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An Efficient Autolanding Control Model Using Modified Black Widow Optimization Based Hybrid Deep Neural Network

Thangaraj Ayyasamy*, Sivaraj Nirmala, and Ayyavoo Saravanakumar
International Journal of Control, Automation, and Systems, vol. 20, no. 2, pp.627-636, 2022

Abstract : In general, designing a safe and robust automatic landing system is considered as a challenging task because most accidents occur during the landing and takeoff of aircraft. Due to the imbalance of a few external disturbances such as atmospheric turbulence, wind gusts, low altitude, measurement noises, low speed as well as wind shears the unexpected accidents take place. So, to overcome such types of accidents during the landing of UAV and to ensure an exact landing path our paper proposes a novel HDRNN-MBWO based aircraft auto-landing system. Here hybrid deep neural networks and Modified black widow optimization algorithms are integrated so as to form a novel HDRNN-MBWO approach. The main intention of the proposed approach involves designing a safe, robust as well as smooth automatic landing system. Here, the HDRNN-MBWO approach is employed to obtain better optimization performances. In addition to this, this proposed approach detects the fault and provides an estimated output with high efficiency, the smooth landing of aircraft as well as a minimum error value rate. The minimization of the error helps to prevent the crushing of aircraft during auto-landing thereby achieving smooth landing. The performance evaluation and the comparative analysis are carried out to examine the efficiency of the proposed approach under various aspects.

Keyword : ALS, altitude, fault, hybrid deep neural network, modified black widow optimization algorithm, UAV.

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