Open Access

ARTICLE

Intelligent PID Control Method for Quadrotor UAV with Serial Humanoid Intelligence

by Linlin Zhang, Lvzhao Bai, Jianshu Liang, Zhiying Qin^*, Yuejing Zhao

School of Mechanical Engineering, Hebei University of Science and Technology, Shijiazhuang, 050018, China

* Corresponding Author: Zhiying Qin. Email:

Computer Systems Science and Engineering 2024, 48(6), 1557-1579. https://doi.org/10.32604/csse.2024.054237

Received 22 May 2024; Accepted 12 August 2024; Issue published 22 November 2024

Abstract

Quadrotor unmanned aerial vehicles (UAVs) are widely used in inspection, agriculture, express delivery, and other fields owing to their low cost and high flexibility. However, the current UAV control system has shortcomings such as poor control accuracy and weak anti-interference ability to a certain extent. To address the control problem of a four-rotor UAV, we propose a method to enhance the controller’s accuracy by considering underactuated dynamics, nonlinearities, and external disturbances. A mathematical model is constructed based on the flight principles of the quadrotor UAV. We develop a control algorithm that combines humanoid intelligence with a cascade Proportional-Integral-Derivative (PID) approach. This algorithm incorporates the rate of change of the error into the inputs of the cascade PID controller, uses both the error and its rate of change as characteristic variables of the UAV’s control system, and employs a hyperbolic tangent function to improve the outer-loop control. The result is a double closed-loop intelligent PID (DCLIPID) control algorithm. Through MATLAB numerical simulation tests, it is found that the DCLIPID algorithm reduces the rise time by 0.5 s and the number of oscillations by 2 times compared to the string PID algorithm when a unit step signal is used as input. A UAV flight test was designed for comparison with the serial PID algorithm, and it was found that when the UAV planned the trajectory autonomously, the errors in the X-, Y-, and Z-directions were reduced by 0.22, 0.21, and 0.31 m, respectively. Under the interference environment of artificial wind about 3.6 m·s^-1, the UAV hovering error in X-, Y-, and Z-directions are 0.24, 0.42, and 0.27 m, respectively. The simulation and experimental results show that the control method of humanoid intelligence and cascade PID can improve the real-time, control accuracy and anti-interference ability of the UAV, and the method has a certain reference value for the research in the field of UAV control.

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Keywords

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