This study proposes a method for real-time identification of a driver model. The proposed method requires only the yaw rate sensor, the steering angle sensor, and velocity sensors that are usually installed in the production car. The identification algorithm involves the division of the recorded data, prefiltering of the divided data, estimation of the driver's desired response, and identification. The prefilter extracts the driver's involuntary response that can be modelled in a simple form. The ideal car response that the driver attempts to track is estimated from the recorded data, and this response is provided to the identification algorithm of the feedback driver model for error tracking. These newly developed methods enable real-time identification under actual driving conditions. The driving simulator experiments and the actual driving tests were performed, and the proposed method was validated. The results show that the time history of the variation in the driver's characteristics can be realized in real time using the proposed method.

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An output-feedback H 1 preview controller is proposed. In the flight control operations performed in this study, the disturbance input is estimated by the observer, and the disturbance responses are predicted and restricted using the H 1 preview controller. The proposed system was applied to the longitudinal dynamics of an experimental unmanned aerial vehicle (UAV), which was developed to study the flight control of rocket planes. The accuracy of the estimated disturbance and the performance of the preview controller were validated through performing flight testing operations. From the results of the flight testing operations, the proposed system based on the disturbance model of the pitching moment can be used for the flight control of the experimental UAV.

A small battery-powered helicopter with a total weight of about 200 g and a rotor diameter of about 35 cm has been developed. Some indoor flights were performed without a human operator using a transmitter. The helicopter is capable of autonomous hovering flight near walls when IR range finders mounted on it are used to measure distances to walls and the floor. Four mounted photodetectors permit two maneuvers. In the first maneuver, the helicopter can follow a moving light. So it can be controlled simply by moving a light, and can follow a robot or a person carrying a light. In the second maneuver, the helicopter flying over a first light can fly towards a second light, when the first light is turned off and the second light is turned on. The position of the helicopter can be controlled by successively switching (on and off) lights in a row.