• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1624-1627
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• 2005

We proposed a design technique for auto-landing guidance and control system. This technique utilizes linear controller and neural network. Main features of this technique is to use conventional linear controller and compensate for the error coming from the model uncertainties and/or reference model mismatch. In this study, the multi-perceptron neural network with single hidden layer is adopted to compensate for the errors. Glide-slope capture logic for auto-landing guidance and control system is designed in this technique. From the simulation results, it is observed that the responses of velocity and pitch angle to commands are fairly good, which are directly related to control inputs of throttle and elevator, respectively.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.50.6-50
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• 2001

This paper deals with a design of nonlinear glide slope capture logic using dynamic model inversion in singular perturbation, which is applicable to the autolanding in ILS. Aircraft dynamics are separated into the fast time-scale variables, related with the inner-loop design, and the slow time-scale variables, related with the outer-loop design. It is assumed that the aircraft starts landing at 1000ft of altitude, -2.5deg of flight path angle, and 250ft/sec of velocity. In the outer-loop design, commands of altitude and velocity are selected and thereby the pseudo-controls of power level and pitch rate are determined. Also the elevator input to the aircraft is determined in the inner-loop design. The final design is evaluated in 6 DOF simulation model of the associated aircraft, in which the actuator models are not included. The results show the satisfactory autolanding ...