期刊文献+

挠性卫星高精度智能控制及物理仿真实验研究 被引量:1

Intelligent Control and Physical Experiment Research for High Precision Attitude of Flexible Satellites
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摘要 从工程角度出发,以具有挠性太阳翼的卫星为背景,着重研究了挠性卫星的智能控制方案。针对高精度控制这一要求,设计了双层小脑模型神经网络(CMAC)与变结构(VSC)复合智能控制器,并基于单轴气浮台全物理仿真系统进行了实验,取得了较高的姿态控制精度,表明了智能控制方法的有效性。 Intelligent control scheme in view of engineering for flexible space structure was studied such as satellites with solar arrays. According to the requirement of high precision, intelligent control law composed of CMAC neural network and VSC with very simple algorithm and strong robustness was designed. With a physical simulation system based on a single axis air bearing table, the control scheme was realized successfully and high precision attitude was obtained. Results show that the method proposed in this paper is effective.
出处 《中国空间科学技术》 EI CSCD 北大核心 2007年第1期9-13,共5页 Chinese Space Science and Technology
关键词 智能控制 挠性结构 神经网络 物理仿真 卫星 Intelligent control Flexible structure Neural network Physical simulation Satellite
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参考文献8

  • 1高为炳 程勉 曾为林.柔性空间飞行器的变结构控制[J].航空学报,1983,9(5).
  • 2周军,李季苏,牟小刚,吴宏鑫.挠性卫星的变结构控制方案研究[J].宇航学报,1996,17(2):1-5. 被引量:11
  • 3ALBUS J S. A New Approach to Manipulator Control:The Celebellar Model Articulation Controller(CMAC)[J]. Transactions of ASME, Journal of Dynamic Systems, Measurement and Control, 1975, 97 (3):220--227.
  • 4MILLER W T, GLANZ F H, KRAFT L G . Application of a General Learning Algorithm to the Control of Robotics Manipulators [J]. Int J Robotics Research, 1987, 2(6): 84--98.
  • 5PARK H J, CHO H S. CMAC-based Iteraticle Learning Control for Hydraulic Servo System [C]. Proceeding of Fluid Power Control and Robotics, Chengdu, 1990: 439--444.
  • 6KING LUNG HUANG, SHU CHENG HSTESH , HSIN CHINFU. Cascade-CMAC Neural Network Application on the Color Scan-nerto Printer Calibration [J]. Proceedings of the 1997 IEEE International Conference on Neural Networks, 1997: 10-- 15.
  • 7COMMURI S, LEWIS F L. Control of Unknown Nonlinear Dynamical Systems Using CMAC Neural Networks: Structure, Stability, and Passivity [J]. Proceedings of the 1995 IEEE International:Symposium on Intelligent Control, 1995. 123--129.
  • 8HORNIK K, STEINCHOMBE M, WHITE H. Multilayer Feedforword Networks are Universal Approximator[J]. Neural Networks, 1989, 2: 359--366.

共引文献10

同被引文献6

  • 1CHIANG C T,LIN C S. CMAC with General Basis Functions[J].Neural Networks,1996,(07):1199-1211.
  • 2HU J,PRATT F. Self-organizing CMAC Neural Networks and Adaptive Dynamic Control[J].IEEE Intelligent Control,1999.259-265.
  • 3LEE H M,CHEN C M,LU Y F. A Self-organizing HCMAC Neural-network Classifier[J].IEEE Transactions,2003,(14):15-27.
  • 4LIN Chin-min,CHEN Te-yu. Self-Organizing CMAC Control for a Class of MIMO Uncertain Nonlinear Systems[J].IEEE Transactions on Neural Networks,2009.1377-1384.
  • 5ALEXANDRIDIS A,SARIMVEIS H,BAFAS G. A New Algorithm for Online Structure and Parameter Adaptation of RBF Networks[J].Neural Networks,2003.1003-1017.
  • 6QIN Ting,CHEN Zong-hai,ZHANG Hai-tao. A Learning Algorithm of CMAC Based on RLS[J].Neural Processing Leters,2004,(03):49-61.

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