Hazrat Bilal;Muhammad Shamrooz Aslam;Yibin Tian;Inam Ullah;Sarra Ayouni;Athanasios V. Vasilakos
{"title":"基于模糊SMO和执行器故障检测控制算法的消费电子增强无人机农场跟踪系统","authors":"Hazrat Bilal;Muhammad Shamrooz Aslam;Yibin Tian;Inam Ullah;Sarra Ayouni;Athanasios V. Vasilakos","doi":"10.1109/TCE.2025.3563993","DOIUrl":null,"url":null,"abstract":"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>$H^{\\infty }$ </tex-math></inline-formula>-SMO (Takagi-Sugeno Fuzzy-based <inline-formula> <tex-math>$H^{\\infty }$ </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>$H^{\\infty }$ </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>$H^{\\infty }$ </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.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 2","pages":"6910-6923"},"PeriodicalIF":10.9000,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Consumer Electronics-Enhanced UAV System for Agricultural Farm Tracking With Fuzzy SMO and Actuator Fault Detection Control Algorithms\",\"authors\":\"Hazrat Bilal;Muhammad Shamrooz Aslam;Yibin Tian;Inam Ullah;Sarra Ayouni;Athanasios V. Vasilakos\",\"doi\":\"10.1109/TCE.2025.3563993\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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>$H^{\\\\infty }$ </tex-math></inline-formula>-SMO (Takagi-Sugeno Fuzzy-based <inline-formula> <tex-math>$H^{\\\\infty }$ </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>$H^{\\\\infty }$ </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>$H^{\\\\infty }$ </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. 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A Consumer Electronics-Enhanced UAV System for Agricultural Farm Tracking With Fuzzy SMO and Actuator Fault Detection Control Algorithms
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-$H^{\infty }$ -SMO (Takagi-Sugeno Fuzzy-based $H^{\infty }$ 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-$H^{\infty }$ -SMO is then designed using matrix coordinate transformation to enable precise fault detection. The fault detection capabilities of the TSF-$H^{\infty }$ -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.
期刊介绍:
The main focus for the IEEE Transactions on Consumer Electronics is the engineering and research aspects of the theory, design, construction, manufacture or end use of mass market electronics, systems, software and services for consumers.