Nonlinear autopilot control design for a 2-DOF helicopter model

Kaloust, J.; Ham, Chan; Qu, Zhihua

doi:10.1049/ip-cta:19971638

Cited by 49 publications

(32 citation statements)

References 2 publications

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“…Eq. (22) shows thatV ie ≤ 0 once the errors are outside the compact set Ω ei . According to the standard Lyapunov theorem, we conclude that s i ,θ i , andφ i are bounded.…”

Section: B Control Design With Limited Informationmentioning

confidence: 97%

“…According to the standard Lyapunov theorem, we conclude that s i ,θ i , andφ i are bounded. From (22) and (24), it can be seen that V ie is strictly negative as long as s i is outside the compact set Ω sie . Therefore, there exists a constant T 1 such that for t > T 1 , the filtered tracking error s i converges to Ω sie , that is to say,…”

Section: B Control Design With Limited Informationmentioning

confidence: 98%

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Synchronized tracking control of multi-agent system with limited information

Cui

Ren

2010

49th IEEE Conference on Decision and Control (CDC)

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In this paper, synchronized tracking control is considered for multiple agents with unknown system dynamics, while the desired trajectory is only available to portion of the team members. Using the weighted average of the neighbors' outputs, adaptive neural network (NN) tracking control is designed for each agent. Rigid mathematical proof was provided for the proposed algorithm based on the Lyapunov analysis. It is shown that, under the proposed NN control, the output tracking error of each agent converges to an adjustable neighborhood of the origin. Simulations of synchronized altitude tracking of multiple unmanned helicopters are provided to demonstrate the effectiveness of the approaches presented.

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“…Eq. (22) shows thatV ie ≤ 0 once the errors are outside the compact set Ω ei . According to the standard Lyapunov theorem, we conclude that s i ,θ i , andφ i are bounded.…”

Section: B Control Design With Limited Informationmentioning

confidence: 97%

Section: B Control Design With Limited Informationmentioning

confidence: 98%

Synchronized tracking control of multi-agent system with limited information

Cui

Ren

2010

49th IEEE Conference on Decision and Control (CDC)

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“…On the other hand, there exist some nonlinear descriptions of helicopter dynamics but only for reduced order models (Kaloust, 1997) or without taking into consideration the interaction of the main rotor with the fuselage (Verdult et al 2004). The model here presented is a realistic nonlinear model of the fundamental dynamics of an articulated rigid-bladed rotorcraft.…”

Section: Helicopter Simulation Modelmentioning

confidence: 99%

Vibration reduction for vision systems on board unmanned aerial vehicles using a neuro-fuzzy controller

Marichal

Tomás-Rodrı́guez

Hernández

et al. 2013

Journal of Vibration and Control

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This is the accepted version of the paper.This version of the publication may differ from the final published version. In this paper, an intelligent control approach based on Neuro-Fuzzy systems performance is presented with the objective of counteracting the vibrations that affect the low-cost vision platform onboard an unmanned aerial system of rotating nature. Permanent repository linkA scaled dynamical model of a helicopter is used to simulate vibrations on its fuselage. The impact of these vibrations on the low-cost vision system will be assessed and an intelligent control approach will be derived in order to reduce its detrimental influence.Different trials that consider a Neuro-Fuzzy approach as a fundamental part of an intelligent semi-active control strategy have been carried out. Satisfactory results have been achieved compared to those obtained by means of vibration reduction passive techniques.

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“…Research work on helicopter control is necessary due to the demand of precise control in emergency conditions like earth quake, fire accidents. Nonlinear control of 2-DOF helicopter research is carried out in [18]- [20].…”

Section: Introductionmentioning

confidence: 99%

Design, Control and Analysis of Low Cost Archetype Dual Rotor Helicopter for Educational Institution

Ramalakshmi¹,

Manoharan²

2016

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This paper presents the design and development of low cost archetype dual rotor helicopter (LCADRH) for academic research in an educational institution. The LCADRH is installed with optical pitch encoder and yaw encoder which measure elevation and side to side motion of helicopter. The objective of the project is to design and integrate the helicopter with data acquisition board and sensors to provide hardware features, software support capability for its rapid real time measurement and control. The low cost designed LCADRH facilitates the academic research for students in the institution and is able to provide hands on training to understand the concept of nonlinearity, system modelled and unmodelled dynamics and uncertainty, modelling, simulation and control by doing practical experiments. The mathematical model of the LCADRH is derived using grey box modelling method. The control of LCADRH is challenging due to its nonlinearity and effect of strong coupling between aerodynamic forces and torques generated by the both pitch and yaw actuators. In closed loop position control of LCADRH, pitch and yaw axis motion is regulated using linear quadratic controller (LQR). Encouraging results are obtained both in simulation and hardware.

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Nonlinear autopilot control design for a 2-DOF helicopter model

Cited by 49 publications

References 2 publications

Synchronized tracking control of multi-agent system with limited information

Synchronized tracking control of multi-agent system with limited information

Vibration reduction for vision systems on board unmanned aerial vehicles using a neuro-fuzzy controller

Design, Control and Analysis of Low Cost Archetype Dual Rotor Helicopter for Educational Institution

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