Automatic Landing Control Using Adaptive Self-Organizing Cerebellar Model Articulation Controller

Abstract

When aircraft approaches landing phase the altitude is low and the speed is slow. If the aircraft encountered wind shear while landing it could cause altitude loss, heading variation and even crash. Nowadays, most aircraft have installed the Automatic Landing System (ALS) which relies on the Instrument Landing System (ILS) to help aircraft landing safely and reduces pilot's workload greatly. But control schemes of the conventional ALS usually use gain-scheduling and conventional adaptive control techniques. If the flight conditions are beyond the preset envelope, the ALS is disabled and the pilot takes over. An inexperienced pilot may not be able to guide the aircraft landing safely. In order to improve the performance of the ALS, this paper presents an intelligent control scheme that uses an adaptive self-organizing cerebellar model articulation controller (CMAC) to replace conventional controller and guide the aircraft to a safe landing. Moreover, stability of the proposed automatic landing control system is guaranteed by the Lyapunov stability analysis. Adaptive learning rates of the selforganizing CMAC (SOCM) controller are derived from Lyapunov theory. The proposed intelligent controller can act as an experienced pilot and guide the aircraft landing safely in wind shear condition. Simulation results show that the proposed controller has better performance than conventional PID controller.