A fuzzy control algorithm for tracing air pollution based on unmanned aerial vehicles

• Published in: Journal of the Air & Waste Management Association (1995) Vol. 72; no. 10; pp. 1174 - 1190
• Main Authors: Jiang, Xinyan, Ding, Tao, He, Yuting, Cui, Xuelin, Liu, Zhenguo, Zhang, Zhenming
• Format: Journal Article
• Language: English
• Published: Pittsburgh Taylor & Francis 03.10.2022; Taylor & Francis Ltd
• Subjects: Air pollution; Algorithms; Control algorithms; Control systems design; Control theory; Controllers; Drones; Fuzzy control; Fuzzy logic; Odor; Plumes; Pollutants; Unmanned aerial vehicles
• ISSN: 1096-2247; 2162-2906; 2162-2906
• DOI: 10.1080/10962247.2022.2102093

The process of atmospheric pollutants traceability based on unmanned aerial vehicles (UAVs) is affected by many factors that can impact and increase the complexity of the traceability of atmospheric pollutants. In this study, we proposed a new algorithm called the fuzzy control traceability (FCT) to track odor plumes. Our proposed algorithm combined the characteristics and fuzzy control of the UAV and designed a controller based on the actual environment of the UAV. The fuzzy controller fuzzed the input gas concentration information, established fuzzy control rules by imitating human brain thinking, and outputted the turning angle and the move length according to rules, thus realizing intelligent tracking of the odor plume by the UAV. We compared the FCT algorithm with the bio-inspired "ZigZag" algorithm to validate its performance. Various concentration fields were constructed, and ten sets of experiments are performed using the two algorithms in different concentration fields. The average success rate of the FCT algorithm under different concentration fields was 95.4% higher than that of the ZigZag algorithm. Implications: Fuzzy control logic is applied to the field of air pollutant traceability of drones, and a single drone traceability algorithm based on fuzzy control is proposed; and in view of the shortcomings of a single traceability subject in the traceability, multiple traceability subjects are introduced to optimize fuzzy control.