基于非线性滤波的小卫星姿态确定及控制研究
|
摘要
现代小卫星以其高性能、低风险、短周期、低投入等特点,成为空间技术发展的一个重要方向。但由于受到成本、尺寸、质量、功耗等条件的限制,使得小卫星常常采用低精度、低功耗敏感器进行姿态确定及控制,致使姿态确定及控制水平普遍较低,严重影响了小卫星的总体性能。为了更好的提高小卫星性能,满足未来任务的需求,研究基于低精度敏感器实现高精度姿态确定及控制的技术,是有着重要意义的。论文正是以此为背景,研究解决了以下几个关键问题,主要内容如下:
研究了多种改进的EKF姿态确定方法。在对传统的EKF方法进行了深入研究的基础上,通过对观测矢量的进一步推导,获得了基于角速度的观测方程,丰富了观测模型,提高了观测数据的利用率,并在此基础上对姿态确定流程进行了设计,改进了传统EKF算法,并通过仿真,获得了较高的姿态确定精度。
研究了联邦滤波算法和预测滤波算法并将其应用于小卫星姿态确定中。通过对联邦滤波算法的研究,提出了基于新息观测的信息分配因子方法,该方法通过将观测新息引入到信息分配因子中去,提高了信息分配因子的准确性和可靠性,进而提高了改进联邦滤波算法的姿态确定精度。通过对预测滤波算法的研究,提出了将模型误差及角速度误差同时进行解耦估计的预测滤波策略,提高了模型的准确性,加速了角速度的收敛速度,进而增强了改进预测滤波算法处理非线性、非高斯白噪声的能力,通过仿真验证了两种方法均具有较高的滤波精度。
研究了粒子滤波加速方法并将其应用于小卫星姿态确定中。通过对粒子滤波理论的学习、研究,大胆引入极限近似的思想,以此证明了采用少量粒子进行状态估计的可行性,并将“局部设计”的思想引入贝叶斯滤波及序贯重要性采样方法中,通过设计混合重采样方法进一步完善了整个粒子滤波加速方法,并在此基础上,对其进行了未知参数估计的增广研究。最后以小卫星姿态确定为例,提出了运用粒子滤波加速方法进行状态估计的一般流程,并取得了较前文方法更高的滤波精度。
研究了基于滑模变结构控制的小卫星姿态控制方法。研究了基于I/O线性化的滑模变结构模糊自适应控制方法,取得了较好的控制效果,在此基础上,通过对非奇异Terminal滑模变结构的设计,提出了非奇异Terminal滑模变结构模糊自适应控制,验证了该方法可以在有限时间内实现姿态控制的快速收敛,通过进一步对该思想的完善,提出了全局快速积分Terminal滑模自适应控制,不仅使小卫星的姿态在有限时间内实现快速收敛,同时增强了其抗干扰能力。最后,通过仿真验证了控制方法的可靠性和有效性。
论文研究的非线性滤波方法及滑模变结构控制理论,具有较强的非线性处理能力,可以实现低精度敏感器的高精度姿态确定及控制,有助于提高小卫星的总体性能,论文的研究工作可以为这一领域的技术研究和发展提供方案参考和技术支持。
With fast development of space technology, modern small satellite plays a more and more important role due to its advantages of high performance, low-risk, short periods and low-cost. However, sensors with low precision and power consumption are usually adopted for small satellite because of the limitations on cost, size, mass and power, which decreses the level of attitude determination and control system (ADCS) and the whole performance. To solve this problem, methods for high precision attitude determination and control with low precision sensors are presented in this dissertation.
The key technologies presented here are listed as following:
Several EKF based attitude determination methods are studied. Comprehensive research on traditional EKF is carried out, and an observation equation is derived from observation vectors, which can enrich the traditional observation equation and enhance the utilization efficiency of observation data. Then the flow of attitude determination is designed and several improved EKF algorithms are proposed, all of which can achieve better accuracy.
The application of Federal Filter and Predictive Filter on snall satellite attitude determination is investigated. To enhance the accuracy and reliability of information distribution gene in Federated Filter, a new method of information distribution gene is proposed based on better utilization of new observation information; so as to further improve the attitude determination precision. To improve the mode accuracy and the convergence efficiency of angle velocity in Predictive Filter, a new approach to estimate the mode error and angle velocity error concurrently by decoupling is proposed, which can better handle nonlinear and non-Gaussian white noises. The high performance of filter precision of these two new proposed methods are verified by simulation results.
The accelerating Particle Filter method and its application in small satellite attitude determination are discussed. The idea of limit approximation is introduced into to the Particle Filter theory, which proves the feasibility of estimating state variables with fewer amounts of particles. To enhance the accelerating Particle Filter method, a new mix-resampling method is put forward by introducing“local-design”into the theory of Bayes Filter and Sequential Importance Sampling, based on which extension Particle Filter to estimate unknown parameters is further studied. At last, a general flow of parameter estimation by accelerating Particle Filter is developed, and tested in the example of small satellite attitude determination, which verifies its high performance of filter precision and even better compared with the aforementioned approaches.
The small satellite attitude control based on sliding mode control is studied. The I/O linearization based adaptive fuzzy sliding mode control is firstly investigated, which achieves good control performance. Based on this method, a non-singular terminal adaptive fuzzy sliding mode control approach is proposed, and its capability of fast convergence in attitude control within limited time is proved. To perfect this research, a new control policy of global fast integral terminal sliding mode control is put forward, which can better improve fast convergence speed and anti-jamming effect. The three foregoing control policies are combined with accelerating Particle Filter and tested by simulations, which have verified their reliability and validity.
The nonlinear filter method and variable structure control theory proposed in this dissertation have the powerful capacities to deal with nonlinearity and benefit to implement high-precision attitude determination and control with low-precision sensors in order to improve the system performance for small satellite. The results in this dissertation can provide reference and technique support for future research in this field.
研究了多种改进的EKF姿态确定方法。在对传统的EKF方法进行了深入研究的基础上,通过对观测矢量的进一步推导,获得了基于角速度的观测方程,丰富了观测模型,提高了观测数据的利用率,并在此基础上对姿态确定流程进行了设计,改进了传统EKF算法,并通过仿真,获得了较高的姿态确定精度。
研究了联邦滤波算法和预测滤波算法并将其应用于小卫星姿态确定中。通过对联邦滤波算法的研究,提出了基于新息观测的信息分配因子方法,该方法通过将观测新息引入到信息分配因子中去,提高了信息分配因子的准确性和可靠性,进而提高了改进联邦滤波算法的姿态确定精度。通过对预测滤波算法的研究,提出了将模型误差及角速度误差同时进行解耦估计的预测滤波策略,提高了模型的准确性,加速了角速度的收敛速度,进而增强了改进预测滤波算法处理非线性、非高斯白噪声的能力,通过仿真验证了两种方法均具有较高的滤波精度。
研究了粒子滤波加速方法并将其应用于小卫星姿态确定中。通过对粒子滤波理论的学习、研究,大胆引入极限近似的思想,以此证明了采用少量粒子进行状态估计的可行性,并将“局部设计”的思想引入贝叶斯滤波及序贯重要性采样方法中,通过设计混合重采样方法进一步完善了整个粒子滤波加速方法,并在此基础上,对其进行了未知参数估计的增广研究。最后以小卫星姿态确定为例,提出了运用粒子滤波加速方法进行状态估计的一般流程,并取得了较前文方法更高的滤波精度。
研究了基于滑模变结构控制的小卫星姿态控制方法。研究了基于I/O线性化的滑模变结构模糊自适应控制方法,取得了较好的控制效果,在此基础上,通过对非奇异Terminal滑模变结构的设计,提出了非奇异Terminal滑模变结构模糊自适应控制,验证了该方法可以在有限时间内实现姿态控制的快速收敛,通过进一步对该思想的完善,提出了全局快速积分Terminal滑模自适应控制,不仅使小卫星的姿态在有限时间内实现快速收敛,同时增强了其抗干扰能力。最后,通过仿真验证了控制方法的可靠性和有效性。
论文研究的非线性滤波方法及滑模变结构控制理论,具有较强的非线性处理能力,可以实现低精度敏感器的高精度姿态确定及控制,有助于提高小卫星的总体性能,论文的研究工作可以为这一领域的技术研究和发展提供方案参考和技术支持。
With fast development of space technology, modern small satellite plays a more and more important role due to its advantages of high performance, low-risk, short periods and low-cost. However, sensors with low precision and power consumption are usually adopted for small satellite because of the limitations on cost, size, mass and power, which decreses the level of attitude determination and control system (ADCS) and the whole performance. To solve this problem, methods for high precision attitude determination and control with low precision sensors are presented in this dissertation.
The key technologies presented here are listed as following:
Several EKF based attitude determination methods are studied. Comprehensive research on traditional EKF is carried out, and an observation equation is derived from observation vectors, which can enrich the traditional observation equation and enhance the utilization efficiency of observation data. Then the flow of attitude determination is designed and several improved EKF algorithms are proposed, all of which can achieve better accuracy.
The application of Federal Filter and Predictive Filter on snall satellite attitude determination is investigated. To enhance the accuracy and reliability of information distribution gene in Federated Filter, a new method of information distribution gene is proposed based on better utilization of new observation information; so as to further improve the attitude determination precision. To improve the mode accuracy and the convergence efficiency of angle velocity in Predictive Filter, a new approach to estimate the mode error and angle velocity error concurrently by decoupling is proposed, which can better handle nonlinear and non-Gaussian white noises. The high performance of filter precision of these two new proposed methods are verified by simulation results.
The accelerating Particle Filter method and its application in small satellite attitude determination are discussed. The idea of limit approximation is introduced into to the Particle Filter theory, which proves the feasibility of estimating state variables with fewer amounts of particles. To enhance the accelerating Particle Filter method, a new mix-resampling method is put forward by introducing“local-design”into the theory of Bayes Filter and Sequential Importance Sampling, based on which extension Particle Filter to estimate unknown parameters is further studied. At last, a general flow of parameter estimation by accelerating Particle Filter is developed, and tested in the example of small satellite attitude determination, which verifies its high performance of filter precision and even better compared with the aforementioned approaches.
The small satellite attitude control based on sliding mode control is studied. The I/O linearization based adaptive fuzzy sliding mode control is firstly investigated, which achieves good control performance. Based on this method, a non-singular terminal adaptive fuzzy sliding mode control approach is proposed, and its capability of fast convergence in attitude control within limited time is proved. To perfect this research, a new control policy of global fast integral terminal sliding mode control is put forward, which can better improve fast convergence speed and anti-jamming effect. The three foregoing control policies are combined with accelerating Particle Filter and tested by simulations, which have verified their reliability and validity.
The nonlinear filter method and variable structure control theory proposed in this dissertation have the powerful capacities to deal with nonlinearity and benefit to implement high-precision attitude determination and control with low-precision sensors in order to improve the system performance for small satellite. The results in this dissertation can provide reference and technique support for future research in this field.
引文
[1]郑敏.基于变结构自适应控制方法的小卫星姿态控制研究[D].南京:南京航空航天大学(硕士),2008.
[2]余金培,杨根庆,梁旭文.现代小卫星技术与应用[M].上海:上海科学普及出版社,2004.
[3]黄玔圭.航天器姿态动力学[M].长沙:国防科技大学出版社,1997.
[4]李琳琳.卫星自主轨道确定及姿态确定技术研究[R].北京: 2003.
[5] Wright P S W H S. An Overview of Sensors in Spacecraft Engineering [C]. IEEE Colloquium on Satellite Instrumenation. 1988(1).
[6]林玉荣.基于星敏感器确定卫星姿态的滤波算法研究[D].哈尔滨:哈尔滨工业大学研究生院博士学位论文(2002).
[7]钱勇.高精度三轴稳定卫星姿态确定和控制系统研究[D].西安:西安工业大学研究生院博士学位论文(2002).
[8]李立宏.适用于星跟踪器的星图识别算法及飞行器姿态估计研究[D].哈尔滨:哈尔滨工业大学研究生院博士学位论文(2000.
[9] Hsing J C R A, Barrentt M F. Gyro-Based Attitude Reference Systems for Communications Satellite [J]. Journal of Guidance and Control, 1980, (3(3)):239~244.
[10] Reid D B. Description of the Milstar Attitude Determination System[C] IEEE Control Conference. The American: 1997: 2313~2322
[11] Farrenkopf R L. Generalized Results for Precision Attitude reference Systems Using Gyros [C]. AIAA-74-0903. 1974.
[12] Wahba G. A Least Squares Estimate of Spacecraft Attitude [J]. SIAM Review, 1965, (7(3)):409~415.
[13] Shuster M D O S D. Three-Axis Attitude Determination from Vector Observations [C]. AIAA-81-4003. 1981.
[14] Bar-Itzhack I Y R H. Optimized TRIAD Algorithm for Attitude Determination [C]. AIAA/AAS Astrodynamics Conference. San Diego, CA, AIAA-1996-3616, 1996.
[15] Choukroun D B-I I Y, Oshman Y. Optimal Request Algorithm for Attitude Determination [C]. AIAA Guidance, Navigation and Control Conference and Exhibit. Montreal Canada,AIAA 2001-4153, 2001.
[16] Bar-Itzhack I Y. "Request"-A Recursive Quest Algorithm for Sequential Attitude Determination [C]. AIAA-1996-3616, 1996.
[17] Markley F L. Attitude Determination Using Vector Observation: A FastOptimal Matrix Algorthm [J]. Journal of the Astronautical Sciences, 1993, 41(2):261~280.
[18] Mortari D. Euler-q Algorithm for Attitude Determination from Vector Observations [J]. Journal of Guidance, Control, and Dynamics, 1998, 21(2):328~334.
[19]杨艳,赵黎平,赵光恒.基于磁强计测量的一种姿态估计方法研究[J].航天控制, 2006, 24 (4):51～56.
[20]张锐,朱振才,张静等.基于磁强计的卫星卫星姿态确定[J].宇航学报, 2006, 27 (4):578～581.
[21]田菁,吴美平,胡小平.三轴磁强计姿态确定[J].国防科技大学学报, 2001, 23 (5):17～22.
[22]刘建业,段方,李丹,郁丰.基于太阳电池板与磁强计的小卫星姿态确定算法研究[J].宇航学报, 2007, 28 (1):218～222.
[23]程杨,杨涤,崔祜涛.利用修正罗德里格参数进行飞行器姿态估计[J].飞行力学, 2002, 20 (4):18～21.
[24] Hyo-Sung A S-H L. Gyroless attitude estimation of sun-point-ing satellite using magnetometers [J]. Geoscience and Remote Sensiong Letters,IEEE, 2005, 2 (1):8~12.
[25]张金槐,蔡洪.飞行器试验统计学[M].长沙:国防科大大学出版社,1995.
[26] Kadirkamanathan V L P, Kirubarajan T. Sequential Monte Carlo Filtering vs. the IMM Estimator for Fault Detection and Isolation in Nonlinear Systems [C]. SPIE. 2001.
[27]屠善澄.卫星姿态动力学与控制[M].北京:宇航出版社,1998.
[28]章仁为.卫星轨道姿态动力学与控制[M].北京:北京航空航天大学出版社,1998.
[29]廖晖.对地定向三轴稳定卫星姿态确定和控制系统研究[D].西安:西北工业大学研究生院博士学位论文(2000).
[30]林玉荣,邓正隆.一类非线性预测滤波器的鲁棒稳定性分析[J].电机与控制学报, 2006, 10 (1):97～101.
[31]廖晖,周凤岐,周军.利用预测滤波法估计小卫星姿态角速度[J].西北工业大学学报, 2001, 19 (1):84～87.
[32] Crassidis J L M F L. Predictive Filtering for Attitude Estimation without Rate Sensor [J]. Journal of Guidance, Control, and Dynamics, 1997, 20 (3):522~527.
[33] Crassidis J L M F L. Predictive Filtering for Nonlinear Systems [J]. Journal of Guidance, Control, and Dynamics, 1997, 20 (3):566~572.
[34]郁丰,刘建业,熊智.预测滤波算法在微小卫星姿态确定中的应用[J].宇航学报, 2008, 29 (1):110～114.
[35]林玉荣,邓正隆.基于模型误差确定卫星姿态的预测滤波算法[J].宇航学报, 2001, 22 (1):79～84.
[36]林玉荣,邓正隆.基于MME准则估计卫星姿态的非线性滤波算法[J].哈尔滨工业大学学报, 2002, 34 (1):14～18.
[37] Mook D J J J L. Minimum Model Error Estimation for Poorly Modeled Dynamic System [J]. Journal of Guidance, Control, and Dynamics, 1988, 11 (3):256~261.
[38] Lu P. Nonlinear Predictive Controllers for Continuous Systems [J]. Journal of Guidance, Control, and Dynamics, 1994, 17 (3):553~560.
[39]张红梅,邓正隆,林玉荣.一种基于模型误差预测的UKF方法[J].航空学报, 2004, 25 (6):598～601.
[40]朱长征.基于星敏感器的星模式识别算法及空间飞行器姿态确定技术研究[D].长沙:2004.
[41]潘泉,杨小军,张洪才.粒子滤波器:进展与展望[J].控制理论与应用, 2006, 23 (2)
[42]梁彦,潘泉,杨峰,张磊.复杂系统的现代估计理论及应用[M].背景:科学出版社,2009.
[43] Blom H A P B E A. Joint IMMPDA particle filter [C]. Proceeding of Sixth International Conference of Information Fusion. 2003.
[44] Boers Y D J N. Interacting multiple model particle filter[C] IEEE Proceedings on Radar, Sonar and Navigation. 2003: 344~349
[45] Daum F E. Exact finite dimensional nonlinear filters [C]. IEEE Trans. on Automatic Control. 1986.
[46] Arulampalam S M S, Gordon N, et al. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking.[C] IEEE Trans. on Signal Processing. 2000: 174~188
[47] Berzuini W R G a C. Following a moving target-Monte Carlo inference for dynamic Bayesian models [J]. J.R.Statist. Soc.B, 2001, 63:127~146.
[48] C.Andrien A D, W.Fitzgerald, and S.Godsill. An introduction to the theory and application of simulation based computational methods in Bayesian signal processing[C] in Proc Int. Conf. Acoust, Speech, Signal Process. 1998:
[49] Gordon N J S D J, Smith A F M. Novel approach to nonlinear/non-gaussian bayesian state estimation [C]. IEEE Proceedings on Radar and Signal Processing. 1993.
[50] Petar M.Djuric S M K. Batch and Sequential Importance Sampling Methods for Statistical Signal Processing [C]. ICASSP. 2002.
[51] Chang Cheng A R. Kernel particle filter for visual tracking [C]. IEEE Signal Processing Letters. 2005.
[52] Morales R P D. Real time monito ring of comp lexindust rialp rocesses with particle filters [R].Orlando: University of British Colunbia: 2003.
[53]王晨,房建成.基于Unscented四元数粒子滤波的微小卫星姿态估计[J].北京航空航天大学学报, 2007, 33 (5):552～556.
[54]姜雪原,马广富,胡庆雷.基于粒子滤波和UKF联合滤波的卫星姿态估计[J].哈尔滨工业大学学报, 2007, 39 (3):337～341.
[55]王建琦.基于姿态敏感器的卫星自主导航与融合定姿方法研究[D].哈尔滨:哈尔滨工业大学研究生院博士学位论文(2004).
[56] Chang F R C Y T, Jan Y W, etal. Data Fusion of Three Attitude Sensors [C]. Proceedings of the 40th SICE Annual Conference. 2001.
[57]王志胜.信息融合估计理论及其在航天器控制中的应用研究[D].西安:西北工业大学研究生院博士学位论文(2002).
[58]曹娟娟,房建成,盛蔚. GFMIMU/GPS组合导航系统信息融合技术研究[J].系统仿真学报, 2008, 20 (2):391～394.
[59]赖际舟,刘建业,刘瑞华,华冰.基于联邦滤波的惯性导航姿态组合算法[J].天津大学学报, 2006, 39 (3):349～353.
[60]郁丰,刘建业,熊智,李丹.微小卫星姿态确定系统多信息融合滤波技术[J].上海交通大学学报, 2006, 42 (5):831～835.
[61] Stephen M F Y P, Robert E Z. The Design and Test of a Compact Propulsion System for CanX Nanosatellite Formation Flying [C]. Proceedings of the 19th Annual AIAA/USU Conference on Small Satellite. Salt Lake City, Utah. USA, 2005.
[62] Schlutz J. Attitude and Orbit Control of Satellite using Thruster System [D]. The University of Sydney, Australian(2005.
[63] Bang H C E Y E, Lee H. Attitude Control of Bias Momentum Spacecraft by Multiple Pulse Inputs [C]. JSASS 17th International Sessions in 41st Aircraft Symposium. Nagano, Japan, 2003.
[64] Mark L P. Magnetic Torquer Attitude Control via Asymptotic Periodic Linear Quadratic Regulation [C]. American Institute of Aeronautics and Astronautics. 2000.
[65]马建伟,李银伢.满意PID控制设计理论与方法[M].北京:科学出版社,2007.
[66]王立新.模糊系统与模糊控制[M].北京:清华大学出版社,2003.
[67] Zhao Z Y T M, Isaka S,. Fuzzy Gain Scheduling of PID Controllers [J]. IEEE Tran. On System, Man Cybern, 1993, 23 (5):1392~1398.
[68] RamakrishnanS M L R. Attitude Control of a -Axis Stabilized Satellite Using Adaptive Control Algorithm [C]. Proceeding of the 2006 IEEE/SMC International Conference on System of Systems Engineering. Los Angeles, USA, 2006.
[69] Guan P L X D, Chen J B. Flexible Satellite Attitude Control Via Adaptive Fuzzy Linearization [J]. Journal of beijing Institute of technology, 2005, 14 (2):140~144.
[70] Terui F. Position and Attitude Control of a Spacecraft by Sliding Model Control [C]. Proceeding of the American Control Conference Philadelphia. Pennsylvania, 1998.
[71]胡剑波,庄开宇.高级变结构控制理论及应用[M].西安:西北工业大学出版社,2008.
[72] Utkin V I. Variable structure system with silding modes [C]. IEEE Trans on Auto Contr. 1977.
[73] Chiang C Y H M. Neural Fuzzy Approach to F/A-18 Aircraft Failure Isolation and Reconfiguration Design [C]. AIAA. 1995.
[74] Rovert F S. Toward Intelligent Flight Control [J]. IEEE Transaction on Systems Man Cyber, 1993, 23 (6):1699~1717.
[75]高为炳.变结构控制理论及设计方法[M].北京:科学出版社,1996.
[76]高为炳.变结构控制系统的品质控制[J].全国控制理论与应用西安年会, 1988:156～159.
[77] Venkataraman S T G S. Terminal sliding modes: a new approach nonlinear control synthesis [J]. 1991,
[78] Venkataraman S T G S. Control of nonlinear systems using Terminal sliding modes [J]. Transaction of the AMSE, 1991, 115 (9):554~564.
[79] Yu X M Z H. On finite time mechanisn: Terminal sliding modes [J]. IEEE workshop on variable structure systems, 1996:164~167.
[80] Yu X M Z H, Wu Y Q,. Terminal sliding modes with fast transient performance [C]. Proceedings of the 36th Conference on Decision and Control. San Diego, USA, 1997.
[81] Wu Y Q Y X, Man Z H. Terminal sliding mode control design for uncertain dynamic systems [J]. Systems and Control Letters, 1998, 34:281~287.
[82]佟绍成.非线性系统的自适应模糊控制[M].北京:科学出版社,2006.
[83]钱山.在轨服务航天器相对测量及姿态控制研究[D].长沙:国防科学
[84]陈新海,李言俊,周军.自适应控制及应用[M].山西:西北工业大学出版社,1998.
[85]林来兴.小卫星将引起卫星应用和卫星技术的重大变革[R].现代小卫星技术第一集团航天工业总公司小卫星论证组. 1995.
[86]马艳红,胡军.姿态四元数相关问题[J].空间控制技术与应用, 2008, 34 (3):55～60.
[87] Choukroun D B-I I Y, Oshman Y. A novel quaternion Kalman Filter [C]. AIAA Guidance, Navigation and Control Conference and Exhibit. Reston, USA, 2002.
[88]赵长山,秦永元,贾继超,郭林峰.修正型罗德里格参数姿态算法研究[J].测控技术, 2009, 28 (9):91～94.
[89]王勇军,秦永元,杨波.四元数、Rodrigues参数在卫星姿态解算上的对比研究[J].中国空间科学技术, 2007, 3 (6):18～23.
[90]张红梅,邓正隆,高玉凯. UKF在基于修正罗德里格参数的飞行器姿态确定中的应用[J].宇航学报, 2005, 26 (2)
[91] Hanspeter Schaub J J L. Steregraphic Parameters for Attitude Dynamics: A Generalization of the Rodrigues Parameters [J]. The Journal of the Astronautical Sciences, 1996, 44 (1):1~19.
[92]秦永元.惯性导航[M].北京:科学出版社,2006.
[93]黄福铭.航天器飞行控制与仿真[M].北京:国防工业出版社,2004.
[94]周江华,苗育红,王明海.姿态运动的Rodrigues参数描述[J].宇航学报, 2004, 25 (5):514～519.
[95] Stain Sondersrod Ose. Attitude determination for the Norwegian student satellite nCube [D]. Trondheim Norway:Norwegian University of Science and Technology(2004.
[96] Kristian Svartveit. Attitude determination of the NCUBE satellite [D]. Trondheim Norway:Norwegian University of Science and Technology(2003.
[97] James R W W J L王张陈.航天任务的分析与设计[M].北京:航空工业出版社,1992.
[98]黄本城.空间环境工程学[M].北京:宇航出版社,1993.
[99] Challa M S N G A, Baker D F, etal. Advantages of estimating rate corrections during dynamic propagation of spacecraft rates-applications to real-time attitude determination of SAMPEX [C]. Proceedings of the Flight Mechanics/Estimation Theory Symposium. 1994.
[100]姜雪原,马广富,石忠.基于TAM的卫星角速度粒子滤波估计[J].哈尔滨工业大学学报, 2007, 39 (11):1681～1685.
[101]陈记争,袁建平,方群.基于修正Rodrigues参数和UKF的姿态估计算法[J].宇航学报, 2008, 29 (5):1622～1626.
[102] Carlson N A. Federated Filter for Fault-Tolerant Integrated Navigation systems [C]. IEEE Proceedings of Position Location and Navigation Symposium. 1988.
[103] A C N. Information-Sharing Approach to Federated Kalman Filtering [C]. IEEE Proceeding of Aerospace and Electronics Conference. 1988.
[104]郁丰,.微小卫星姿轨自主确定技术研究[D].南京:南京航空航天大学研究生院博士学位论文(2008).
[105]张红梅,.非线性滤波及其在姿态确定和导航中的应用[D].哈尔滨:哈尔滨工业大学研究生院博士学位论文(2004).
[106]胡士强,敬忠良.粒子滤波算法综述[J].控制与决策, 2005, 20 (4):361～365.
[107] Doucet A G S. On sequential Monte Carlo sampling methods for Bayesian filtering [D]. Cambridge:University of Cambridge(1998.
[108]同济大学应用数学系.高等数学[M].北京:高等教育出版社,1993.
[109]武元新.对偶四元数导航算法与非线性高斯滤波研究[D].长沙:国防科学技术大学研究生院博士学位论文(2005).
[110] GAO Xian-zhong H Z-x, REN Bo-tao. Extension Particle Filtering Algorithm for State and Parameter Estimation in Dynamic Control Process [C]. Control and Decision Conference. 2009.
[111] T.Chen J M, E.Martin. Particle filters for state and parameter estimation in batch processes [J]. Journal of Process Control, 2005, 6 (2):215~220.
[112]管萍,刘小河,刘向杰.挠性卫星的变结构姿态控制[J].控制理论与应用, 2007, 24 (3):480～484.
[113]田春华.卫星姿态控制系统的分析、设计和仿真研究[D].哈尔滨:哈尔滨工业大学研究生院博士学位论文(2000).
[114]李鹏奎.在轨服务组合平台姿态确定与控制研究及地面试验相对测量系统设计[D].长沙:国防科学技术大学研究生院硕士学位论文(2008).
[115] Kumar G a K D. Fault Tolerant Reconfigurable Satellite Formations Using Adaptive Variable Structure Techniques [J]. Journal of Guidance, Control and Dynamics, 2010, 33 (3):153~168.
[116]方勇纯,卢桂章.非线性系统理论[M].北京:清华大学出版社,2009.
[117]王青,景韶光,张明廉.推力矢量空空导弹的变结构自适应控制[J].航空学报, 2000, 21 (4):352～354.
[118]孔庆松.低轨小卫星高性能磁力矩器的设计与实现.2006.6
[119] J.S.White, F.H.Shigemoto, and K.Bourquin. Satellite Attitude Control Utilizing the Earth's MagneticField [J]. Technical Report NASA TN-DI068, National Aeronuatics and Space Adminisrtation, 1961.8.
[120] F. Mesch. Magnetic Components for the Attitude Control of Space Vehicles. IEEE Transactions on Magnetics. 1969.9 Vol. MAG-5. NO. 3. 586~592.
[2]余金培,杨根庆,梁旭文.现代小卫星技术与应用[M].上海:上海科学普及出版社,2004.
[3]黄玔圭.航天器姿态动力学[M].长沙:国防科技大学出版社,1997.
[4]李琳琳.卫星自主轨道确定及姿态确定技术研究[R].北京: 2003.
[5] Wright P S W H S. An Overview of Sensors in Spacecraft Engineering [C]. IEEE Colloquium on Satellite Instrumenation. 1988(1).
[6]林玉荣.基于星敏感器确定卫星姿态的滤波算法研究[D].哈尔滨:哈尔滨工业大学研究生院博士学位论文(2002).
[7]钱勇.高精度三轴稳定卫星姿态确定和控制系统研究[D].西安:西安工业大学研究生院博士学位论文(2002).
[8]李立宏.适用于星跟踪器的星图识别算法及飞行器姿态估计研究[D].哈尔滨:哈尔滨工业大学研究生院博士学位论文(2000.
[9] Hsing J C R A, Barrentt M F. Gyro-Based Attitude Reference Systems for Communications Satellite [J]. Journal of Guidance and Control, 1980, (3(3)):239~244.
[10] Reid D B. Description of the Milstar Attitude Determination System[C] IEEE Control Conference. The American: 1997: 2313~2322
[11] Farrenkopf R L. Generalized Results for Precision Attitude reference Systems Using Gyros [C]. AIAA-74-0903. 1974.
[12] Wahba G. A Least Squares Estimate of Spacecraft Attitude [J]. SIAM Review, 1965, (7(3)):409~415.
[13] Shuster M D O S D. Three-Axis Attitude Determination from Vector Observations [C]. AIAA-81-4003. 1981.
[14] Bar-Itzhack I Y R H. Optimized TRIAD Algorithm for Attitude Determination [C]. AIAA/AAS Astrodynamics Conference. San Diego, CA, AIAA-1996-3616, 1996.
[15] Choukroun D B-I I Y, Oshman Y. Optimal Request Algorithm for Attitude Determination [C]. AIAA Guidance, Navigation and Control Conference and Exhibit. Montreal Canada,AIAA 2001-4153, 2001.
[16] Bar-Itzhack I Y. "Request"-A Recursive Quest Algorithm for Sequential Attitude Determination [C]. AIAA-1996-3616, 1996.
[17] Markley F L. Attitude Determination Using Vector Observation: A FastOptimal Matrix Algorthm [J]. Journal of the Astronautical Sciences, 1993, 41(2):261~280.
[18] Mortari D. Euler-q Algorithm for Attitude Determination from Vector Observations [J]. Journal of Guidance, Control, and Dynamics, 1998, 21(2):328~334.
[19]杨艳,赵黎平,赵光恒.基于磁强计测量的一种姿态估计方法研究[J].航天控制, 2006, 24 (4):51～56.
[20]张锐,朱振才,张静等.基于磁强计的卫星卫星姿态确定[J].宇航学报, 2006, 27 (4):578～581.
[21]田菁,吴美平,胡小平.三轴磁强计姿态确定[J].国防科技大学学报, 2001, 23 (5):17～22.
[22]刘建业,段方,李丹,郁丰.基于太阳电池板与磁强计的小卫星姿态确定算法研究[J].宇航学报, 2007, 28 (1):218～222.
[23]程杨,杨涤,崔祜涛.利用修正罗德里格参数进行飞行器姿态估计[J].飞行力学, 2002, 20 (4):18～21.
[24] Hyo-Sung A S-H L. Gyroless attitude estimation of sun-point-ing satellite using magnetometers [J]. Geoscience and Remote Sensiong Letters,IEEE, 2005, 2 (1):8~12.
[25]张金槐,蔡洪.飞行器试验统计学[M].长沙:国防科大大学出版社,1995.
[26] Kadirkamanathan V L P, Kirubarajan T. Sequential Monte Carlo Filtering vs. the IMM Estimator for Fault Detection and Isolation in Nonlinear Systems [C]. SPIE. 2001.
[27]屠善澄.卫星姿态动力学与控制[M].北京:宇航出版社,1998.
[28]章仁为.卫星轨道姿态动力学与控制[M].北京:北京航空航天大学出版社,1998.
[29]廖晖.对地定向三轴稳定卫星姿态确定和控制系统研究[D].西安:西北工业大学研究生院博士学位论文(2000).
[30]林玉荣,邓正隆.一类非线性预测滤波器的鲁棒稳定性分析[J].电机与控制学报, 2006, 10 (1):97～101.
[31]廖晖,周凤岐,周军.利用预测滤波法估计小卫星姿态角速度[J].西北工业大学学报, 2001, 19 (1):84～87.
[32] Crassidis J L M F L. Predictive Filtering for Attitude Estimation without Rate Sensor [J]. Journal of Guidance, Control, and Dynamics, 1997, 20 (3):522~527.
[33] Crassidis J L M F L. Predictive Filtering for Nonlinear Systems [J]. Journal of Guidance, Control, and Dynamics, 1997, 20 (3):566~572.
[34]郁丰,刘建业,熊智.预测滤波算法在微小卫星姿态确定中的应用[J].宇航学报, 2008, 29 (1):110～114.
[35]林玉荣,邓正隆.基于模型误差确定卫星姿态的预测滤波算法[J].宇航学报, 2001, 22 (1):79～84.
[36]林玉荣,邓正隆.基于MME准则估计卫星姿态的非线性滤波算法[J].哈尔滨工业大学学报, 2002, 34 (1):14～18.
[37] Mook D J J J L. Minimum Model Error Estimation for Poorly Modeled Dynamic System [J]. Journal of Guidance, Control, and Dynamics, 1988, 11 (3):256~261.
[38] Lu P. Nonlinear Predictive Controllers for Continuous Systems [J]. Journal of Guidance, Control, and Dynamics, 1994, 17 (3):553~560.
[39]张红梅,邓正隆,林玉荣.一种基于模型误差预测的UKF方法[J].航空学报, 2004, 25 (6):598～601.
[40]朱长征.基于星敏感器的星模式识别算法及空间飞行器姿态确定技术研究[D].长沙:2004.
[41]潘泉,杨小军,张洪才.粒子滤波器:进展与展望[J].控制理论与应用, 2006, 23 (2)
[42]梁彦,潘泉,杨峰,张磊.复杂系统的现代估计理论及应用[M].背景:科学出版社,2009.
[43] Blom H A P B E A. Joint IMMPDA particle filter [C]. Proceeding of Sixth International Conference of Information Fusion. 2003.
[44] Boers Y D J N. Interacting multiple model particle filter[C] IEEE Proceedings on Radar, Sonar and Navigation. 2003: 344~349
[45] Daum F E. Exact finite dimensional nonlinear filters [C]. IEEE Trans. on Automatic Control. 1986.
[46] Arulampalam S M S, Gordon N, et al. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking.[C] IEEE Trans. on Signal Processing. 2000: 174~188
[47] Berzuini W R G a C. Following a moving target-Monte Carlo inference for dynamic Bayesian models [J]. J.R.Statist. Soc.B, 2001, 63:127~146.
[48] C.Andrien A D, W.Fitzgerald, and S.Godsill. An introduction to the theory and application of simulation based computational methods in Bayesian signal processing[C] in Proc Int. Conf. Acoust, Speech, Signal Process. 1998:
[49] Gordon N J S D J, Smith A F M. Novel approach to nonlinear/non-gaussian bayesian state estimation [C]. IEEE Proceedings on Radar and Signal Processing. 1993.
[50] Petar M.Djuric S M K. Batch and Sequential Importance Sampling Methods for Statistical Signal Processing [C]. ICASSP. 2002.
[51] Chang Cheng A R. Kernel particle filter for visual tracking [C]. IEEE Signal Processing Letters. 2005.
[52] Morales R P D. Real time monito ring of comp lexindust rialp rocesses with particle filters [R].Orlando: University of British Colunbia: 2003.
[53]王晨,房建成.基于Unscented四元数粒子滤波的微小卫星姿态估计[J].北京航空航天大学学报, 2007, 33 (5):552～556.
[54]姜雪原,马广富,胡庆雷.基于粒子滤波和UKF联合滤波的卫星姿态估计[J].哈尔滨工业大学学报, 2007, 39 (3):337～341.
[55]王建琦.基于姿态敏感器的卫星自主导航与融合定姿方法研究[D].哈尔滨:哈尔滨工业大学研究生院博士学位论文(2004).
[56] Chang F R C Y T, Jan Y W, etal. Data Fusion of Three Attitude Sensors [C]. Proceedings of the 40th SICE Annual Conference. 2001.
[57]王志胜.信息融合估计理论及其在航天器控制中的应用研究[D].西安:西北工业大学研究生院博士学位论文(2002).
[58]曹娟娟,房建成,盛蔚. GFMIMU/GPS组合导航系统信息融合技术研究[J].系统仿真学报, 2008, 20 (2):391～394.
[59]赖际舟,刘建业,刘瑞华,华冰.基于联邦滤波的惯性导航姿态组合算法[J].天津大学学报, 2006, 39 (3):349～353.
[60]郁丰,刘建业,熊智,李丹.微小卫星姿态确定系统多信息融合滤波技术[J].上海交通大学学报, 2006, 42 (5):831～835.
[61] Stephen M F Y P, Robert E Z. The Design and Test of a Compact Propulsion System for CanX Nanosatellite Formation Flying [C]. Proceedings of the 19th Annual AIAA/USU Conference on Small Satellite. Salt Lake City, Utah. USA, 2005.
[62] Schlutz J. Attitude and Orbit Control of Satellite using Thruster System [D]. The University of Sydney, Australian(2005.
[63] Bang H C E Y E, Lee H. Attitude Control of Bias Momentum Spacecraft by Multiple Pulse Inputs [C]. JSASS 17th International Sessions in 41st Aircraft Symposium. Nagano, Japan, 2003.
[64] Mark L P. Magnetic Torquer Attitude Control via Asymptotic Periodic Linear Quadratic Regulation [C]. American Institute of Aeronautics and Astronautics. 2000.
[65]马建伟,李银伢.满意PID控制设计理论与方法[M].北京:科学出版社,2007.
[66]王立新.模糊系统与模糊控制[M].北京:清华大学出版社,2003.
[67] Zhao Z Y T M, Isaka S,. Fuzzy Gain Scheduling of PID Controllers [J]. IEEE Tran. On System, Man Cybern, 1993, 23 (5):1392~1398.
[68] RamakrishnanS M L R. Attitude Control of a -Axis Stabilized Satellite Using Adaptive Control Algorithm [C]. Proceeding of the 2006 IEEE/SMC International Conference on System of Systems Engineering. Los Angeles, USA, 2006.
[69] Guan P L X D, Chen J B. Flexible Satellite Attitude Control Via Adaptive Fuzzy Linearization [J]. Journal of beijing Institute of technology, 2005, 14 (2):140~144.
[70] Terui F. Position and Attitude Control of a Spacecraft by Sliding Model Control [C]. Proceeding of the American Control Conference Philadelphia. Pennsylvania, 1998.
[71]胡剑波,庄开宇.高级变结构控制理论及应用[M].西安:西北工业大学出版社,2008.
[72] Utkin V I. Variable structure system with silding modes [C]. IEEE Trans on Auto Contr. 1977.
[73] Chiang C Y H M. Neural Fuzzy Approach to F/A-18 Aircraft Failure Isolation and Reconfiguration Design [C]. AIAA. 1995.
[74] Rovert F S. Toward Intelligent Flight Control [J]. IEEE Transaction on Systems Man Cyber, 1993, 23 (6):1699~1717.
[75]高为炳.变结构控制理论及设计方法[M].北京:科学出版社,1996.
[76]高为炳.变结构控制系统的品质控制[J].全国控制理论与应用西安年会, 1988:156～159.
[77] Venkataraman S T G S. Terminal sliding modes: a new approach nonlinear control synthesis [J]. 1991,
[78] Venkataraman S T G S. Control of nonlinear systems using Terminal sliding modes [J]. Transaction of the AMSE, 1991, 115 (9):554~564.
[79] Yu X M Z H. On finite time mechanisn: Terminal sliding modes [J]. IEEE workshop on variable structure systems, 1996:164~167.
[80] Yu X M Z H, Wu Y Q,. Terminal sliding modes with fast transient performance [C]. Proceedings of the 36th Conference on Decision and Control. San Diego, USA, 1997.
[81] Wu Y Q Y X, Man Z H. Terminal sliding mode control design for uncertain dynamic systems [J]. Systems and Control Letters, 1998, 34:281~287.
[82]佟绍成.非线性系统的自适应模糊控制[M].北京:科学出版社,2006.
[83]钱山.在轨服务航天器相对测量及姿态控制研究[D].长沙:国防科学
[84]陈新海,李言俊,周军.自适应控制及应用[M].山西:西北工业大学出版社,1998.
[85]林来兴.小卫星将引起卫星应用和卫星技术的重大变革[R].现代小卫星技术第一集团航天工业总公司小卫星论证组. 1995.
[86]马艳红,胡军.姿态四元数相关问题[J].空间控制技术与应用, 2008, 34 (3):55～60.
[87] Choukroun D B-I I Y, Oshman Y. A novel quaternion Kalman Filter [C]. AIAA Guidance, Navigation and Control Conference and Exhibit. Reston, USA, 2002.
[88]赵长山,秦永元,贾继超,郭林峰.修正型罗德里格参数姿态算法研究[J].测控技术, 2009, 28 (9):91～94.
[89]王勇军,秦永元,杨波.四元数、Rodrigues参数在卫星姿态解算上的对比研究[J].中国空间科学技术, 2007, 3 (6):18～23.
[90]张红梅,邓正隆,高玉凯. UKF在基于修正罗德里格参数的飞行器姿态确定中的应用[J].宇航学报, 2005, 26 (2)
[91] Hanspeter Schaub J J L. Steregraphic Parameters for Attitude Dynamics: A Generalization of the Rodrigues Parameters [J]. The Journal of the Astronautical Sciences, 1996, 44 (1):1~19.
[92]秦永元.惯性导航[M].北京:科学出版社,2006.
[93]黄福铭.航天器飞行控制与仿真[M].北京:国防工业出版社,2004.
[94]周江华,苗育红,王明海.姿态运动的Rodrigues参数描述[J].宇航学报, 2004, 25 (5):514～519.
[95] Stain Sondersrod Ose. Attitude determination for the Norwegian student satellite nCube [D]. Trondheim Norway:Norwegian University of Science and Technology(2004.
[96] Kristian Svartveit. Attitude determination of the NCUBE satellite [D]. Trondheim Norway:Norwegian University of Science and Technology(2003.
[97] James R W W J L王张陈.航天任务的分析与设计[M].北京:航空工业出版社,1992.
[98]黄本城.空间环境工程学[M].北京:宇航出版社,1993.
[99] Challa M S N G A, Baker D F, etal. Advantages of estimating rate corrections during dynamic propagation of spacecraft rates-applications to real-time attitude determination of SAMPEX [C]. Proceedings of the Flight Mechanics/Estimation Theory Symposium. 1994.
[100]姜雪原,马广富,石忠.基于TAM的卫星角速度粒子滤波估计[J].哈尔滨工业大学学报, 2007, 39 (11):1681～1685.
[101]陈记争,袁建平,方群.基于修正Rodrigues参数和UKF的姿态估计算法[J].宇航学报, 2008, 29 (5):1622～1626.
[102] Carlson N A. Federated Filter for Fault-Tolerant Integrated Navigation systems [C]. IEEE Proceedings of Position Location and Navigation Symposium. 1988.
[103] A C N. Information-Sharing Approach to Federated Kalman Filtering [C]. IEEE Proceeding of Aerospace and Electronics Conference. 1988.
[104]郁丰,.微小卫星姿轨自主确定技术研究[D].南京:南京航空航天大学研究生院博士学位论文(2008).
[105]张红梅,.非线性滤波及其在姿态确定和导航中的应用[D].哈尔滨:哈尔滨工业大学研究生院博士学位论文(2004).
[106]胡士强,敬忠良.粒子滤波算法综述[J].控制与决策, 2005, 20 (4):361～365.
[107] Doucet A G S. On sequential Monte Carlo sampling methods for Bayesian filtering [D]. Cambridge:University of Cambridge(1998.
[108]同济大学应用数学系.高等数学[M].北京:高等教育出版社,1993.
[109]武元新.对偶四元数导航算法与非线性高斯滤波研究[D].长沙:国防科学技术大学研究生院博士学位论文(2005).
[110] GAO Xian-zhong H Z-x, REN Bo-tao. Extension Particle Filtering Algorithm for State and Parameter Estimation in Dynamic Control Process [C]. Control and Decision Conference. 2009.
[111] T.Chen J M, E.Martin. Particle filters for state and parameter estimation in batch processes [J]. Journal of Process Control, 2005, 6 (2):215~220.
[112]管萍,刘小河,刘向杰.挠性卫星的变结构姿态控制[J].控制理论与应用, 2007, 24 (3):480～484.
[113]田春华.卫星姿态控制系统的分析、设计和仿真研究[D].哈尔滨:哈尔滨工业大学研究生院博士学位论文(2000).
[114]李鹏奎.在轨服务组合平台姿态确定与控制研究及地面试验相对测量系统设计[D].长沙:国防科学技术大学研究生院硕士学位论文(2008).
[115] Kumar G a K D. Fault Tolerant Reconfigurable Satellite Formations Using Adaptive Variable Structure Techniques [J]. Journal of Guidance, Control and Dynamics, 2010, 33 (3):153~168.
[116]方勇纯,卢桂章.非线性系统理论[M].北京:清华大学出版社,2009.
[117]王青,景韶光,张明廉.推力矢量空空导弹的变结构自适应控制[J].航空学报, 2000, 21 (4):352～354.
[118]孔庆松.低轨小卫星高性能磁力矩器的设计与实现.2006.6
[119] J.S.White, F.H.Shigemoto, and K.Bourquin. Satellite Attitude Control Utilizing the Earth's MagneticField [J]. Technical Report NASA TN-DI068, National Aeronuatics and Space Adminisrtation, 1961.8.
[120] F. Mesch. Magnetic Components for the Attitude Control of Space Vehicles. IEEE Transactions on Magnetics. 1969.9 Vol. MAG-5. NO. 3. 586~592.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |