• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.148.5-148
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• 2001

In this report, an adaptive flight control law based on a linear-parameter-varying (LPV) model is presented for a flight control system. The control system is designed to track an output of a vehicle to a reference signal from the guidance system, which generates a reference flight path. The proposed adaptive control law adjusts the controller gains continuously on line as flight conditions change. The obtained adaptive controller guarantees global stability over a wide flight envelope. Computer simulation involving six-degree-of-freedom nonlinear flight dynamics is applied to Japan´s automatic landing flight experimental vehicle (ALFLEX) to examine the effectiveness of the proposed adaptive flight control law.

• 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.

• 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.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.519-524
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• 2003

In this report, a longitudinal adaptive flight control law is presented for the automatic landing system of a Japanese automatic landing flight experiment vehicle (ALFLEX). The longitudinal adaptive flight control law is designed to track an output of the vehicle to a guidance signal from the guidance portion of the automatic landing system. The proposed adaptive control law in the attitude control portion adjusts the controller gains continuously online as flight conditions change, in spite of the existence of unmodeled dynamics. The number of the controller gains to be adjusted is decreased to 1/2 from the previous studies. Computer simulation involving six-degree-of-freedom (DOF) nonlinear flight dynamics is performed to examine the effectiveness of the proposed adaptive control law. In order to verify the influence of the dispersion of the initial conditions, the Monte Carlo simulation is also applied. The initial conditions are more widely dispersed than the previous studies. As a result, except under the unsuitable initial conditions, the ALFLEX successfully landed on the runway.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.12
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• pp.1070-1078
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• 2000

A nonlinear adaptive flight control system is proposed using a backstepping controller with neural network controller. The backstepping controller is used to stabilize all state variables simultaneously without the two-timescale assumption that separates the fast dynamics, involving the angular rates of the aircraft, from the slow dynamics which includes angle of attack, sideslip angle, and bank angle. It is assumed that the aerodynamic coefficients include uncertainty, and an adaptive controller based on neural networks is used to compensate for the effect of the aerodynamic modeling error. It is shown by the Lyapunov stability theorem that the tracking errors and the weights of neural networks exponentially converge to a compact set. Finally, nonlinear six-degree-of-freedom simulation results for an F-16 aircraft model are presented to demonstrate the effectiveness of the proposed control law.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.11
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• pp.995-1002
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• 2000

In this paper, fuzzy logic controllers(FLC) are designed for control of flight. For tuning FLC, we used adaptive evolutionary algorithms(AEA) which uses a genetic algorithm(GA) and an evolution strategy (ES) in an adaptive manner in order to take merits of two different evolutionary computations. We used AEA to search for optimal settings of the membership functions shape and gains of the inputs and outputs of FLC. Finally, the proposed controller is applied to the high-angle-of-attack flight system for a supermaneuverable version of the f-18 aircraft and compares with other methods.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.40.2-40
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• 2002

This paper presents the reconfigurable flight controller in the presence of jammed actuator fault using the adaptive sliding mode control scheme. It is developed under the assumption that the control surface fault cannot be detected and the positions of stuck control surfaces are unknown. It is well known that sliding mode controller shows good performance for the systems with various uncertainties. None-operating stuck actuator makes the system behave like bias which degrades the system performance and sometimes destabilizes the system. Therefore, the bias term generated by actuator faults has to be compensated by the control system. To the objective, we adopt the adaptive sliding mode cont...

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.4
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• pp.329-339
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• 2003

A neural network based adaptive controller design method is proposed for reconfigurable flight control systems in the presence of variations in aerodynamic coefficients or control effectiveness decrease caused by control surface damage. The neural network based adaptive nonlinear controller is developed by making use of the backstepping technique for command following of the angle of attack, sideslip angle, and bank angle. On-line teaming neural networks are implemented to guarantee reconfigurability and robustness to the uncertainties caused by aerodynamic coefficients variations. The main feature of the proposed controller is that the adaptive controller is designed with assumption that not any of the nonlinear functions of the system is known accurately, whereas most of the previous works assume that only some of the nonlinear functions are unknown. Neural networks loam through the weight update rules that are derived from the Lyapunov control theory. The closed-loop stability of the error states is also investigated according to the Lyapunov theory. A nonlinear dynamic model of an F-16 aircraft is used to demonstrate the effectiveness of the proposed control law.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.52-56
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• 2004

Unmanned Aerial Vehicles (UAVs) are remotely piloted or self-piloted aircraft by inputted program in advance or artificial intelligence. In this study Aileron and Elevator are used to control the movement of airplane for horizontal and vertical flights about its longitudinal and lateral axis. In an introduction, the drone was linearly modeled by extracting aerodynamic parameter through flight test and simulation, lift and drag coefficient corresponding to angle of attack, changes of pitching moment coefficient. In the main subject, the flight simulation was performed after constructing hardware using TMS320F2812 from TI company and PID with lateral and longitudinal controller for horizontal and vertical flights. Flying characteristics of two system were estimated and compared through real flight test with hardware equipped algorithm and adaptive algorithm that was applied to consider external factors such as turbulence. In conclusion the control performance of the controller with proposed algorithm was streamlined at lateral and longitudinal controller respectively, we will discuss guidance command to pass way point.