Quad-Rotor UAV: High-Fidelity Modeling and Nonlinear PID Control

Milhim, Alaeddin Bani; Zhang, Youmin; Rabbath, C.A.

doi:10.2514/6.2010-8362

Cited by 8 publications

(7 citation statements)

References 2 publications

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“…Initially, the gains were set to zero and then slowly increased until the qua-drone's oscillation was critically damped; after that, the tuning process was repeated until the quadrotor flies in a stable manner. A practitioner's method in tuning qua-drone's PID gains using computational software was reported in [53][54][55]. Although the computational software seemed to result in optimized gains, the first flight crashed due to the incorrect sampling rate, and extensive tuning had to be performed again to fly the quadrotor at the following attempt.…”

Section: Overall Control Law Of Multivariable Qua-drones For Intelligent Optimal Pid Controller Tuningmentioning

confidence: 99%

Dynamic Decoupling and Intelligent Optimal PID Controller Tuning of Multivariable Qua-drones

Kim¹

2021

IJRTE

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This paper deals with dynamic decoupling and its intelligent PID control method of multivariable qua-drone. Up to this time, many sophisticated intelligent algorithms and control methods for drone systems have been mentioned. However, almost many cases have been focusing on single loop control methods and general multivariable systems. Therefore, we cannot guarantee its stability and optimal response by PID control used in multivariable qua-drone. Herein, this paper suggests a novel control method for PID control for multivariable qua-drone. As first step, this paper decouples dynamic of multivariable qua-drone using diagonal method of system matrix and then applies intelligent method PSO and GA to single loop obtained by decoupling method to obtain optimal response.

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Section: Overall Control Law Of Multivariable Qua-drones For Intelligent Optimal Pid Controller Tuningmentioning

confidence: 99%

Dynamic Decoupling and Intelligent Optimal PID Controller Tuning of Multivariable Qua-drones

Kim¹

2021

IJRTE

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“…where denote damping ratio; and denote natural oscillation angular frequency and damping oscillation angular frequency, respectively; and denote settling time and rise time, respectively; % denotes overshoot; set = 5 s, = 2%; it is easy to get = 0.7797, = 1.0035 rad/s. Substitute (16) into (15);the adjustable differential equation can be obtained:…”

Section: Mrac Law Designmentioning

confidence: 99%

“…In recent years, some advanced control theories are gradually introduced into the design of attitude control system for UAVs with the development of computer technology [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. In [1], the output feedback control method was used to design the attitude control system for UAV.…”

Section: Introductionmentioning

confidence: 99%

Design of Attitude Control System for UAV Based on Feedback Linearization and Adaptive Control

Zhou

Yin

Wang

et al. 2014

Mathematical Problems in Engineering

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Attitude dynamic model of unmanned aerial vehicles (UAVs) is multi-input multioutput (MIMO), strong coupling, and nonlinear. Model uncertainties and external gust disturbances should be considered during designing the attitude control system for UAVs. In this paper, feedback linearization and model reference adaptive control (MRAC) are integrated to design the attitude control system for a fixed wing UAV. First of all, the complicated attitude dynamic model is decoupled into three single-input single-output (SISO) channels by input-output feedback linearization. Secondly, the reference models are determined, respectively, according to the performance indexes of each channel. Subsequently, the adaptive control law is obtained using MRAC theory. In order to demonstrate the performance of attitude control system, the adaptive control law and the proportional-integral-derivative (PID) control law are, respectively, used in the coupling nonlinear simulation model. Simulation results indicate that the system performance indexes including maximum overshoot, settling time (2% error range), and rise time obtained by MRAC are better than those by PID. Moreover, MRAC system has stronger robustness with respect to the model uncertainties and gust disturbance.

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“…Rockwell Collins demonstrated a damage-tolerant control system for DARPA's Joint Unmanned Combat Aircraft system program using an unmanned F/A-18 subscale aircraft model with 60% of wing loss [21]. In this interesting work the all-attitude autopilot formulation was shown to enable aerobatic maneuvers and automatic recovery from unusual attitudes, while enforcing flow angle and load limits.…”

Section: Structure Damage-tolerant Uavsmentioning

confidence: 99%

“…These methods have been tested in Georgia Institute of Technology, and interesting results are obtained [6]. With reference to gain scheduling PID, a gain scheduling based PID controller is designed for a quadrotor UAV, at MIE of Concordia University [21]. In this work the controller in each control channel consists of two parts: the first part controls the orientation of the propeller, and the second part controls the speed of each propeller.…”

Section: Structure Damage-tolerant Uavsmentioning

confidence: 99%

A Review on Fault-Tolerant Control for Unmanned Aerial Vehicles (UAVs)

Sadeghzadeh¹,

Zhang²

2011

Infotech@Aerospace 2011

Self Cite

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The growing number of Unmanned Aerial Vehicles (UAVs) is considerable in the last decades. Many flight test scenarios, including single and multi-vehicle formation flights, are demonstrated using different control algorithms with different test platforms. In this paper, we present a brief literature review on the development and key issues of current researches in the field of Fault-Tolerant Control (FTC) applied to UAVs. It consists of various intelligent or hierarchical control architectures for a single vehicle or a group of UAVs in order to provide potential solutions for tolerance to the faults, failures or damages in relevant to UAV components during flight. Among various UAV test-bed structures, a sample of every class of UAVs, including single-rotor, quadrotor, and fixed-wing types, are selected and briefly illustrated. Also, a short description of terms, definitions, and classifications of fault-tolerant control systems (FTCS) is presented before the main contents of review.

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Quad-Rotor UAV: High-Fidelity Modeling and Nonlinear PID Control

Cited by 8 publications

References 2 publications

Dynamic Decoupling and Intelligent Optimal PID Controller Tuning of Multivariable Qua-drones

Dynamic Decoupling and Intelligent Optimal PID Controller Tuning of Multivariable Qua-drones

Design of Attitude Control System for UAV Based on Feedback Linearization and Adaptive Control

A Review on Fault-Tolerant Control for Unmanned Aerial Vehicles (UAVs)

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