Nonlinear Controller of Quadcopters for Agricultural Monitoring

Andaluz, Víctor H.; López, Edison; Manobanda, David; Guamushig, Franklin; Chicaiza, Fernando A.; Sánchez, Jorge Sanjurjo; Rivas, David; Pérez, Fabricio; Sánchez, Carlos García; Morales, Vicente

doi:10.1007/978-3-319-27857-5_43

Cited by 22 publications

(9 citation statements)

References 11 publications

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“…The nonlinearity of the quadrotor behavior poses major obstacles in synthesizing effective controllers and control algorithms. Proportional, integral and derivative (PID) controllers are among the popular controllers that have been studied due to their simple structure, good performance, reliability and effectiveness when tuned properly [15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Real Time Optimal Tuning of Quadcopter Attitude Controller Using Particle Swarm Optimization

Abdalla

Al-Baradie

2020

J. Eng. Technol. Sci.

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Section: Introductionmentioning

confidence: 99%

Real Time Optimal Tuning of Quadcopter Attitude Controller Using Particle Swarm Optimization

Abdalla

Al-Baradie

2020

J. Eng. Technol. Sci.

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“…Different categories of UAVs can perform different missions retorting the requirement of the user (Almeida et al, 2006). UAVs are being used in a diversity of civilian purposes such as agricultural purposes (Andaluz et al, 2015), nature safety (Olivares-Mendez et al, 2015), arrangement scrutiny (Sa and Corke, 2014) and monitoring a person (Lim and Sinha, 2015). UAVs can be divided into two categories: fixed-wing and rotatory-wing.…”

Section: Introductionmentioning

confidence: 99%

Disturbances rejection based on sliding mode control

Taimoor

Amin

2019

AEAT

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Purpose The purpose of this paper aims to investigate an effective algorithm for different types of disturbances rejection. New dynamics are designed based on disturbance. Observer-based sliding mode control (SMC) technique is used for approximation the disturbances as well as to stabilize the system effectively in presence of uncertainties. Design/methodology/approach This research work investigates the disturbances rejection algorithm for fixed-wing unmanned aerial vehicle. An algorithm based on SMC is introduced for disturbances rejection. Two types of disturbances are considered, the constant disturbance and the sinusoidal disturbance. The comprehensive lateral and longitudinal models of the system are presented. Two types of dynamics, the dynamics without disturbance and the new dynamics with disturbance, are presented. An observer-based algorithm is presented for the estimation of the dynamics with disturbances. Intensive simulations and experiments have been performed; the results not only guarantee the robustness and stability of the system but the effectiveness of the proposed algorithm as well. Findings In previous research work, new dynamics based on disturbances rejection are not investigated in detail; in this research work both the lateral and longitudinal dynamics with different disturbances are investigated. Practical implications As the stability is always important for flight, so the algorithm proposed in this research guarantees the robustness and rejection of disturbances, which plays a vital role in practical life for avoiding any kind of damage. Originality/value In the previous research work, new dynamics based on disturbances rejection are not investigated in detail; in this research work both the lateral and longitudinal dynamics with different disturbances are investigated. An observer-based SMC not only approximates the different disturbances and also these disturbances are rejected in order to guarantee the effectiveness and robustness.

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“…Over the past several decades, unmanned aerial vehicles (UAVs) have proved their potential in several applications by using their several capabilities, inter alia, continuous and persistent surveillance, eliminating the need of aircrew, image processing capabilities by using relatively cheap sensors, and decreasing the size and weight of the aerial vehicle when compared to a conventional aircraft. UAVs have been used in a variety of civilian applications; some of which are disaster rescue [1], agricultural monitoring [2], wildlife protection [3], infrastructure inspection [4], 3D environment reconstruction [5], and person following [6].…”

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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Nonlinear Controller of Quadcopters for Agricultural Monitoring

Cited by 22 publications

References 11 publications

Real Time Optimal Tuning of Quadcopter Attitude Controller Using Particle Swarm Optimization

Real Time Optimal Tuning of Quadcopter Attitude Controller Using Particle Swarm Optimization

Disturbances rejection based on sliding mode control

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

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