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Research on Filtering in Rendezvous and Docking

Author: LiPeng
Tutor: ChenXingLin;SongShenMin
School: Harbin Institute of Technology
Course: Control Science and Engineering
Keywords: Gaussian process regression risk sensitive filter adaptive particle filter fuzzy Kalman filter fuzzy logic observer physical demonstration platform
CLC: TN713
Type: PhD thesis
Year: 2010
Downloads: 94
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Abstract


With the development of space technology, especially the development of manned space technology, Space rendezvous and docking technology has already gong through the stages of exploration research and experiment, has gradually matured and been applied to actual space activities. Now, rendezvous and docking operation has become a space routine, which has been improved from relying on the joint manual operation of ground stations and astronauts to automatic control, and the autonomy of satellite has been consistently heightened. Mainly affected by on-board sensors, the measurement accuracy of relative status of principal and subordinate satellite will determine the degree of autonomy and technical level, so it is the key factors to ensure the success of rendezvous and docking. Under the background of obtaining the relative status information in rendezvous and docking, this thesis studies state estimation of homing stage, state estimation of fly-around stage, attitude determination of multi-sensor combination platform, the calibration of high precision IMU, and ground demonstration of docking and separation. The main contents are listed as follows.An algorithm of state estimation of homing stage of rendezvous and docking is studied and proposed that is based on square root unscented Kalman filter incorporating Gaussian process regression. The inaccuracy of system model, that is model error, is one of the main reasons for filter divergence. There are three main error sources in system model: mathematical model does not meet the actual physical process, linearization and dimension reduction are inappropriate, and statistical parameters selection of process noise and measurement noise are unreasonable. In order to solve these problems, square root unscented Kalman filter combining with Gaussian process regression is proposed, which deals with the uncertainty of system models and can adapt covariance of measurement noise. The new algorithm is composed of learning stage and estimation stage. In the first stage, Gaussian process regression is applied to learn the training data, regression models and noise covariance of the dynamic system are obtained. In the second stage, state equations and observation equations are substituted by their regression models respectively, while real-time noise covariance is adjusted adaptively by Gaussian kernel function. Finally, this algorithm is applied in state estimation of homing stage, and simulation results show the new algorithm is effective.State estimation of fly-around stage based on adaptive sensitive particle filter is designed. Although the resampling step reduces the effects of the degeneracy problem, it introduces other practical problems. A common one is the sample impoverishment phenomenon, where after a few iterations, the particles which have high weighs are statistically selected many times, leads to a loss of diversity among the particles, so the posterior distribution couldn’t be approximated by the particles. According the theory that regularized method could increase diversity of particles, result in inhibition against sample impoverishment, the risk sensitive particle filter is designed by combining risk sensitive filter with particle filter. Compared with the other methods dealing with impoverishment phenomenon, this algorithm is effective and easy to be implemented. Meanwhile according to relative entropy theory and the thought of particle-number controller, the adaptive particle filter is designed to change the particle number of sample process, so the efficiency of algorithm is improved. Finally the adaptive sensitive particle filter is applied in state estimation of fly-around stage, through compared with particle filter and unscented particle filter, the efficiency of new algorithm is verified.Attitude determination of multi-sesors combinated platform is studied and proposed that is based on hybrid Kalman filter-fuzzy logic inference architecture. In order to determine the attitude of satellite combination platform after rendezvous and docking, different attitude sensors are combined. Their complementary properties could increase the accuracy and reliability of attitude determination system. The Kalman filter, which is applied in federal filter as a sub-filter, is substituted by fuzzy Kalman filter. The new design could track the varied statistic of sub-system measurement noise, which is just the shortage of federal Kalman filter whose performance decrease in these cases because of missing these changes. The reliability function is used to compute the confidence of sub-filters. This new algorithm could overcome the global output deterioration which comes from the sub-system with low confidence or sub-system failure.In order to provide the physical platform of ground test and verification for space docking and separation control, a three degree of freedom ground-based physical simulator is developed. After the test system components and simplified model being described, relative motion equation of test simultor was deduced. The combined measurement method, which is composed of MIMU and vision measurement unit, is designed to compensate the mesurement for the biase of MIMU. According to the hybird architecture of fuzzy logic inference and Kalman filter, the measure system based on information fusion algorithm is designed to calculate the navigation datas. Finally, through all the verify such as the static test experiment, attitude maneuver test, location maneuver test, and the whole process of rendezvous and docking, the simulation results show that navigation accuracy of the measurement system is effective.

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CLC: > Industrial Technology > Radio electronics, telecommunications technology > Basic electronic circuits > Filtering techniques,the filter
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