A neural network approach to a cooperative balancing problem in quadrotor-unmanned aerial vehicles (QUAVs)

Faelden, Gerard Ely U.; Maningo, Jose Martin Z.; Nakano, Reiichiro; Dadios, Elmer P.

doi:10.1109/hnicem.2015.7393219

Cited by 8 publications

(1 citation statement)

References 11 publications

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“…In any FNN architecture, the use of a learning algorithm is a must. In literature, there are three types of learning algorithms: derivative-based ones (backpropagation [16], Levenberg-Marquardt [17,18], and least square), derivative-free ones (genetic algorithm, particle swarm optimization [19], and sliding mode control (SMC) theory-based), and hybrid algorithms (Levenberg-Marquardt-particle swarm optimization [20], backpropagation-Kalman filter, gradient descent-Kalman filter, and genetic algorithm-Kalman filter). The main problem with the derivative-based learning algorithms is that they need the calculation of the partial derivatives of the outputs of the FNN with respect to the antecedent parameters.…”

Section: Introductionmentioning

confidence: 99%

Learning Control of Fixed-Wing Unmanned Aerial Vehicles Using Fuzzy Neural Networks

Kayacan

Khanesar

Hervás

et al. 2017

International Journal of Aerospace Engineering

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A learning control strategy is preferred for the control and guidance of a fixed-wing unmanned aerial vehicle to deal with lack of modeling and flight uncertainties. For learning the plant model as well as changing working conditions online, a fuzzy neural network (FNN) is used in parallel with a conventional P (proportional) controller. Among the learning algorithms in the literature, a derivative-free one, sliding mode control (SMC) theory-based learning algorithm, is preferred as it has been proved to be computationally efficient in real-time applications. Its proven robustness and finite time converging nature make the learning algorithm appropriate for controlling an unmanned aerial vehicle as the computational power is always limited in unmanned aerial vehicles (UAVs). The parameter update rules and stability conditions of the learning are derived, and the proof of the stability of the learning algorithm is shown by using a candidate Lyapunov function. Intensive simulations are performed to illustrate the applicability of the proposed controller which includes the tracking of a three-dimensional trajectory by the UAV subject to timevarying wind conditions. The simulation results show the efficiency of the proposed control algorithm, especially in real-time control systems because of its computational efficiency.

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Section: Introductionmentioning

confidence: 99%

Learning Control of Fixed-Wing Unmanned Aerial Vehicles Using Fuzzy Neural Networks

Kayacan

Khanesar

Hervás

et al. 2017

International Journal of Aerospace Engineering

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Nonlinear Robust Compensation Method for Trajectory Tracking Control of Quadrotors

Sun

Wang

et al. 2019

IEEE Access

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In this paper, a nonlinear robust control method is developed for trajectory tracking of a quadrotor aircraft. The proposed approach combines the robust signal compensation method and the backstepping technique. First, the quadrotor dynamic system with multiple disturbances and uncertainties is divided into four subsystems. Then, the nominal controllers are constructed for the subsystems without disturbance terms, while the robust signal compensators are introduced to compensate for the effect of nonlinearities and uncertainties. Furthermore, the Lyapunov analysis has shown that the proposed controller can achieve stable tracking in the presence of violent discontinuous disturbances and uncertainties. The tracking error converges to an arbitrarily small neighborhood of zero. Finally, the proposed control design is validated with real flight experiments. The results show superior trajectory tracking performance of the quadrotor system affected by external disturbances. INDEX TERMS Robust control, quadrotor, trajectory tracking, signal compensation. JIA SUN received the B.S. degree in automation from the School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China, in 2015, where she is currently pursuing the Ph.D. degree in control science and engineering. She was a visiting Ph.D. student with the

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Torque Performance Analysis of a Hydrokinetic Turbine in a Tank Set-up using CFD

Fernando

Marfori

Ubando

et al. 2018

2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environm

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A neural network approach to a cooperative balancing problem in quadrotor-unmanned aerial vehicles (QUAVs)

Cited by 8 publications

References 11 publications

Learning Control of Fixed-Wing Unmanned Aerial Vehicles Using Fuzzy Neural Networks

Learning Control of Fixed-Wing Unmanned Aerial Vehicles Using Fuzzy Neural Networks

Nonlinear Robust Compensation Method for Trajectory Tracking Control of Quadrotors

Torque Performance Analysis of a Hydrokinetic Turbine in a Tank Set-up using CFD

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