| Summary: | 碩士 === 國立臺灣海洋大學 === 通訊與導航工程學系 === 101 === According to a survey of the National Transportation Safety Board, 28% of aircraft accidents in the years of 1950 to 2010 were weather related. Aircraft takeoff and landing has always been the most important part of aviation safety. In the approach or landing stage, aircraft altitude and speed are low, dramatic changes of atmosphere, such as the wind shear or turbulence, will cause the aircraft off heading and glide path, and seriously affect the flight safety. Nowadays most aircraft have been installed automatic landing system. In the normal environment, aircraft automatic landing system can significantly reduce the pilot's workload. Conventional automatic landing systems are designed by the use of gain scheduling or traditional adaptive control techniques, once the flight conditions or wind disturbance intensity is beyond the limits of the system, the pilot must turn off the automatic landing system and take over the aircraft landing procedures manually. The purpose of this thesis is to apply Cerebellar Model Articulation Controller (CMAC) and Sliding Mode Control (SMC) to aircraft landing control, combined with genetic algorithm (GA), particle swarm optimization (PSO) and chaotic particle swarm optimization (CPSO) which are used to adjust parameters of Sliding Mode Control. The proposed intelligent control scheme not only can effectively improve the intelligent systems to against the wind disturbance, but also can help the pilots guide the aircraft to a safe landing in difficult environment. In addition, Lyapunov theory is utilized to derive the optimal learning rule. The proposed intelligent control scheme can help the pilots guide the aircraft to a safe landing in difficult environment. Furthermore, the TMS320C6713 DSP development tools, links, JTAG, CCS compiled code flash memory are applied to achieve real-time automatic landing system by the floating-point DSP controller.
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