Cerebellar Model Articulation Neural Network-Based Distributed Fault Tolerant Tracking Control with Obstacle Avoidance for Fixed-Wing UAVs

Qian, Moshu; Wu, Zhu; Zhang, Kangkang

doi:10.1109/taes.2023.3283237

Cited by 4 publications

(1 citation statement)

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“…Additionally, it is challenging to obtain globally optimal solutions that satisfy mission requirements. It is worth mentioning that the Virtual-agent Artificial Potential Function constructed by Qian et al improved the issue of local minima while reducing computation time, but such methods still lack long-term and global optimization capabilities [14].…”

Section: Introductionmentioning

confidence: 99%

Transfer Learning Method from Parameter Scene to Physical Scene Based on Self-Game Theory

Zhang,

Yang,

et al. 2024

Electronics

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Due to the inherent challenges in meeting the comprehensive training requirements of combat scenarios within a digital environment, this paper proposes innovative solutions. Firstly, a parametric scene training method is introduced, aiming to enhance the adaptability of the training process. Secondly, the utilization of the digital environment as a test environment is suggested, which can significantly improve the efficiency of iterative virtual-to-real conversion. Additionally, employing digital simulation as a pre-test environment for the physical setting can effectively reduce deployment costs and enhance the safety of virtual reality migration experiments. The proposed approach involves constructing a training model based on the parameterized environment and implementing a feedback loop utilizing the digital environment agent model. This framework enables continuous verification of the environment’s parameters, ensuring the accuracy of decision-making strategies and evaluating the transferability of the decision-making model.

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Section: Introductionmentioning

confidence: 99%