Optimal neuro-controller in longitudinal auto-landing of a commercial jet transport

Izadi, Hojjat A.; Pakmehr, Mehrdad; Sadati, N.

doi:10.1109/cca.2003.1223466

Cited by 18 publications

(15 citation statements)

References 4 publications

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“…This kind of command is then used for the proposed adaptive longitudinal auto-landing controller. From the general altitude equation (10), the flight path angle of touchdown at constant airspeed will be expressed as…”

Section: Flare Modementioning

confidence: 99%

Longitudinal auto‐landing controller design via adaptive backstepping

Tsai

2008

Adaptive Control & Signal

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This paper presents an auto-landing controller for glide-slope tracking and the flare maneuver via adaptive backstepping design and describes a flight path command generator for indirect altitude control in order to provide precise altitude trajectories for auto-landing of unmanned aerial vehicles (UAVs). Using the adaptive backstepping procedure to synthesize a glide-slope tracking and flare maneuver control law is being used differently from designing the guidance and control loops separately in autopilot. An adaptive controller is proposed to control aircraft from glide-slope to flare by following the flight path angle command for indirect altitude control via elevator and maintaining the constant airspeed control via throttle. Simulation results demonstrate that the adaptive auto-landing controller is capable of effectively guiding the UAV along the flight path angle command under the presence of the wind turbulence. Copyright (C) 2008 John Wiley & Sons, Ltd

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Section: Flare Modementioning

confidence: 99%

Longitudinal auto‐landing controller design via adaptive backstepping

Tsai

2008

Adaptive Control & Signal

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“…However, these researches did not include weather factors such as wind disturbances. There have been some researches on the problem of intelligent landing control [11][12][13][14][15][16][17] but most of them do not consider wind disturbances. Here, we present a learning scheme, which uses adaptive fuzzy neural network controller, to guide the aircraft to a safe landing and make the controller more robust and adaptive to the ever-changing environment.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fuzzy Neural Network Control for Automatic Landing System

Juang

Chien

2010

Computational Collective Intelligence. Technologies and Applications

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Abstract. This paper presents an intelligent automatic landing system that uses adaptive fuzzy neural network controller to improve the performance of conventional automatic landing systems. Functional fuzzy rules are implemented in neural network. In this study, Lyapunov stability theory is utilized to derive adaptive learning rate in the controller design. Stability of the control system is guaranteed. Simulation results show that the fuzzy neural network controller with adaptive learning rate has better performance than PID controller in guiding aircraft to a safe landing in turbulence condition.

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“…Taking into account the airplane dynamics as above gives us 8 conditions ((3), (5), (8) and (9)) that Y * (t) has to fulfil. That is why we have chosen to determine Y * (t) as a 7th-order polynomial:…”

Section: A Joining the Glide Path 1) Lateral Motionmentioning

confidence: 99%

“…1). When the required altitude is reached, the flare manoeuvre is activated, until touch down [3], [8], [9], [10]. During all the phases, the airplane has to stay aligned with the runway axis.…”

Section: Introductionmentioning

confidence: 99%

Visual servoing of an airplane for auto-landing

Bourquardez

Chaumette

2007

2007 IEEE/RSJ International Conference on Intelligent Robots and Systems

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To cite this version:O. Bourquardez, François Chaumette. Visual servoing of an airplane for auto-landing. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, IROS'07, 2007, San Diego, CA, France. pp.1314-1319, 2007. Visual Servoing of an Airplane for Auto-LandingOdile Bourquardez and François Chaumette Abstract-In this paper, a visual servoing scheme is proposed to control an airplane during its landing. A linearized model of the airplane dynamics and decoupled visual features are used to build the control scheme. A desired trajectory which takes into account the airplane dynamic is designed. Coupling this trajectory and the control law enables the airplane to join its desired path. Then the airplane is controlled to follow the glide path, realize the flare manoeuvre and finally touchdown. Simulation results are obtained with a quite realistic flight simulator which is based on a non linear airplane dynamic model. They show that the airplane is able to land automatically by using visual data.

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Optimal neuro-controller in longitudinal auto-landing of a commercial jet transport

Cited by 18 publications

References 4 publications

Longitudinal auto‐landing controller design via adaptive backstepping

Longitudinal auto‐landing controller design via adaptive backstepping

Adaptive Fuzzy Neural Network Control for Automatic Landing System

Visual servoing of an airplane for auto-landing

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