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This paper addresses the autonomous flight control system of an unmanned helicopter. We adopt a model-free discrete linear quadratic tracking (LQT) control architecture based on reinforcement learning algorithm by rewriting the Q-learning approach. From input and output data, the linear quadratic optimal gain is directly found without system identification procedure. Least square method is adopted...
The purpose of this paper is to present a proper guidance law for an UAV tracking and landing on a moving target with aid of on-board vision sensor. The image processing procedure extracts the target from the captured video and estimate the position and velocity of the target. With this information on image plane, we could calculate its position with relation to inertia frame. Using this geolocation...
This paper concerns with the autonomous flight control system of an unmanned helicopter, which is combined with reinforcement learning based neuro-controller. We assume that PID (proportional-integral-derivative) type, linear feedback controller is predesigned and it can stabilize the system with limited performance. The conservative control behavior is improved by the synthesis of the poor feedback...
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