Design of Disaster Four-rotor Drone Control System Based on Fuzzy Self-turning PID Control
DOI:
https://doi.org/10.62051/34pvep07Keywords:
complex situation; fuzzy PID control; disaster drone; fuzzy rules.Abstract
Disasters often manifest as multifaceted crises that pose significant challenges for emergency responders, particularly when accessing hazardous environments such as fire scenes or chemically contaminated areas. The complexity of these situations necessitates the exploration of innovative solutions to augment the efficacy of rescue operations. In this context, the present study introduces a novel approach through the development of a disaster drone control system, predicated on the principles of fuzzy self-tuning control. This paper delineates the conceptual framework and architectural design of a fuzzy self-tuning Proportional-Integral-Derivative (PID) controller. It leverages fuzzy logic to enhance the precision of control actions by employing fuzzy rules, thereby facilitating a comparative analysis between traditional adaptive controllers and their fuzzy counterparts. Furthermore, this study embarks on a comprehensive examination of the current landscape, practical applications, and prospective advancements in the realm of disaster robotics. The findings underscore the superior accuracy and efficiency of the self-tuning PID control mechanism, which significantly expedites the identification of victims. Consequently, the deployment of disaster drones not only streamlines rescue operations by curtailing time and financial expenditures but also contributes to the broader objective of making rescue missions more manageable and effective.
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Islam A K M Z, Hanna D, Ferworn A. Volunteer drone: Search and rescue of the industrial building collapsed worker. 4th International Conference on Wireless, Intelligent and Distributed Environment for Communication: WIDECOM 2021. Cham: Springer International Publishing, 2022: 99-110.
Surmann H, Daun K, Schnaubelt M, et al. Lessons from robot‐assisted disaster response deployments by the German Rescue Robotics Center task force. Journal of Field Robotics, 2023.
Franci, F. The Use of Satellite Remote Sensing for Flood Risk Management. Ph.D. Thesis, ALMADL University of Bologna Digital Library, Bologna, Italy, 2015.
Kuswadi S, Adji S I, Sigit R, et al. Disaster swarm robot development: On going project. 2017 International Conference on Electrical Engineering and Informatics (ICELTICs). IEEE, 2017: 45-50.
Couceiro M S, Portugal D, Rocha R P, et al. Fostering human–robot cooperative architectures for search and rescue missions in urban fires. Simulation, 2021, 97(3): 177-194.
Gomez F, Miikkulainen R. Incremental evolution of complex general behavior[J]. Adaptive Behavior, 1997, 5(3-4): 317-342.
Petrlik M, Petracek P, Kratky V, et al. UAVs beneath the surface: Cooperative autonomy for subterranean search and rescue in DARPA SubT. arXiv preprint arXiv:2206.08185, 2022.
Lee S, Har D, Kum D. Drone-assisted disaster management: Finding victims via infrared camera and lidar sensor fusion. 2016 3rd Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE). IEEE, 2016: 84-89.
Daud S M S M, Yusof M Y P M, Heo C C, et al. Applications of drone in disaster management: A scoping review. Science & Justice, 2022, 62(1): 30-42.
Bai Y, Wang D. Fundamentals of fuzzy logic control—fuzzy sets, fuzzy rules and defuzzifications. Advanced fuzzy logic technologies in industrial applications, 2006: 17-36.
Zile M. Intelligent and adaptive control. Microgrid Architectures, Control and Protection Methods, 2020: 423-446.
Al-Amin M, Islam M S. Design of an intelligent temperature controller of furnace system using the fuzzy self-tuning PID controller. 2021 International Conference on Electronics, Communications and Information Technology (ICECIT). IEEE, 2021: 1-4.
Chen Chao, Tang Jian, Zhan Zu-guang. A Path Planning Algoritihm for Seeing Eye Robots Based on V-Graph. Mechanical Science and Technology for Aerospace,2014,33(4): 490-495.
Sheng G, Gao G. Research on the attitude control of civil quad-rotor UAV based on fuzzy PID control. 2019 Chinese Control And Decision Conference (CCDC). IEEE, 2019: 4566-4569.
Aslanov V, Kruglov G, Yudintsev V. Newton–Euler equations of multibody systems with changing structures for space applications. Acta Astronautica, 2011, 68(11-12): 2080-2087.
Thamma M, Homchat K. Real-time implementation of self-tuning fuzzy PID controller for FOPDT system base on microcontroller STM32. 2017 2nd International Conference on Control and Robotics Engineering (ICCRE). IEEE, 2017: 130-134.
Ramanathan P, Sukanya K C, Mishra S, et al. Study on Fuzzy Logic and PID Controller for temperature regulation of a system with time delay. 2013 International Conference on Energy Efficient Technologies for Sustainability. IEEE, 2013: 274-277.
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