Joint State and Parameter Estimation for Hypersonic Glide Vehicles Based on Moving Horizon Estimation via Carleman Linearization
Aimed at joint state and parameter estimation problems in hypersonic glide vehicle defense, a novel moving horizon estimation algorithm via Carleman linearization is developed in this paper. First, the maneuver characteristic parameters that reflect the target maneuver law are extended into the stat...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/9/4/217 |
| _version_ | 1797437457395875840 |
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| author | Yudong Hu Changsheng Gao Wuxing Jing |
| author_facet | Yudong Hu Changsheng Gao Wuxing Jing |
| author_sort | Yudong Hu |
| collection | DOAJ |
| description | Aimed at joint state and parameter estimation problems in hypersonic glide vehicle defense, a novel moving horizon estimation algorithm via Carleman linearization is developed in this paper. First, the maneuver characteristic parameters that reflect the target maneuver law are extended into the state vector, and a dynamic tracking model applicable to various hypersonic glide vehicles is constructed. To improve the estimation accuracy, constraints such as path and parameter change amplitude constraints in flight are taken into account, and the estimation problem is transformed into a nonlinear constrained optimal estimation problem. Then, to solve the problem of high time cost for solving a nonlinear constrained optimal estimation problem, in the framework of moving horizon estimation, nonlinear constrained optimization problems are transformed into bilinear constrained optimization problems by linearizing the nonlinear system via Carleman linearization. For ensuring the consistency of the linearized system with the original nonlinear system, the linearized model is continuously updated as the window slides forward. Moreover, a CKF-based arrival cost update algorithm is also provided to improve the estimation accuracy. Simulation results demonstrate that the proposed joint state and parameter estimation algorithm greatly improves the estimation accuracy while reducing the time cost significantly. |
| first_indexed | 2024-03-09T11:19:32Z |
| format | Article |
| id | doaj.art-c73f82176c924c248193477a3e7fdcd2 |
| institution | Directory Open Access Journal |
| issn | 2226-4310 |
| language | English |
| last_indexed | 2024-03-09T11:19:32Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj.art-c73f82176c924c248193477a3e7fdcd22023-12-01T00:22:27ZengMDPI AGAerospace2226-43102022-04-019421710.3390/aerospace9040217Joint State and Parameter Estimation for Hypersonic Glide Vehicles Based on Moving Horizon Estimation via Carleman LinearizationYudong Hu0Changsheng Gao1Wuxing Jing2School of Astronautics, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Astronautics, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Astronautics, Harbin Institute of Technology, Harbin 150001, ChinaAimed at joint state and parameter estimation problems in hypersonic glide vehicle defense, a novel moving horizon estimation algorithm via Carleman linearization is developed in this paper. First, the maneuver characteristic parameters that reflect the target maneuver law are extended into the state vector, and a dynamic tracking model applicable to various hypersonic glide vehicles is constructed. To improve the estimation accuracy, constraints such as path and parameter change amplitude constraints in flight are taken into account, and the estimation problem is transformed into a nonlinear constrained optimal estimation problem. Then, to solve the problem of high time cost for solving a nonlinear constrained optimal estimation problem, in the framework of moving horizon estimation, nonlinear constrained optimization problems are transformed into bilinear constrained optimization problems by linearizing the nonlinear system via Carleman linearization. For ensuring the consistency of the linearized system with the original nonlinear system, the linearized model is continuously updated as the window slides forward. Moreover, a CKF-based arrival cost update algorithm is also provided to improve the estimation accuracy. Simulation results demonstrate that the proposed joint state and parameter estimation algorithm greatly improves the estimation accuracy while reducing the time cost significantly.https://www.mdpi.com/2226-4310/9/4/217hypersonic glide vehiclesjoint state and parameter estimationmoving horizon estimationCarleman linearizationconstrained optimization probleminequality constraints |
| spellingShingle | Yudong Hu Changsheng Gao Wuxing Jing Joint State and Parameter Estimation for Hypersonic Glide Vehicles Based on Moving Horizon Estimation via Carleman Linearization Aerospace hypersonic glide vehicles joint state and parameter estimation moving horizon estimation Carleman linearization constrained optimization problem inequality constraints |
| title | Joint State and Parameter Estimation for Hypersonic Glide Vehicles Based on Moving Horizon Estimation via Carleman Linearization |
| title_full | Joint State and Parameter Estimation for Hypersonic Glide Vehicles Based on Moving Horizon Estimation via Carleman Linearization |
| title_fullStr | Joint State and Parameter Estimation for Hypersonic Glide Vehicles Based on Moving Horizon Estimation via Carleman Linearization |
| title_full_unstemmed | Joint State and Parameter Estimation for Hypersonic Glide Vehicles Based on Moving Horizon Estimation via Carleman Linearization |
| title_short | Joint State and Parameter Estimation for Hypersonic Glide Vehicles Based on Moving Horizon Estimation via Carleman Linearization |
| title_sort | joint state and parameter estimation for hypersonic glide vehicles based on moving horizon estimation via carleman linearization |
| topic | hypersonic glide vehicles joint state and parameter estimation moving horizon estimation Carleman linearization constrained optimization problem inequality constraints |
| url | https://www.mdpi.com/2226-4310/9/4/217 |
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