Adaptive Fuzzy Neural Network Control for Automatic Landing System

Juang, Jih‐Gau; Chien, Li-Hsiang

doi:10.1007/978-3-642-16693-8_53

Cited by 2 publications

(4 citation statements)

References 12 publications

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“…The simulations quote 2,16,20,21,25 results. The inputs of the LFNN controller are altitude, altitude command, altitude rate, and altitude rate command of the aircraft.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…With a fixed rate, it can only reach 55 ft/s. 21 The LFNN controller can successfully overcome 75 ft/s, 20 while the adaptive LFNN can reach 80 ft/s with optimal learning rates. Using optimal convergence theorems, the proposed controller performs better than the controllers trained by the fixed learning rate.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…In this study, wind disturbance is considered in aircraft flight paths. A turbulence model called Dryden form 21 is implemented in the simulations. The equations of the model are given as follows…”

Section: System Descriptionmentioning

confidence: 99%

“…In turbulence conditions, the LFNN controller with a fixed learning rate was also successfully applied to the automatic landing system. 18 In Juang and Chien, 21 a functional fuzzy neural network (FFNN) was proposed for the landing system. An intelligent system includes wind disturbances in controller design, which provides adaptive control ability to the aircraft's automatic landing system.…”

Section: Introductionmentioning

confidence: 99%

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Application of linguistic fuzzy neural network to landing control

Chien,

Juang

2024

Advances in Mechanical Engineering

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Most aircraft accidents occurred during the final approach. Wind disturbance is one of the significant factors in these accidents. During the landing phase, the Automatic Landing System (ALS) can help aircraft land safely and significantly reduce the pilot’s work loading. Control schemes of the conventional ALS usually use gain-scheduling and traditional PID control techniques. A traditional controller cannot control the aircraft if the weather conditions are beyond the allowed limits. To improve the performance of the landing control, this study applies a linguistic fuzzy neural network (LFNN) to replace the conventional controller of ALS. Adaptive learning rules are proposed to enhance the LFNN control ability. The method used to obtain adaptive learning rules is the Lyapunov stability theory. Moreover, the convergence of the system performance error is proved by the Lyapunov theory. This study also compares previously proposed control schemes in aircraft landing control. Different turbulence strengths are implemented into the flight simulation to make the proposed controller more robust and adaptive to various wind disturbance conditions. The LFNN controller can successfully overcome 75 ft/s wind speed, while the adaptive LFNN can reach 80 ft/s with optimal learning rates. Using optimal convergence theorems, the proposed controller performs better than the controllers trained by a fixed learning rate.

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“…The simulations quote 2,16,20,21,25 results. The inputs of the LFNN controller are altitude, altitude command, altitude rate, and altitude rate command of the aircraft.…”

Section: Simulation Resultsmentioning

confidence: 99%

Section: Simulation Resultsmentioning

confidence: 99%

Section: System Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations