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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/18933

Title: 模糊系統與類神經網路控制器之整合及飛機著陸控制之應用
Integration of Fuzzy System and Neural Network Controller and It's Application to Aircraft Landing Control
Authors: 莊季高
Contributors: NTOU:Department of Communications Navigation and Control Engineering
國立臺灣海洋大學:通訊與導航工程學系
Keywords: automatic landing system;fuzzy neural etwork;genetic algorithm
Date: 2002
Issue Date: 2011-08-17T07:05:44Z
Abstract: 飛行器在大氣中飛行,因為大氣的任何變化,都會影響飛航品質,甚至威脅飛航安全。美國國家運輸安全委員會在1989 年到1999 年的研究調查指出,飛航失事中,平均有22.6 %的意外事件跟天候有關。航機在進場或落地階段時,因為在高度不高及速度不快的情況下,一但遇到剪風或亂流等大氣的劇烈變化,會造成飛機航向、下滑軌跡的偏移,嚴重影響飛航安全。任何飛行器不論在何處,降落都是最困難的一環,大多數的飛機都有儀降系統,自動著陸系統藉由儀降系統所提供的高度、位置、角度等相關資訊,幫助飛行器安穩地著陸,大大減少飛行員的工作負擔。傳統控制器的控制理論多採增益預定的方式,當飛行狀況已經超出原先設定範圍,自動駕駛必須改成手動駕駛;若駕駛員對剪風或亂流的判斷經驗不足,落地的那兩、三秒就會造成飛機失速而墬毀。類神經網路與模糊邏輯系統彼此有互補的特性,整合後可使系統兼具兩者優點,模糊類神經網路控制器因此有模糊系統的決策能力與類神經網路的適應性學習能力。本論文應用模糊類神經網路於飛機之著陸控制,分別建立語意式與函數式模糊類神經控制器。另外利用基因演算法的全域搜尋能力,嘗試以實數型基因演算法調整俯仰角自動導航系統的控制參數,並藉由類神經網路模擬風擾的機制做適應控制。也以時序性倒傳遞演算法結合線性化反飛機模組改善模糊類神經控制器的鍵結值,期望建立智慧型的飛機著陸控制器,讓此模糊類神經控制器具有強健性與適應性,能克服外在環境的劇烈變化,提升飛機在風擾中安全著陸的能力。
The atmospheric disturbances affect not only flying qualities of an airplane but also flight safety. According to a survey of the National Transportation Safety Board, 22.6 percent of aircraft accidents in the years of 1989 to 1999 were weather related. When aircraft approaches landing phase the altitude is low and the speed is slow. If the aircraft encountered wind shear or turbulence while landing it could cause altitude loss, heading variation and even crash. Take off and landing are the most difficult operations of a flight. Most aircrafts have installed the Automatic Landing System (ALS) which helps aircraft landing stably and reduces pilot's work loading greatly. Controller of the conventional ALS usually uses gain-scheduling techniques. If the landing environment is beyond predefined conditions, the ALS must disable and the pilot has to operate the aircraft manually. Most pilots have no experience on wind shear or turbulence environment. Usually it ends up with airplane crash. Neural network and fuzzy logic system are complementary techniques to each other. The combination of these two techniques is called Fuzzy Neural Network which has both policy decision of fuzzy system and adaptive learning of neural network. This paper presents an aircraft automatic landing control scheme that uses a fuzzy neural network controller. The controller is constructed by linguistic fuzzy rule or functional fuzzy rule. A real number type genetic algorithm is used to adjust control gains of the pitch autopilot. A neural network device emulates wind disturbances, which provides adaptive control to the system. A backpropagation through time algorithm with a linearized inverse aircraft model refines the connection weights of the fuzzy neural network controller. The proposed controller can overcome violent variation of environment and enhance capability of control for wind disturbances during landing.
URI: http://ntour.ntou.edu.tw/handle/987654321/18933
Appears in Collections:[通訊與導航工程學系] 研究計畫

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