Model derivation, attitude control and Kalman filter estimation of a quadcopter

Kaba, Aziz; Ermeydan, Ahmet; Kiyak, Emre

doi:10.1109/iceee2.2017.7935821

Cited by 13 publications

(5 citation statements)

References 9 publications

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“…Enhanced PD structure is based on the saturation of the integral term to overcome the anti wind-up. An attitude controller based on PD and KF is proposed in [22] for a quadrotor. The measurement and modelling errors are eliminated by KF and system states are controlled by the PD controller, respectively.…”

Section: Related Workmentioning

confidence: 99%

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A Comparative Study on The Tuning of The PID Flight Controllers Using Swarm Intelligence

Kaba

2020

IJAST

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QUAVs have some shortcomings in terms of nonlinearities, coupled dynamics, unstable open–loop characteristics, and they are prone to internal and external disturbances. Therefore, control problem of the QUAVs is still an open issue. Designed controllers based on the linear dynamics have limited operating ranges. Therefore, nonlinear dynamics of the QUAVs must be derived and used in the control problem. Although some advanced controllers are presented for QUAV control, PID controllers are the most employed, well–known controllers with the simple structure, ease of implementation, solid functionality and robustness amongst the variations up to a degree. In this paper, PID based controllers are proposed for the nonlinear attitude dynamics to overcome the control problem of the QUAVs. However, since optimality and tuning of the PID controllers are fuzzy because of trial and error approaches, swarm intelligence based meta-heuristic algorithms (ABC, ACO and PSO) are employed to optimize the PID coefficients. Results are compared in terms of transient analysis and MC analysis to cover the rise time, settling time, percentage overshoot, steady–state error for the former and stochastic fitness evaluation for the latter, respectively.

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Section: Related Workmentioning

confidence: 99%

“…Then, velocity and positions are calculated according to Eqs. (22)(23). Algorithm continues until the pre-defined conditions are satisfied.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

A Comparative Study on The Tuning of The PID Flight Controllers Using Swarm Intelligence

Kaba

2020

IJAST

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“…[16] has proposed a CF for estimating UAV attitude with small computational effort, however, CF requires high sensor accuracy, and the filtering performance decreases with increasing gyroscope drift error. [17] has proposed KF fusion of gyroscope and accelerometer data to calculate the optimal estimate to correct the output error recursively, but this method does not provide an accurate estimation of the yaw angle and does not apply to nonlinear systems such as UAVs. [18] has applied EKF to multi-axis vehicle attitude estimation, EKF is an expansion of the nonlinear equations into a first-order approximation using Taylor's formula, however, when the linearization assumption does not hold, it leads to a degradation of the filter performance.…”

Section: Introductionmentioning

confidence: 99%

Attitude Estimation of Quadrotor UAV Based on QUKF

Liang,

Yang,

Han

et al. 2023

IEEE Access

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Toward the quadcopter unmanned aerial vehicle (UAV) attitude measurement problem, to improve the accuracy of acquisition of vehicle attitude parameters, and ensure accuracy of subsequent attitude control, the Quaternion-based Unscented Kalman filter (QUKF) data fusion method is presented. This attitude measurement system uses STM32F103 as the central controller, MPU6050 with integrated accelerometer and gyroscope, and magnetometer HMC5883l as the measurement sensor. Coordinate Rotation Relationships for the Attitude Heading Reference System (AHRS) in Quaternions, combining Unscented Kalman Filter (UKF) to Fuse Low-Cost Attitude Measurement Systems, tracking estimation of the genuine attitude of the vehicle. High-precision sensor measurements as real values, by comparing with Extended Kalman Filter (EKF), Complementary Filter (CF), and genuine values to validate and analyze the effectiveness of the algorithm applied to the low-cost attitude measurement system. Experimental results show that the low-cost attitude measurement system using quaternions as state variables combined with UKF can accurately estimate attitude information, providing precise attitude information for subsequent attitude control of UAVs.INDEX TERMS Unscented Kalman filter, quaternion, quadrotor UAV, attitude estimation, data fusion.

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“…Due to the aforementioned issue, control of the quadrotor is an open problem in the literature. To address this challenge, some controller designs are developed and applied to the quadrotor control problem such as linear quadratic regulator (Rinaldi et al , 2013), proportional integral derivative type (PID) control (Kaba et al , 2017), model predictive control (Mehndiratta and Kayacan, 2017), adaptive control (Perez-Alcocer and Moreno-Valenzula, 2019), robust control (Bhargavapuri et al , 2019), feedback linearization control (Voos, 2009), fault-tolerant control (Caliskan and Hajiyev, 2016), sliding – mode control (Chen et al , 2016), backstepping control (Vries and Subbarao, 2010), H ∞ control (Raffo et al , 2010), fuzzy logic control (Amirkhani et al , 2016) and neural network control (Dierks and Jagannathan, 2010).…”

Section: Introductionmentioning

confidence: 99%

Improved PID rate control of a quadrotor with a convexity-based surrogated model

Kaba

2021

AEAT

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Purpose The purpose of this paper is to develop, extend and propose an improved proportional integral derivative (PID) rate control of a quadrotor unmanned aerial vehicle based on a convexity-based surrogated firefly algorithm. Design/methodology/approach An improved PID controller structure is proposed for the rate dynamics of the quadrotor. Optimality of the controller is ensured by a recent, simple yet efficient firefly optimization method. The hybrid structure is further enhanced with a convexity-based surrogated model function. Findings Monte Carlo, transient response, error metrics and histogram distribution analyzes are conducted to show the performance of the proposed controller. The performance of the proposed method is evaluated under various convex combination values to further investigate the effect of the proposed surrogated model. According to the results, the proposed method is capable of controlling the rate quadrotor dynamics with the steady-state error of 0.0023 (rad/s) for P, −0.0024 (rad/s) for Q and 0 (rad/s) for the R state, respectively. Also, the least mean objective value is achieved at = 0 value of convexity in Monte Carlo trials. Originality/value The originality of this paper is to propose an improved PID rate controller with a convexity-based surrogated firefly algorithm.

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Model derivation, attitude control and Kalman filter estimation of a quadcopter

Cited by 13 publications

References 9 publications

A Comparative Study on The Tuning of The PID Flight Controllers Using Swarm Intelligence

A Comparative Study on The Tuning of The PID Flight Controllers Using Swarm Intelligence

Attitude Estimation of Quadrotor UAV Based on QUKF

Improved PID rate control of a quadrotor with a convexity-based surrogated model

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