J].中国科学( E辑) . 2001,31(2):137~149
9解永春,牟小刚,吴宏鑫,李季苏.挠性航天器大角度机动的全系数自适应控制. 1999,20(2):1~6
10齐春子. TDRS多变量自适应控制方法研究[D].中国空间技术研究院博士学位论文. 1999
11齐春子,吴宏鑫,吕振铎.多变量全系数自适应控制系统稳定性的研究[J].控制理论与应用. 2000,17(4):489~494
12孙多青,吴宏鑫.四阶时变离散系统的一致渐近稳定性[J].控制理论与应用. 2006,23(6):845~852
13 Korovin S K, Utkin V I. Using sliding modes in static optimization and nonlinear programming. Automatic. 1974,10(5):525~532
14 Liman Yang, Yunhua Li, Li zuo. A Robust Sliding Mode Control with RBFNN Compensation For Uncertain Networked Control System. IEEE. 2008:1189 ~1194
15 Dun-wei Gong, Jian-hua Zhang, Yi-nan Guo. Design of sliding mode controller for a class of networked control systems with uncertaintie. Control and Decision. 2006,21(10):1197~1200
16 Yunhua Li, Liman Yang. A class of disturbance-estimated sliding mode control strategy for uncertain networked control system. IEEE International Conference on Robotics, Automation. 2006
17 Zhangzhi Qiu, Qingling Zhang. Robust controller design for dynamic output feedback networked control systems with uncertain time delay. Journal of System Engineering. 2007,22(2):176~180
18 Liman Yang, Yunhua Li. A kind of sliding mode control strategy based on delay estimation online for networked control system with stochastic less delay. IEEE International Conference on Robotics, Automation and Mechatronics. 2006:847~853
19高为炳,程勉.变结构控制的品质控制[J].控制与决策. 1989,4(4):1~6
20胡庆雷.挠性航天器姿态机动的动振动控制.哈尔滨工业大学博士论文. 2006
21胡庆雷,马广富.带有输入非线性的挠性航天器姿态机动变结构控制.宇航学报. 2006,27(4):630~634
22胡庆雷,马广富.基于变结构/输入成形的航天器振动抑制方法.2006,38(10):1679~1777
23朱良宽,胡庆雷,马广富,胡庆雷.带有死区非线性输入的挠性航天器姿态机动智能控制.吉林大学学报. 2008,38(增刊):195~200
24陈宇,董朝阳,王青,张明廉.柔性卫星大角度机动的自适应模糊变结构控制.北京航空航天大学学报. 2007,33(7):761~764
25管萍,陈家斌.挠性卫星的自适应模糊滑模控制.航天控制. 2004,22(4):62~ 67
26 Heejin Lee, Euntai Kim, Hyung-Jin Kang, Migmon Park. A new sliding-mode control with fuzzy boundary layer. Fuzzy sets and systems. 2001:135~143
27周军,周凤岐,李季苏,牟小刚.挠性卫星大角度机动变结构控制的全物理仿真实验研究.宇航学报. 1999,20(1):66~70
28靳瑾,张景瑞,刘藻珍.航天器大角度姿态快速机动控制器参数优化设计.清华大学学报(自然科学版). 2009,49(2):289~292
29 Wang Zi-Qin etc. Hash encoding in CMAC Neural Networks[R]. Proceedings of the 1996 IEEE International Conference on Neural Networks, Washington,DC, USA.1996:1698~1703
30朱大奇,孔敏.基于平衡学习的神经网络非线性滑模容错控制.控制理论与应用. 2008,25(1):81~86
31 Parks P C, Militzer J. Convergence Properties of Associative Memory Storage for Learning Control System. Automation and Remote Control. 1989,(10): 254~ 258
32 Wong Y, Sideris A. Learning Convergence in the Cerebellar Model Articulation controller. IEEE Trans. Neural Networks. 1992,3(6):115~121
33 Nguyen, M.N, Shi, D., Quek, C.FCMAC-BYY. Fuzzy CMAC Using Bayesian Ying-Yang Learning Systems.Man and Cybernetics, Part B, IEEE Transactions. 2006,36:1180~1190
34 Tung-Sheng Chiang, Chian-Song Chiu. TS-CMAC Based Sliding mode control for time-delay systems. IEEE International Conference on Fuzzy Systems. 2006, 1918~1923
35 Chun-Sheng Chen. Sliding-Mode-Based Fuzzy CMAC Controller Design for a Class of Uncertain Nonlinear System. 2009:3030-3034
36 C.M.Kwan, H.Xu, F.L.Lewis, L.Haynes, D.Pryor.Robust spacecraft attitude control using fuzzy CMAC.Proceedings of the 1996 IEEE International Symposium on Intelligent control. 1996:43~48
37 C. T. Chiang and C. S. Lin. CMAC with general basis functions. J. Neural Networks. 1996,9(7):1199~1211
38王源,胡寿松.基于自组织模糊CMAC网络的非线性系统鲁棒自适应跟踪控制.自动化学报. 2002,28(6):984~989
39 J. Hu, F. Pratt. Self-organizing CMAC neural networks and adaptive dynamic control. IEEE Intelligent Control. 1999: 259~265
40 H. M. Lee, C. M. Chen, Y. F. Lu.A self-organizing HCMAC neural-network classifier. IEEE Trans. 2003,14(14):15~27
41 Chin-Min Lin, Te-Yu Chen. Self-Organizing CMAC control for a class of MIMO uncertain nonlinear systems.IEEE transactions on nueral networks. 2009,20: 1377~1384
42 Ting Qin, Zonghai Chen, Haitao Zhang, Sifu Li, Wei Xiang, Ming Li. A Learning Algorithm of CMAC Based on RLS. Neural Processing Leters. 2004, 19(3):49~6l
43章仁为.卫星轨道姿态动力学与控制.北京航空航天大学出版社. 1998
44 HouL WU W, Zhu B, Li F. A segmentation method for mergede characters usingself-organizing map neural networks. Journal of Information and Computational Seience. 2006, 3(2):219~26.
45李士勇.模糊控制?神经控制和智能控制论.哈尔滨工业大学出版社. 1996
46南东. RBF和MLP神经网络逼近能力的几个结果.大连理工大学博士论文. 2007
47李广兴,周军,周凤岐.挠性卫星高精度智能控制及物理仿真实验研究.中国空间科学技术. 2007,1:9~13
2 H. Bittner, E. Bruderle. Closed Loop Dynamic Testing of the Intelsat, Attitude Determination and Control Subsystem Hardware. IFAC Symposium on Automatic Control in Space. 2007, 2(2):2~5
3李勇,魏春岭.卫星自主导航技术发展综述.航天控制. 2002, (2): 70~74
4 W. D. Deininger. Space Technology Three: Mission Overview and Spacecraft Concept Description. Astronautica. 2003,52:455~465
5张勇.航天器模拟实验系统设计与研究.哈尔滨工业大学硕士论文. 2008
6刘亚静.三轴稳定卫星姿态控制方法研究.哈尔滨工业大学硕士论文. 2008
7吴宏鑫.全系数自适应控制理论与应用[M].国防工业出版社. 1990
8吴宏鑫,刘一武,刘忠汉,解永春.特征建模与挠性结构的控制[J].中国科学( E辑) . 2001,31(2):137~149
9解永春,牟小刚,吴宏鑫,李季苏.挠性航天器大角度机动的全系数自适应控制. 1999,20(2):1~6
10齐春子. TDRS多变量自适应控制方法研究[D].中国空间技术研究院博士学位论文. 1999
11齐春子,吴宏鑫,吕振铎.多变量全系数自适应控制系统稳定性的研究[J].控制理论与应用. 2000,17(4):489~494
12孙多青,吴宏鑫.四阶时变离散系统的一致渐近稳定性[J].控制理论与应用. 2006,23(6):845~852
13 Korovin S K, Utkin V I. Using sliding modes in static optimization and nonlinear programming. Automatic. 1974,10(5):525~532
14 Liman Yang, Yunhua Li, Li zuo. A Robust Sliding Mode Control with RBFNN Compensation For Uncertain Networked Control System. IEEE. 2008:1189 ~1194
15 Dun-wei Gong, Jian-hua Zhang, Yi-nan Guo. Design of sliding mode controller for a class of networked control systems with uncertaintie. Control and Decision. 2006,21(10):1197~1200
16 Yunhua Li, Liman Yang. A class of disturbance-estimated sliding mode control strategy for uncertain networked control system. IEEE International Conference on Robotics, Automation. 2006
17 Zhangzhi Qiu, Qingling Zhang. Robust controller design for dynamic output feedback networked control systems with uncertain time delay. Journal of System Engineering. 2007,22(2):176~180
18 Liman Yang, Yunhua Li. A kind of sliding mode control strategy based on delay estimation online for networked control system with stochastic less delay. IEEE International Conference on Robotics, Automation and Mechatronics. 2006:847~853
19高为炳,程勉.变结构控制的品质控制[J].控制与决策. 1989,4(4):1~6
20胡庆雷.挠性航天器姿态机动的动振动控制.哈尔滨工业大学博士论文. 2006
21胡庆雷,马广富.带有输入非线性的挠性航天器姿态机动变结构控制.宇航学报. 2006,27(4):630~634
22胡庆雷,马广富.基于变结构/输入成形的航天器振动抑制方法.2006,38(10):1679~1777
23朱良宽,胡庆雷,马广富,胡庆雷.带有死区非线性输入的挠性航天器姿态机动智能控制.吉林大学学报. 2008,38(增刊):195~200
24陈宇,董朝阳,王青,张明廉.柔性卫星大角度机动的自适应模糊变结构控制.北京航空航天大学学报. 2007,33(7):761~764
25管萍,陈家斌.挠性卫星的自适应模糊滑模控制.航天控制. 2004,22(4):62~ 67
26 Heejin Lee, Euntai Kim, Hyung-Jin Kang, Migmon Park. A new sliding-mode control with fuzzy boundary layer. Fuzzy sets and systems. 2001:135~143
27周军,周凤岐,李季苏,牟小刚.挠性卫星大角度机动变结构控制的全物理仿真实验研究.宇航学报. 1999,20(1):66~70
28靳瑾,张景瑞,刘藻珍.航天器大角度姿态快速机动控制器参数优化设计.清华大学学报(自然科学版). 2009,49(2):289~292
29 Wang Zi-Qin etc. Hash encoding in CMAC Neural Networks[R]. Proceedings of the 1996 IEEE International Conference on Neural Networks, Washington,DC, USA.1996:1698~1703
30朱大奇,孔敏.基于平衡学习的神经网络非线性滑模容错控制.控制理论与应用. 2008,25(1):81~86
31 Parks P C, Militzer J. Convergence Properties of Associative Memory Storage for Learning Control System. Automation and Remote Control. 1989,(10): 254~ 258
32 Wong Y, Sideris A. Learning Convergence in the Cerebellar Model Articulation controller. IEEE Trans. Neural Networks. 1992,3(6):115~121
33 Nguyen, M.N, Shi, D., Quek, C.FCMAC-BYY. Fuzzy CMAC Using Bayesian Ying-Yang Learning Systems.Man and Cybernetics, Part B, IEEE Transactions. 2006,36:1180~1190
34 Tung-Sheng Chiang, Chian-Song Chiu. TS-CMAC Based Sliding mode control for time-delay systems. IEEE International Conference on Fuzzy Systems. 2006, 1918~1923
35 Chun-Sheng Chen. Sliding-Mode-Based Fuzzy CMAC Controller Design for a Class of Uncertain Nonlinear System. 2009:3030-3034
36 C.M.Kwan, H.Xu, F.L.Lewis, L.Haynes, D.Pryor.Robust spacecraft attitude control using fuzzy CMAC.Proceedings of the 1996 IEEE International Symposium on Intelligent control. 1996:43~48
37 C. T. Chiang and C. S. Lin. CMAC with general basis functions. J. Neural Networks. 1996,9(7):1199~1211
38王源,胡寿松.基于自组织模糊CMAC网络的非线性系统鲁棒自适应跟踪控制.自动化学报. 2002,28(6):984~989
39 J. Hu, F. Pratt. Self-organizing CMAC neural networks and adaptive dynamic control. IEEE Intelligent Control. 1999: 259~265
40 H. M. Lee, C. M. Chen, Y. F. Lu.A self-organizing HCMAC neural-network classifier. IEEE Trans. 2003,14(14):15~27
41 Chin-Min Lin, Te-Yu Chen. Self-Organizing CMAC control for a class of MIMO uncertain nonlinear systems.IEEE transactions on nueral networks. 2009,20: 1377~1384
42 Ting Qin, Zonghai Chen, Haitao Zhang, Sifu Li, Wei Xiang, Ming Li. A Learning Algorithm of CMAC Based on RLS. Neural Processing Leters. 2004, 19(3):49~6l
43章仁为.卫星轨道姿态动力学与控制.北京航空航天大学出版社. 1998
44 HouL WU W, Zhu B, Li F. A segmentation method for mergede characters usingself-organizing map neural networks. Journal of Information and Computational Seience. 2006, 3(2):219~26.
45李士勇.模糊控制?神经控制和智能控制论.哈尔滨工业大学出版社. 1996
46南东. RBF和MLP神经网络逼近能力的几个结果.大连理工大学博士论文. 2007
47李广兴,周军,周凤岐.挠性卫星高精度智能控制及物理仿真实验研究.中国空间科学技术. 2007,1:9~13