Achieving stable trajectory tracking in complex environments using an adaptive PID control strategy-based quadcopter drone

Research Article
Open access

Achieving stable trajectory tracking in complex environments using an adaptive PID control strategy-based quadcopter drone

Xinran Yuan 1*
  • 1 Beijing University of Posts and Telecommunications    
  • *corresponding author buptyxr@bupt.edu.cn
Published on 4 February 2024 | https://doi.org/10.54254/2755-2721/34/20230330
ACE Vol.34
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-293-0
ISBN (Online): 978-1-83558-294-7

Abstract

Stable trajectory tracking of unmanned aerial vehicles (UAVs) in complex environments is of paramount importance for achieving high precision and robustness in flight missions. In this paper, we address the attitude control problem of quadrotor UAVs and propose an optimization method based on adaptive PID control strategy. This text first presents an overview of the current status of UAVs both domestically and internationally, followed by the establishment of a mathematical model for quadrotor UAVs. Next, analysing the application of the traditional PID algorithm in UAV attitude control and provide a detailed description of the principles behind genetic algorithms and simulated annealing algorithms, along with their application in optimizing PID parameters. Through simulation experiments conducted in strong wind conditions, we compare the performance of the traditional PID algorithm with the optimization algorithm in stable trajectory tracking tasks. The experimental results demonstrate that the optimization algorithm significantly enhances the flight stability and accuracy of UAVs. Finally, this text summarizes the research findings and provide an outlook on future development directions.

Keywords:

Quadrotor UAV, Stable Trajectory Tracking, Adaptive PID Control, Genetic Algorithm, Simulated Annealing Algorithm, Complex Environment

Yuan,X. (2024). Achieving stable trajectory tracking in complex environments using an adaptive PID control strategy-based quadcopter drone. Applied and Computational Engineering,34,212-225.
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References

[1]. Liu, X., Sang, C., Li, Y., You, B., & Liu, C. Patrol UAV based on PID algorithm. In Southeast University Chengxian College, Nanjing, Jiangsu, China.

[2]. Shao, L., Liao, F., Ding, L., Shu, W., & He, Z. PID simulation design of quadcopter drone flight control based on MATLAB. In School of Electrical and Information Engineering, Huaihua University, Huaihua, Hunan, China.

[3]. Cen, Z., Yue, X., Wang, L., Ling, K., Cheng, Z., & Lu, Y. Design and experimentation of UAV adaptive variable spraying system based on neural network PID. In College of Electronic Engineering, South China Agricultural University, Guangzhou, Guangdong, China.

[4]. Zhang, X., Yu, F., & Liu, C. Design of quadcopter drone based on fuzzy PID control. In (1. School of Information Business, Zhongyuan Institute of Technology, Zhengzhou 451191, China; 2. School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, China).

[5]. Wang Hongfei, Shi Yongkang. “ROI-Accelerated Autonomous and Precise Landing System for Unmanned Aerial Vehicles.” Modern Electronics Technique, 2023, 46(6): 85-90. DOI:10.16652/j.issn.1004-373x.2023.06.016.

[6]. Zhao Qi, Zhen Ziyang, Gong Huajun, et al. “Unmanned Aerial Vehicle Formation Control Based on Deep Reinforcement Learning.” Electron, Optoelectronics and Control, 2022, 29(10): 29-33, 63. DOI:10.3969/j.issn.1671-637X.2022.10.006.

[7]. Chen, Jundong. “Tuning PID Control Parameters for Quadrotor UAV Based on Improved Genetic Algorithm.” Modern Information Technology, 2023, 7(11), 175-178. DOI: 10.19850/j.cnki.2096-4706.2023.11.040.

[8]. Zhang, Yibo; Wu, Huanyu; Qi, Haoyu. “Optimal Positioning Model for UAV based on Simulated Annealing Algorithm.” China High-Tech Zone, 2019, (4), 50. DOI: 10.3969/j.issn.1671-4113.2019.04.040.

[9]. Li, Yan; Wu, Zhiming. “Grouping Method of Manufacturing Units Based on GA and SA.” Control and Decision, 2000, 15(6), 654-657. DOI: 10.3321/j.issn:1001-0920.2000.06.004.

[10]. Liu, Xinyu; Sang, Chen; Li, Yuanyuan, et al. “Patrolling UAV based on PID Algorithm.” Modern Information Technology, 2022, (20), 141-145, 151. DOI: 10.19850/j.cnki.2096-4706.2022.20.033.

Cite this article

Yuan,X. (2024). Achieving stable trajectory tracking in complex environments using an adaptive PID control strategy-based quadcopter drone. Applied and Computational Engineering,34,212-225.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation

ISBN：978-1-83558-293-0(Print) / 978-1-83558-294-7(Online)
Editor：Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.34
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Liu, X., Sang, C., Li, Y., You, B., & Liu, C. Patrol UAV based on PID algorithm. In Southeast University Chengxian College, Nanjing, Jiangsu, China.

[2]. Shao, L., Liao, F., Ding, L., Shu, W., & He, Z. PID simulation design of quadcopter drone flight control based on MATLAB. In School of Electrical and Information Engineering, Huaihua University, Huaihua, Hunan, China.

[3]. Cen, Z., Yue, X., Wang, L., Ling, K., Cheng, Z., & Lu, Y. Design and experimentation of UAV adaptive variable spraying system based on neural network PID. In College of Electronic Engineering, South China Agricultural University, Guangzhou, Guangdong, China.

[4]. Zhang, X., Yu, F., & Liu, C. Design of quadcopter drone based on fuzzy PID control. In (1. School of Information Business, Zhongyuan Institute of Technology, Zhengzhou 451191, China; 2. School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, China).

[5]. Wang Hongfei, Shi Yongkang. “ROI-Accelerated Autonomous and Precise Landing System for Unmanned Aerial Vehicles.” Modern Electronics Technique, 2023, 46(6): 85-90. DOI:10.16652/j.issn.1004-373x.2023.06.016.

[6]. Zhao Qi, Zhen Ziyang, Gong Huajun, et al. “Unmanned Aerial Vehicle Formation Control Based on Deep Reinforcement Learning.” Electron, Optoelectronics and Control, 2022, 29(10): 29-33, 63. DOI:10.3969/j.issn.1671-637X.2022.10.006.

[7]. Chen, Jundong. “Tuning PID Control Parameters for Quadrotor UAV Based on Improved Genetic Algorithm.” Modern Information Technology, 2023, 7(11), 175-178. DOI: 10.19850/j.cnki.2096-4706.2023.11.040.

[8]. Zhang, Yibo; Wu, Huanyu; Qi, Haoyu. “Optimal Positioning Model for UAV based on Simulated Annealing Algorithm.” China High-Tech Zone, 2019, (4), 50. DOI: 10.3969/j.issn.1671-4113.2019.04.040.

[9]. Li, Yan; Wu, Zhiming. “Grouping Method of Manufacturing Units Based on GA and SA.” Control and Decision, 2000, 15(6), 654-657. DOI: 10.3321/j.issn:1001-0920.2000.06.004.

[10]. Liu, Xinyu; Sang, Chen; Li, Yuanyuan, et al. “Patrolling UAV based on PID Algorithm.” Modern Information Technology, 2022, (20), 141-145, 151. DOI: 10.19850/j.cnki.2096-4706.2022.20.033.

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