Abstract

This article proposes a new three-dimensional (3D) guidance algorithm for a two-stage solid rocket-powered launch vehicle in the ascent phase. The method consists of a longitudinal and lateral cooperative online trajectory generation method and a model predictive control (MPC) based tracking method. First, we introduced a new variable, named lateral displacement distance, to describe lateral motion. After decoupling the 3D dynamics into the longitudinal and lateral movements, the lateral distance was calculated by minimum order polynomials, as well as the altitude in the longitudinal plane. The multiple constraints were satisfied accordingly. This new representation transforms the online trajectory generation problem into a single-parameter root-finding problem, which is solved online by Newton’s method. Second, the traditional energy management method, which usually works in the last phase of multistage solid rockets, was extended to all stages. A proportion was put forward to allocate the energy management demand to the two stages of solid rockets. This strategy can enlarge the energy management capacity of solid rockets. Third, the MPC method was adopted to obtain the tracking guidance law to lessen the effect of uncertain parameters. The subsequent stability analysis shows that the MPC-based tracking controller is closed-loop stable and can handle the constraints and parameter uncertainties simultaneously. Finally, the effectiveness and robustness were verified by validating a two-stage solid rocket’s ascent phase flight under aerodynamic uncertainties and initial state deviations. In comparison to the existing energy management method, the incorporation of the sideslip angle design in this study renders solid rockets more capable at energy management. The proposed algorithm can accurately steer the rocket vehicle to the transition window.

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Data Availability Statement

Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.

Acknowledgments

This work is supported by the National Natural Science Foundation of China (Nos. 61903146 and 61873319).

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Go to Journal of Aerospace Engineering
Journal of Aerospace Engineering
Volume 37Issue 1January 2024

History

Received: Jan 17, 2023
Accepted: Jun 16, 2023
Published online: Sep 20, 2023
Published in print: Jan 1, 2024
Discussion open until: Feb 20, 2024

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Ph.D. Student, National Key Laboratory of Science and Technology on Multispectral Information Processing, School of Artificial Intelligence and Automation, Huazhong Univ. of Science and Technology, Wuhan 430074, China. ORCID: https://orcid.org/0000-0003-3656-0002. Email: [email protected]
Associate Professor, National Key Laboratory of Science and Technology on Multispectral Information Processing, School of Artificial Intelligence and Automation, Huazhong Univ. of Science and Technology, Wuhan 430074, China (corresponding author). ORCID: https://orcid.org/0000-0002-8588-4284. Email: [email protected]
Dept. of Computing Science, Univ. of Aberdeen, Aberdeen AB24 3UE, UK. ORCID: https://orcid.org/0000-0002-3121-7208. Email: [email protected]
Zhongtao Cheng [email protected]
Lecturer, School of Aerospace Engineering, Huazhong Univ. of Science and Technology, Wuhan 430074, China. Email: [email protected]
Professor, National Key Laboratory of Science and Technology on Multispectral Information Processing, School of Artificial Intelligence and Automation, Huazhong Univ. of Science and Technology, Wuhan 430074, China. Email: [email protected]
Professor, National Key Laboratory of Science and Technology on Multispectral Information Processing, School of Artificial Intelligence and Automation, Huazhong Univ. of Science and Technology, Wuhan 430074, China. ORCID: https://orcid.org/0000-0002-5978-8459. Email: [email protected]

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