1. A Novel Robust Sparse-Grid Quadrature Kalman Filter Design for HCV Transfer Alignment Against Model Parameter Uncertainty
- Author
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Jianjuan Liu and Hongmei Chen
- Subjects
Lyapunov function ,0209 industrial biotechnology ,Computer science ,Sparse grid ,Stability (learning theory) ,020206 networking & telecommunications ,Ocean Engineering ,02 engineering and technology ,Kalman filter ,Oceanography ,Quadrature (mathematics) ,symbols.namesake ,Extended Kalman filter ,020901 industrial engineering & automation ,Robustness (computer science) ,Control theory ,0202 electrical engineering, electronic engineering, information engineering ,symbols ,Transfer alignment - Abstract
A novel robust scheme for Transfer Alignment (TA) is proposed for improving the accuracy of the navigation of a Hypersonic Cruise Vehicle (HCV). The main goal is to instil robustness in the safety and accuracy of the attitude determination, despite mode uncertainties. This article focuses on Robust Sparse-Grid Quadrature Filtering (R-SGQF) using two given robust factors for norm-bounded model uncertainties in non-linear systems. Missile dynamic and measurement model uncertainties are established to validate TA technologies. The nominal stability of the R-SGQF is defined by estimating error dynamics. The technique gives sufficient conditions for the R-SGQF in terms of two parameterised Riccati equations. Robust stability is analysed using Lyapunov theory and the accuracy level of the Sparse-Grid Quadrature (SGQ) algorithm. Embedding the SGQ technique into the robust filter structure, R-SGQF is not only of robust stability against uncertainty but also of higher accuracy. The simulation results of the TA algorithm demonstrate that attitude determinations validate the effectiveness of the R-SGQF algorithm.
- Published
- 2017
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