Research on Fuzzy Guidance Law for Unmanned Aerial Vehicle

Wang, Hui; Guan, Zuo yu; Liu, Yu; Zhang, Bo bo

doi:10.2991/icacsei.2013.31

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…Kasmaiee and Tadjfar's use of image processing in aerospace applications and improving the efficiency of the spraying system is fully described in the papers [11,12]. Reference [13] proposes a new guidance law based on the fuzzy logic method. Li et al [14] consider target acceleration as a bounded disturbance, then use a primal-dual neural network to solve the optimal solution under this constraint, and develop a guidance law based on model predictive control.…”

Section: Introductionmentioning

confidence: 99%

Intercept Guidance of Maneuvering Targets with Deep Reinforcement Learning

Hu,

Xiao,

Guan

et al. 2023

International Journal of Aerospace Engineering

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In this paper, a novel guidance law based on a reinforcement learning (RL) algorithm is presented to deal with the maneuvering target interception problem using a deep deterministic policy gradient descent neural network. We take the missile’s line-of-sight (LOS) rate as the observation of the RL algorithm and propose a novel reward function, which is constructed with the miss distance and LOS rate to train the neural network off-line. In the guidance process, the trained neural network has the capacity of mapping the missile’s LOS rate to the normal acceleration of the missile directly, so as to generate guidance commands in real time. Under the actor-critic (AC) framework, we adopt the twin-delayed deep deterministic policy gradient (TD3) algorithm by taking the minimum value between a pair of critics to reduce overestimation. Simulation results show that the proposed TD3-based RL guidance law outperforms the current state of the RL guidance law, has better performance to cope with continuous action and state space, and also has a faster convergence speed and higher reward. Furthermore, the proposed RL guidance law has better accuracy and robustness when intercepting a maneuvering target, and the LOS rate is converged.

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Section: Introductionmentioning

confidence: 99%

Intercept Guidance of Maneuvering Targets with Deep Reinforcement Learning

Hu,

Xiao,

Guan

et al. 2023

International Journal of Aerospace Engineering

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“…Hence guidance laws are still being researched as an active topic to develop an algorithm with higher probability of intercept and reduced complexity in computation. Genetic algorithms, fuzzy logic, observers, sliding mode and artificial intelligence (AI) are some of the concepts that have been used to improve the guidance algorithms (Chen and Zhang, 2008; Hossain et al , 2013; Xu, 2012; Wang et al , 2013; Yang et al , 2013; Ma and Zhang, 2014; Li et al , 2014a, b).…”

Section: Introductionmentioning

confidence: 99%

Aerial vehicle guidance based on passive machine learning technique

Chithapuram

Cherukuri

Jeppu

2016

IJICC

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Purpose The purpose of this paper is to develop a new guidance scheme for aerial vehicles based on artificial intelligence. The new guidance scheme must be able to intercept maneuvering targets with higher probability and precision compared to existing algorithms. Design/methodology/approach A simulation setup of the aerial vehicle guidance problem is developed. A model-based machine learning technique known as Q-learning is used to develop a new guidance scheme. Several simulation experiments are conducted to train the new guidance scheme. Orthogonal arrays are used to define the training experiments to achieve faster convergence. A well-known guidance scheme known as proportional navigation guidance (PNG) is used as a base model for training. The new guidance scheme is compared for performance against standard guidance schemes like PNG and augmented proportional navigation guidance schemes in presence of sensor noise and computational delays. Findings A new guidance scheme for aerial vehicles is developed using Q-learning technique. This new guidance scheme has better miss distances and probability of intercept compared to standard guidance schemes. Research limitations/implications The research uses simulation models to develop the new guidance scheme. The new guidance scheme is also evaluated in the simulation environment. The new guidance scheme performs better than standard existing guidance schemes. Practical implications The new guidance scheme can be used in various aerial guidance applications to reach a dynamically moving target in three-dimensional space. Originality/value The research paper proposes a completely new guidance scheme based on Q-learning whose performance is better than standard guidance schemes.

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Research on Fuzzy Guidance Law for Unmanned Aerial Vehicle

Cited by 2 publications

References 2 publications

Intercept Guidance of Maneuvering Targets with Deep Reinforcement Learning

Intercept Guidance of Maneuvering Targets with Deep Reinforcement Learning

Aerial vehicle guidance based on passive machine learning technique

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