A Consumer Electronics-Enhanced UAV System for Agricultural Farm Tracking With Fuzzy SMO and Actuator Fault Detection Control Algorithms

• Published in: IEEE transactions on consumer electronics Vol. 71; no. 2; pp. 6910 - 6923
• Main Authors: Bilal, Hazrat, Aslam, Muhammad Shamrooz, Tian, Yibin, Ullah, Inam, Ayouni, Sarra, Vasilakos, Athanasios V.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: actuator fault detection; Actuators; agricultural farm tracking; Agricultural practices; Algorithms; Artificial intelligence; Attitude control; Autonomous aerial vehicles; Consumer electronics; Consumer electronics-enhanced UAV; Control algorithms; Coordinate transformations; Dynamical systems; Farms; Fault detection; Fault tolerance; Faults; H∞ based sliding mode control; Monitoring; Nonlinear dynamics; Quadrotors; SISO (control systems); T-S fuzzy network; Tracking systems; Trajectories; Trajectory; Trajectory tracking; Uncertainty; Unmanned aerial vehicles
• ISSN: 0098-3063; 1558-4127
• DOI: 10.1109/TCE.2025.3563993

The adoption of agricultural robots, or agrobots, has revolutionized modern farming operations, ranging from crop monitoring to automated harvesting, significantly boosting productivity. Motivated by the rapid advancements in agrobots and their integration into smart agricultural practices, this study proposes an autonomous trajectory tracking system for wheat farms using quadcopter UAVs. To address actuator fault detection, including stuck faults and partial loss of efficiency, a TSF-<inline-formula> <tex-math notation="LaTeX">H^{\infty } </tex-math></inline-formula>-SMO (Takagi-Sugeno Fuzzy-based <inline-formula> <tex-math notation="LaTeX">H^{\infty } </tex-math></inline-formula> Sliding Mode Observer) fault detection framework is introduced. The approach initiates with the derivation of a TSF (Takagi-Sugeno Fuzzy) attitude control model that integrates an uncertainty term, constructed from the original nonlinear dynamics of the UAV and approximated through local linear models at four equilibrium positions. An actuator fault model is subsequently integrated to develop a comprehensive TSF-UAV model, accounting for actuator faults. The TSF-<inline-formula> <tex-math notation="LaTeX">H^{\infty } </tex-math></inline-formula>-SMO is then designed using matrix coordinate transformation to enable precise fault detection. The fault detection capabilities of the TSF-<inline-formula> <tex-math notation="LaTeX">H^{\infty } </tex-math></inline-formula>-SMO are evaluated through simulations on the TSF-UAV model under SISO (single-input single-output) actuator fault scenarios. The experimental results validate the proposed system, demonstrating its ability to detect a range of actuator faults accurately and promptly. The analysis reveals a proportional relationship between the amplitude of the state change and the severity of the fault, attributed to the interaction between system states and actuator flaps. This approach underscores the potential for deploying autonomous UAV-based fault detection and trajectory tracking systems in agricultural applications. Furthermore, integrating such advanced fault-tolerant control algorithms holds promise for consumer technology applications, where precision, reliability, and robustness are critical to enhancing system performance and operational efficiency.