The quadrotor MAV system using PID control

Duc, Minh Nguyen; Trong, Thang Nguyen; Xuan, Yang Sheng

doi:10.1109/icma.2015.7237537

Cited by 33 publications

(16 citation statements)

References 14 publications

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“…Proportional Integral Derivative (PID) controller is a classical controller that have proven to be robust and tremendously beneficial in many linear or non-linear applications. The PID design are pointed out in many references, such as [13], [22]- [25]. The controller attempts to minimize the error over time by adjustment of a control variable ( ).…”

Section: Pid Controlmentioning

confidence: 99%

Sensor Fusion for Attitude Estimation and PID Control of Quadrotor UAV

Noordin¹,

Basri²,

Mohamed³

2018

IJEETC

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This paper presents sensor fusion algorithm using nonlinear complementary filter (NCF) for attitude estimation and a proportional-integral-derivative (PID) controller for small-scale quadrotor unmanned aerial vehicle (UAV) stabilization. From inertial measurement unit (IMU) data, gyroscope as a main sensor is fused to another two sensors; accelerometer and magnetometer to correct drift error of gyroscope to obtain reliable attitude estimation. In this paper, the performance of NCF is implemented on real-time while applying PID controller for attitude stabilization of quadrotor UAV during hovering. Experimental results show the effectiveness of PID controller for quadrotor UAV stabilization during attitude control  .

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Section: Pid Controlmentioning

confidence: 99%

Sensor Fusion for Attitude Estimation and PID Control of Quadrotor UAV

Noordin¹,

Basri²,

Mohamed³

2018

IJEETC

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“…B. PID Control PID controller is a classical controller that has proven to be robust and tremendously beneficial in many linear or nonlinear applications. The PID design are pointed out in many references, such as [10][17] [18][19] [20]. The controller attempts to minimize the error over time by adjustment of a control variable u(t).…”

Section: A Quadrotor Modelmentioning

confidence: 99%

“…[26], and is random real-number between [0 1], the parameters and are the social coefficient and personal coefficient which is constant and typically set as two [22]. (19) The new position then is updated by (20) Stopping criteria used in this application is maximum iteration where, once the iteration reached the maximum value set, the simulation will stop and the best result obtained is display.…”

Section: A Quadrotor Modelmentioning

confidence: 99%

Modelling and PSO Fine-tuned PID Control of Quadrotor UAV

Noordin¹,

Basri²,

Mohamed³

et al. 2017

International Journal on Advanced Science, Engineering and Information Technology

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This paper describes nonlinear dynamics model of x-configuration quadrotor using Newton-Euler modelling technique. To stabilize quadrotor attitude (roll (ϕ), pitch (θ), yaw (ψ)) during hovering, a PID controller is proposed. There is individual PID controller for each roll, pitch, yaw and z where 12 parameters consist of kp, ki, and kd are fine-tuned using particle swarm optimization algorithms. From the simulation, the sum absolute error fitness function give the best optimize result where quadrotor achieve zero steady state error for hovering with 18.9% overshoot, and 4.42s settling time. Accordingly, for attitude stabilization, roll angle, pitch angle, and yaw angle converge to the set point, zero approximately with settling time 2.76s, 0.1s and 3.2s respectively.

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“…Numerous research studies have examined the attitude and position control design and implementation to retain satisfactory performance operation. Some of the several control algorithms proposed in the literature includes proportional integral derivative (PID) [10], [11], linear quadratic regulator (LQR) [8], [10], [12], sliding mode control [13], [14], H ∞ [15], backstepping control [9], fuzzy logic [16], and visual-based techniques [17]. Another interesting methodology by Chen and Sun in 2019 [18], which generally deals with the control of underactuated systems that can treat various system constraints, could be used to control quadrotors.…”

Section: Introductionmentioning

confidence: 99%

Novel Lyapunov-Based Autonomous Controllers for Quadrotors

2020

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In this paper, we look into the dynamic motion planning and control of an unmanned aerial vehicle, namely, the quadrotor, governed by its dynamical equations. It is shown for the first time that the Direct or the Second Method of Lyapunov is an effective tool to derive a set of continuous nonlinear control laws that not only provide smooth trajectories from a designated initial position to a designated target, but also continuously minimise the roll and pitch of the quadrotor en route to its targets. The latter successfully addresses the challenging problem of a quadrotor autonomously transporting valuable and fragile payloads safely to the designated target. Computer simulations are used to illustrate the effectiveness of the proposed control laws. INDEX TERMS Artificial potential field, Lyapunov-based control scheme, quadrotor, stability, UAV.

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The quadrotor MAV system using PID control

Cited by 33 publications

References 14 publications

Sensor Fusion for Attitude Estimation and PID Control of Quadrotor UAV

Sensor Fusion for Attitude Estimation and PID Control of Quadrotor UAV

Modelling and PSO Fine-tuned PID Control of Quadrotor UAV

Novel Lyapunov-Based Autonomous Controllers for Quadrotors

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