Attitude Control of Quadrotor Using PD Plus Feedforward controller on SO(3)

Ginting, Almido Haryanto; Wahyunggoro, Oyas; Cahyadi, Adha Imam

doi:10.11591/ijece.v8i1.pp566-575

Cited by 6 publications

(5 citation statements)

References 6 publications

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“…Also, the body-fixed frame origin is located at the center of the mass of this vehicle. The model is represented by the rotation matrix ∈ (3) [13][14][15][16]. Consequently, the configuration manifold is the special euclidean group SE(3), which is the semi-direct product of ℝ 3 and the special orthogonal group SO(3).…”

Section: Quadrotor Dynamic Modelmentioning

confidence: 99%

“…In previous studies [12,13], a control approach that was designed by characterizing the geometric properties of nonlinear manifolds intrinsically. The geometric control techniques can completely avoid the singularities and ambiguities issues, this approach [14][15][16]. Furthermore, the flying environment's internal and external perturbations do not affect the durability of the mentioned control [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

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Geometric control of quadrotor UAVs using integral backstepping

Bouchaib

Taleb

Massoum

et al. 2021

IJEECS

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The traditional quadcopter control systems should deal with two common problems. Namely, the singularities related to the inverse kinematics and the ambiguity linked to the quaternion representation of the dynamic model. Moreover, the stability problem due to the system nonlinearity and high degree of coupling. This paper provides a solution to the two issues by employing a geometrical integral-backstepping control system. The integral terms were added to improve system ability to track desired trajectories. The high-level control laws are considered as a virtual control and transmitted to the low-level to track the high-level commands. The proposed control system along with the quadcopter dynamic model were expressed in the special Euclidean group SE(3). Finally, the control system robustness against mismatching parameters was studied while tracking various paths.

show abstract

Section: Quadrotor Dynamic Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Geometric control of quadrotor UAVs using integral backstepping

Bouchaib

Taleb

Massoum

et al. 2021

IJEECS

View full text Add to dashboard Cite

show abstract

“…There are various ways of expressing the motion dynamics, which mainly depend on the rotation parametrization. The most common attitude parametrizations are: Euler angles, axis-angle, rotation matrix [1], Rodrigues parameters [2] , and unit quaternion [3]. Euler angles are widely used to present the quadrotor's orientation, it is simple, unique, and can be easily understood.…”

Section: Introductionmentioning

confidence: 99%

A predictive sliding mode control for quadrotor’s trackingtrajectory subject to wind gusts and uncertainties

Meradi¹,

Benselama²,

Hedjar³

2022

IJECE

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<p>In this paper, a predictive sliding mode control (PSMC) strategy for the quadrotors tracking trajectory problem is proposed. This strategy aims to combine the advantages of sliding mode control (SMC) and non-linear model predictive control (NMPC) to improve the tracking control performance for quadrotors in terms of optimality, inputs/states constraints satisfaction, and strong robustness against disturbances. A comparative study of three popular controllers: the SMC, NMPC, and the integral backstepping control (IBC) is performed with different criteria. Accordingly, IBC and SMC show less computational time and strong robustness, while NMPC has minimum control effort. The discrete Dryden turbulence model is used as a benchmark model to represent the wind effect on the trajectory tracking accuracy. The effectiveness of the proposed method PSMC has been proven and compared with discrete-time slidingmode control (DSMC) and NMPC in several scenarios. Simulation results show that under both wind turbulence and time-variant uncertainties, the PSMC outperforms the other controllers by providing simultaneously disturbance rejection and guarantee that the control inputs are within bounded constraints.</p>

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“…In recent years, scientists and researchers have developed multiple control strategies to enhance and improve the stability and movement of unmanned aerial systems [6], [7]. Most of these methods involve the use of conventional PID controllers [8]- [11] which are easy to implement. However, they are not recommended for applications requiring precision during maneuvers such as inspection drones carrying on-board cameras.…”

Section: Introductionmentioning

confidence: 99%

Flight parameters improvement for an unmanned aerial vehicle using a lookup table based fuzzy PID controller

Benbouali

Chabni

Taleb

et al. 2021

IJEECS

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In this paper, a control scheme based on lookup table fuzzy proportionalintegral-derivate (PID) controller for the quadrotor unmanned aerial vehicle (UAV) movement control is proposed. This type of control provides enhanced quadrotor movement control beyond what can be achieved with conventional controllers and has a less computational burden on the processor. The proposed control scheme uses three lookup table based fuzzy logic controllers to control the different movement ranges of a quadrotor (i.e. roll, pitch, and yaw) to achieve stability. The mathematical model of a quadrotor, used to design the proposed controller, is derived based on the Lagrange approach. The processor in the loop (PIL) technique was used to test and validate the proposed control scheme. MATLAB/Simulink environment was used as a platform for the quadrotor model, whereas a low cost and high-performance STM32F407 microcontroller was used to implement the controllers. Data transfer between the hardware and software is via serial communication converter. The control system designed based on simulation is tested and validated using “processor in the loop” techniques.

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Attitude Control of Quadrotor Using PD Plus Feedforward controller on SO(3)

Cited by 6 publications

References 6 publications

Geometric control of quadrotor UAVs using integral backstepping

Geometric control of quadrotor UAVs using integral backstepping

A predictive sliding mode control for quadrotor’s trackingtrajectory subject to wind gusts and uncertainties

Flight parameters improvement for an unmanned aerial vehicle using a lookup table based fuzzy PID controller

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