TY - GEN
T1 - Optimal Parameter Estimation-Assisted Event-Triggered Model Predictive Guidance for Launch Vehicle
AU - Zhang, Tengfei
AU - Su, Hua
AU - Liu, Songyu
AU - Gong, Chunlin
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - Nonlinear Model Predictive Guidance (NMPG) based on optimal control (trajectory optimization) is an extremely promising method for achieving optimal and precise guidance. Event-triggered NMPG offers a dynamic cycle strategy where updates to the optimal guidance law are made only when the deviation between the actual state and the optimal reference trajectory exceeds a set threshold, thus conserving computational resources effectively. To further minimize event-triggering occurrences, we propose embedding an optimal parameter estimation step into it. With increasing sampled flight-state data over time, the motion model for NMPG progressively converges towards the actual one, thereby yielding more precise open-loop guidance laws. The simulation results of an ascent guidance problem show that the proposed method has higher guidance accuracy, a shorter simulation time, and fewer guidance law update times than traditional iterative guidance (IG) and general NMPGs.
AB - Nonlinear Model Predictive Guidance (NMPG) based on optimal control (trajectory optimization) is an extremely promising method for achieving optimal and precise guidance. Event-triggered NMPG offers a dynamic cycle strategy where updates to the optimal guidance law are made only when the deviation between the actual state and the optimal reference trajectory exceeds a set threshold, thus conserving computational resources effectively. To further minimize event-triggering occurrences, we propose embedding an optimal parameter estimation step into it. With increasing sampled flight-state data over time, the motion model for NMPG progressively converges towards the actual one, thereby yielding more precise open-loop guidance laws. The simulation results of an ascent guidance problem show that the proposed method has higher guidance accuracy, a shorter simulation time, and fewer guidance law update times than traditional iterative guidance (IG) and general NMPGs.
KW - Event-Triggered
KW - Nonlinear Model Predictive Guidance
KW - Optimum Parameter Estimation
UR - http://www.scopus.com/inward/record.url?scp=105000508518&partnerID=8YFLogxK
U2 - 10.1007/978-981-96-2248-1_2
DO - 10.1007/978-981-96-2248-1_2
M3 - 会议稿件
AN - SCOPUS:105000508518
SN - 9789819622474
T3 - Lecture Notes in Electrical Engineering
SP - 12
EP - 23
BT - Advances in Guidance, Navigation and Control - Proceedings of 2024 International Conference on Guidance, Navigation and Control Volume 13
A2 - Yan, Liang
A2 - Duan, Haibin
A2 - Deng, Yimin
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Guidance, Navigation and Control, ICGNC 2024
Y2 - 9 August 2024 through 11 August 2024
ER -