Comparison Analysis between PID and Fuzzy Logic Controllers for Quadrotor in a Simulated and in a Real Environment

Favarim, Fábio; Brito, Robison Cris; Linares, Kathya Silvia Collazos; Todt, Eduardo

doi:10.1109/lars/sbr/wre.2018.00092

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…Unmanned Aerial Vehicles (UAVs) have been widely applied in recent decade with their characteristics of simple structure, light weight, easy to carry and strong reliability, which has become a research hotspot [1,2]. However, the controller design of the UAV is highly challenging due to strong coupling and nonlinearities of the dynamic control system [3,4].…”

Section: Introductionmentioning

confidence: 99%

UAV based adaptive trajectory tracking control with input saturation and unknown time‐varying disturbances

Zhou

Tian

Lin

2022

IET Intelligent Trans Sys

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This paper proposes an adaptive control method for an underactuated complex nonlinear system, that is, unmanned aerial vehicle (UAV), to realize high‐precision trajectory tracking under input saturation constraint and other disturbances via artificial neural network and anti‐saturation auxiliary system. First, the rigid body motion theory is applied to establish a simplified nonlinear dynamic model of the UAV. Then a Lyapunov function is designed with the error surface to form the preliminary control law based on dynamic surface control. Afterwards, a radical basis function neural network is developed and used to estimate and compensate the disturbance, while the input saturation is also addressed via the designed anti‐saturation auxiliary system. Thus the stability and signal consistency of the closed‐loop UAV control system can be proved via the Lyapunov stability theory with appropriately tuned parameters. Simulation experiments are performed to verify that the proposed control algorithm can be used for UAV high‐precision trajectory tracking under time‐varying disturbances.

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Section: Introductionmentioning

confidence: 99%

UAV based adaptive trajectory tracking control with input saturation and unknown time‐varying disturbances

Zhou

Tian

Lin

2022

IET Intelligent Trans Sys

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“…The Unmanned Aerial Vehicle (UAV) has the characteristics of simple structure, light weight, easy to carry and strong reliability which has been widely used in diversified applicable fields, becoming a research hotspot [1]- [2]. However, owing to the high nonlinearity and strong coupling of the quadrotor system, the controller design of the UAV is highly challenging [3]- [4].…”

Section: Introductionmentioning

confidence: 99%

UAV adaptive trajectory tracking control under unknown time-varying disturbance and input saturation

Zhou¹,

Tian²

2021

Preprint

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In this paper, the flight control of the Unmanned aerial vehicle (UAV) is discussed with the proposed adaptive dynamic surface control method owing to its underactuated and non-linear characteristics. The proposed control algorithm is based on radial basis function (RBF) neural network and anti-saturation auxiliary system to realize high-precision trajectory tracking under time-varying disturbances and input saturation. First, the nonlinear dynamic model of the UAV with disturbances is established with the aid of rigid body motion theory. With the adoption of the dynamic surface control algorithm, the error surface and the Lyapunov function are defined to design the preliminary control law of the designed controller. Then the RBF neural network is introduced to estimate and compensate the disturbance. Further, an anti - saturation module is designed to tackle the problem of input saturation. By using the Lyapunov stability theory, it is proved that the stability and signal consistency of the closed-loop system are bounded, along with the constrained conditions of the control parameters. Simulation experiments have been performed and the results demonstrate that the proposed control algorithm has high-precision trajectory tracking ability and strong anti-disturbance capability under the input saturation constraint with high control performance.

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“…Quadrotor with PID controller can be modeled using MATLAB/Simulink and it useful for quadrotor researches [18,19]. PID controller also has better performance if compared with Linear Quadratic (LQ) controller since used for indoor quadrotor [20] although it is not more robust compared to FLC [21]. Lastly, Proportional Derivative Active Force Control (PDAFC) is  ISSN: 1693-6930 TELKOMNIKA Vol.…”

Section: Introductionmentioning

confidence: 99%

Low-cost quadrotor hardware design with PID control system as flight controller

Tahtawi¹,

Yusuf²

2019

TELKOMNIKA

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In designing an Unmanned Aerial Vehicle (UAV), such as quadrotor, sometimes an engineer should consider the required cost that is relatively expensive. As we know, quadrotor is one of robots that very usefull and has several advantages for human needs such as disaster area monitoring, air quality monitoring, area mapping, aerial photography, and surveillance. Thus, designing a rapid quadrotor with low-cost components and simple control system needs to be considered here. This paper presents design and implementation of a quadrotor using relatively low-cost components with Proportional Integral Derivative (PID) control system as its controller. The components used consist of microcontroller, Inertial Measurement Unit (IMU) sensor, Brushless Direct Current (BLDC) motor, Electronic Speed Control (ESC), remote control unit, battery, and frame. These components can be easily found in the electronic markets, especially in Indonesia. As an addition, this paper also describes PID control system as flight controller.A simple economic analysis is presented to clarify the cost in designing this quadrotor. Based on experimental testing result, the quadrotor able to fly stably with PID controller although there still overshoot at the attitude responses.

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Comparison Analysis between PID and Fuzzy Logic Controllers for Quadrotor in a Simulated and in a Real Environment

Cited by 5 publications

References 7 publications

UAV based adaptive trajectory tracking control with input saturation and unknown time‐varying disturbances

UAV based adaptive trajectory tracking control with input saturation and unknown time‐varying disturbances

UAV adaptive trajectory tracking control under unknown time-varying disturbance and input saturation

Low-cost quadrotor hardware design with PID control system as flight controller

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