Joint dynamic optimization of the target orbit and flight trajectory of a launch vehicle based on state-triggered indices

Song, Zhengyu; Gong, Qinghai

doi:10.1016/j.actaastro.2020.04.017

Cited by 22 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To this effect, the recovery and reuse of flight hardware has been a multidisciplinary problem being tackled by scientists, engineers and academics all over the world [4]. As the nerve center of the launch vehicle [5], the guidance and control system directly affects the flight quality and comprehensive performance of the launch vehicle and determines the success of the space mission [6]. The guidance and control theory and technology involved are the hot and difficult points in the field of aircraft control nowadays, and they are cutting-edge, basic and comprehensive.…”

Section: Introductionmentioning

confidence: 99%

Research on Intelligent Control Method of Launch Vehicle Landing Based on Deep Reinforcement Learning

Xue,

Bai,

Zhao

et al. 2023

Mathematics

View full text Add to dashboard Cite

A launch vehicle needs to adapt to a complex flight environment during flight, and traditional guidance and control algorithms can hardly deal with multi-factor uncertainties due to the high dependency on control models. To solve this problem, this paper designs a new intelligent flight control method for a rocket based on the deep reinforcement learning algorithm driven by knowledge and data. In this process, the Markov decision process of the rocket landing section is established by designing a reinforcement function with consideration of the combination effect on the return of the terminal constraint of the launch vehicle and the cumulative return of the flight process of the rocket. Meanwhile, to improve the training speed of the landing process of the launch vehicle and to enhance the generalization ability of the model, the strategic neural network model is obtained and trained via the form of a long short-term memory (LSTM) network combined with a full connection layer as a landing guidance strategy network. The proximal policy optimization (PPO) is the training algorithm of reinforcement learning network parameters combined with behavioral cloning (BC) as the reinforcement learning pre-training imitation learning algorithm. Notably, the rocket-borne environment is transplanted to the Nvidia Jetson TX2 embedded platform for the comparative testing and verification of this intelligent model, which is then used to generate real-time control commands for guiding the actual flying and landing process of the rocket. Further, comparisons of the results obtained from convex landing optimization and the proposed method in this work are performed to prove the effectiveness of this proposed method. The simulation results show that the intelligent control method in this work can meet the landing accuracy requirements of the launch vehicle with a fast convergence speed of 84 steps, and the decision time is only 2.5 ms. Additionally, it has the ability of online autonomous decision making as deployed on the embedded platform.

show abstract

Section: Introductionmentioning

confidence: 99%

Research on Intelligent Control Method of Launch Vehicle Landing Based on Deep Reinforcement Learning

Xue,

Bai,

Zhao

et al. 2023

Mathematics

View full text Add to dashboard Cite

show abstract

“…Although numerous scenarios in actual launch missions were considered, the orbit mission under power failure conditions could still not be reconfigured. The literature [17] used a joint-convex optimization method to optimize the orbit mission and trajectory under power failures, with high planning accuracy and timeliness. In this research, selecting the initial value of the target orbital elements and prioritizing the orbit height before correcting the orbital position deviation are of great engineering significance.…”

Section: Introductionmentioning

confidence: 99%

Orbit-Injection Strategy and Trajectory-Planning Method of the Launch Vehicle under Power Failure Conditions

Diao

et al. 2022

Aerospace

View full text Add to dashboard Cite

Aiming at the problem of autonomous decision making and trajectory planning (ADMTP) for launch vehicles under power failure conditions, the target degradation order strategy (TDOS) and the trajectory online planning method were studied in this paper. Firstly, the influence of power failure on the orbit-injection process was analyzed. Secondly, the TDOS was proposed according to the mission attribute, failure mode, and multi-payload combination. Then, an online planning method based on the adaptive target update iterative guidance method (ATU-IGM) and radial basis neural network (RBFNN) was proposed, where the ATU-IGM adopted the basic TDOS criterion for generating optimal orbit-injection samples and online guidance instructions, and the RBFNN was used for orbit-injection samples training and online generation of orbital missions. Finally, the comparative simulation analysis was performed under multi-failure conditions. The results showed that the TDOS proposed in this paper could meet the requirements of the mission decision making under different failures, target orbit types, orbit-injection methods, and payload compositions. The online trajectory-planning capability deviation was less than 5%, and the mission decision-making and trajectory-planning time were less than 10 ms. This study provides theoretical support for autonomous decision making and planning of space launch missions.

show abstract

Autonomous Guidance Reconfiguration for the Thrust Drop Fault of a Multi-satellite Launch Mission

Song

2023

Lecture Notes in Electrical Engineering

View full text Add to dashboard Cite

Joint dynamic optimization of the target orbit and flight trajectory of a launch vehicle based on state-triggered indices

Cited by 22 publications

References 9 publications

Research on Intelligent Control Method of Launch Vehicle Landing Based on Deep Reinforcement Learning

Research on Intelligent Control Method of Launch Vehicle Landing Based on Deep Reinforcement Learning

Orbit-Injection Strategy and Trajectory-Planning Method of the Launch Vehicle under Power Failure Conditions

Autonomous Guidance Reconfiguration for the Thrust Drop Fault of a Multi-satellite Launch Mission

Contact Info

Product

Resources

About