Microwave Integrated Circuits Design with Relational Induction Neural Network

Liu, Jie; Chen, Zhi-Xi; Dong, Wenhui; Wang, Xiao; Shi, Jia; Teng, Hong-Liang; Dai, Xi-Wang; Yau, Shing‐Tung; Liang, Chuntao; Feng, Pingfa

doi:10.48550/arxiv.1901.02069

Cited by 1 publication

(1 citation statement)

References 22 publications

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“…In 2016, the AlphaGo agent with a DRL algorithm as one of the core technologies defeated the top human professional go player Lee Sedol in the Go match, making the DRL algorithm widely recognized and deeply studied by the research community [11]. In recent years, DRL has been widely promoted and researched in many fields such as robotics, intelligent driving and electronic design [12][13][14]. In the literature [15], a reinforcement learning algorithm was used to design a staged reward function based on terminal constraint and fuel consumption index to train the rocket landing guidance process, and a guidance strategy with the ability to generalize the model deviation was obtained.…”

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

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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.

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