• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.185-188
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• 1993

The goal of this paper is to develop an on-board controller for a model helicopter's hovering attitude control, using i8096 one-chip microcontroller. Required controller algorithm is programmed in ASM-96 assembly language and downloaded into an i8096 microcontroller. The performance of hovering flight using this system is verified by experiments with the model helicopter mounted on an instrumented flight stand where 3 potentiometers and an optical proximity sensor measure te attitude and main rotor speed of the helicopter.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.2
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• pp.112-124
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• 2004

A neural network based adaptive reconfigurable flight controller is presented for a class of discrete-time nonlinear flight systems in the presence of variations of aerodynamic coefficients and control effectiveness decrease caused by control surface damage. The proposed adaptive nonlinear controller is developed making use of the backstepping technique for the angle of attack, sideslip angle, and bank angle command following without two time separation assumption. Feedforward multilayer neural networks are implemented to guarantee reconfigurability for control surface damage as well as robustness to the aerodynamic uncertainties. The main feature of the proposed controller is that the adaptive controller is developed under the assumption that all of the nonlinear functions of the discrete-time flight system are not known accurately, whereas most previous works on flight system applications even in continuous time assume that only the nonlinear functions of fast dynamics are unknown. Neural networks learn through the recursive weight update rules that are derived from the discrete-time version of Lyapunov control theory. The boundness of the error states and neural networks weight estimation errors is also investigated by the discrete-time Lyapunov derivatives analysis. To show the effectiveness of the proposed control law, the approach is i]lustrated by applying to the nonlinear dynamic model of the high performance aircraft.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.8
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• pp.367-375
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• 2001

In Underwater Flight Vehicle depth control system, the followings must be required. First, it needs robust performance which can get over modeling error, parameter variation and disturbance. Second, it needs accurate performance which have small overshoot phenomenon and steady state error to avoid colliding with ground surface or obstacles. Third, it needs continuous control input to reduce the acoustic noise and propulsion energy consumption. Finally, it needs interpolation method which can sole the speed dependency problem of controller parameters. To solve these problems, we propose a depth control method using Fuzzy Sliding Mode Controller with feedforward control-plane bias term and Neural Network Interpolator. Simulation results show the proposed method has robust and accurate control performance by the continuous control input and has no speed dependency problem.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.590-593
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• 2005

Unmanned Helicopter has several abilities such as vertical Take off, hovering, low speed flight at low altitude. Such vehicles are becoming popular in actual applications such as search and rescue, aerial reconnaissance and surveillance. These vehicles also used under risky environments without threatening the life of a pilot. Since a small unmanned helicopter is very sensitive to environmental conditions, it is generally known that the flight control is very difficult problems. The nonlinear adaptive fuzzy controller design procedure and its applications for altitude control of unmanned helicopter were described in the paper. This research was concentrated on describing the design methodologies of altitude controller design for small unmanned helicopter acquiring autonomous take off and vertical movement. The design methodologies and performance of the altitude controller were simulated and verified with an adaptive fuzzy controller. Throughout simulation results, I showed that the proposed adaptive controllers have enhanced control performance such as robustness, effectiveness and safety, in the altitude control of the unmanned helicopter.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.1063-1067
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• 1993

Fuzzy logic is applied to a roll autopilot for missiles. Fuzzy rules are made so that the response duplicates that of the conventional control law for some flight condition. A scaling factor of the fuzzy controller is then scheduled by the missile velocity and altitude information to cope with the variation of the roll dynamics from that flight condition. By computer simulations and calculation of the stability margin, it is shown that the fuzzy control is robuster than the conventional one over the flight envelope even though two control laws work similarly for some flight conditions.

• International Journal of Aeronautical and Space Sciences
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• v.8 no.1
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• pp.32-45
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• 2007

The T-50 advanced supersonic jet trainer employs the Relaxed Static Stability (RSS) concept to improve the aerodynamic performance while the flight control system stabilizes the unstable aircraft and provides adequate handling qualities. The T-50 flight control laws employ a proportional-plus-integral type controller based on a dynamic inversion method in longitudinal axis and a proportional type controller based on a blended roll system with simple roll rate feedback and beta-betadot feedback system. These control laws are verified by flight tests with various maneuver set flight envelopes and the control laws are updated to resolve flight test issues. This paper describes several concepts of flight control laws used in T-50 to resolve those flight test issues. Control laws for solving the roll-off problem during pitch maneuver in asymmetric loading configurations, improving the departure resistance in negative angle of attack conditions and enhancing the fine tracking performance in air-to-air tracking maneuvers are described with flight test data.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.969-974
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• 2004

For a maneuvering unmanned autonomous helicopter, it is necessary to design a proper controller of each flight mode. In this paper, overall helicopter dynamics is derived and hovering model is linearized and transformed into a state equation form. However, since it is difficult to obtain parameters of stability derivatives in the state equation directly, a linear control model is derived by time-domain parametric system identification method with real flight data of the model helicopter. Then, two different controllers - a linear feedback controller with proportional gains and a robust controller - are designed and their performance is compared. Both proposed controllers show outstanding results by computer simulation. These validated controllers can be used to autonomous flight controller of a real unmanned model helicopter.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.21 no.3
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• pp.1-8
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• 2013

An application of adaptive flight controller is required for the non-linear and high uncertain system that configuration of tiltrotor aircraft is dramatically changed from rotary wing mode to fixed wing mode. In this paper, the applicable adaptive controller for the tiltrotor aircraft was designed using Neural Networks and DMI (Dynamic Model Inversion). The performance of the SCAS (Stability and Control Augmentation System) was simulated against manned military specification, using the fullscale model of 'Smart UAV(Unmanned Aerial Vehicle)' developed by Korea Aerospace Research Institute. And Neural Networks based adaptive controller was verified through its whole operating envelope using the established HQ (Handling Quality) criteria.

• International Journal of Aeronautical and Space Sciences
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• v.14 no.3
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• pp.247-255
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• 2013

The flight control of re-entry vehicles poses a challenge to conventional gain-scheduled flight controllers due to the widely spread aerodynamic coefficients. In addition, a wide range of uncertainties in disturbances must be accommodated by the control system. This paper presents the design of a roll channel controller for a non-axisymmetric reentry vehicle model using the trajectory linearization control (TLC) method. The dynamic equations of a moving mass system and roll control model are established using the Lagrange method. Nonlinear tracking and decoupling control by trajectory linearization can be viewed as the ideal gain-scheduling controller designed at every point along the flight trajectory. It provides robust stability and performance at all stages of the flight without adjusting controller gains. It is this "plug-and-play" feature that is highly preferred for developing, testing and routine operating of the re-entry vehicles. Although the controller is designed only for nominal aerodynamic coefficients, excellent performance is verified by simulation for wind disturbances and variations from -30% to +30% of the aerodynamic coefficients.

• Design & Manufacturing
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• v.11 no.3
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• pp.41-45
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• 2017

Drones can be manipulated in a variety of ways. One of the most common controller is joystick method. But joystick controller uses both hands and takes a long time to learn. Particularly, in the case of 8-character flight, it is necessary to use both front and rear flight (pitch), left and right flight (Roll), and body rotation (Yaw). Joystick controller has limitations to intuitively control it. In particular, when the main body rotates, the viewpoint of the forward direction is changed between the drones and the user, thereby causing a mental rotation problem in which the user must control the rotating state of the drones. Therefore, we developed a motion matching controller that matches the motion of the drones and the controller. That is, the movement of the drone and the movement of the controller are the same. In this study, we used a gyro sensor and an acceleration sensor to map the controller's forward / backward, left / right and body rotation movements to drone's forward / backward, left / right, and rotational flight motion. The motor output is controlled by the throttle dial at the center of the controller. As the motions coincide with each other, it is expected that the first drone operator will be able to control more intuitively than the joystick manipulator with less learning.