首页|Shenzhen University Researchers Discuss Research in Robotics (Enhancing Trajecto ry Tracking and Vibration Control of Flexible Robots With Hybrid Fuzzy ADRC and Input Shaping)
Shenzhen University Researchers Discuss Research in Robotics (Enhancing Trajecto ry Tracking and Vibration Control of Flexible Robots With Hybrid Fuzzy ADRC and Input Shaping)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews-New research on robotics is the subject of a new report. According to news originating fromShenzhen, People's Republic of China, by NewsRx correspondents, research stated, "Flexible robot systemsare importan t in a variety of academic and industrial settings. Introducing innovative ideas for improvingtheir performance is always valuable due to their wide range of p ractical applications."Funders for this research include Middle East University, Jordan; National Natur al Science Foundationof China; Guangdong Province Basic And Applied Research Ma jor Project Fund; Shenzhen University2035 Program For Excellent Research; Equip ment Development Project of Shenzhen University.The news reporters obtained a quote from the research from Shenzhen University: "Compared totraditional methods, the utilization of soft computing techniques c an improve the overall performance bypredicting and optimizing the outcomes. Th is research proposes a hybrid control method that combinesinput shaping with fu zzy active disturbance rejection control for a flexible joint robotic manipulato rwith unknown perturbations and parametric uncertainties. The suggested algorit hm's control objectiveis to adapt and learn in different real-world scenarios t o accurately follow the required trajectories whiledampening the system's vibra tions. Overshoot is reduced, and reaction time is increased by employing aninpu t shaping approach, while the extended state observer is built to handle unexpec ted perturbations anduncertainties in parameters. Additionally, fuzzy logic adj usts the linear feedback control law gains onlineto boost the control system's dynamic capability. Compared with active disturbance rejection controller,inter val type-2 fuzzy logic controller, input shaping-active disturbance rejection co ntroller, modified linearactive disturbance rejection controller, genetic algor ithm-fuzzy logic controller, and fuzzy-tuned PID, theexperimental results indic ate that the hybrid input shaping enhanced fuzzy based active disturbance rejection controller control law is efficient and resilient."
Shenzhen UniversityShenzhenPeople's Republic of ChinaAsiaEmerging TechnologiesFuzzy LogicMachine LearningN ano-robotRobotics
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