首页|Reports Summarize Robotics and Automation Study Results from Texas A& M University (Improving Disturbance Estimation and Suppression Via Learning Amon g Systems With Mismatched Dynamics)
Reports Summarize Robotics and Automation Study Results from Texas A& M University (Improving Disturbance Estimation and Suppression Via Learning Amon g Systems With Mismatched Dynamics)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in Robotic s - Robotics and Automation. According to news reporting from College Station, T exas, by NewsRx journalists, research stated, “Iterative learning control (ILC) is a method for reducing system tracking or estimation errors over multiple iter ations by using information from past iterations. The disturbance observer (DOB) is used to estimate and mitigate disturbances within the system, while the syst em is being affected by them.” Financial support for this research came from National Science Foundation (NSF). The news correspondents obtained a quote from the research from Texas A& M University, “ILC enhances system performance by introducing a feedforward sign al in each iteration. However, its effectiveness may diminish if the conditions change during the iterations. On the other hand, although DOB effectively mitiga tes the effects of new disturbances, it cannot entirely eliminate them as it ope rates reactively. Therefore, neither ILC nor DOB alone can ensure sufficient rob ustness in challenging scenarios. This study focuses on the simultaneous utiliza tion of ILC and DOB to enhance system robustness. The proposed methodology speci fically targets dynamically different linearized systems performing repetitive t asks. The systems share similar forms but differ in dynamics (e.g. sizes, masses , and controllers). Consequently, the design of learning filters must account fo r these differences in dynamics. To validate the approach, the study establishes a theoretical framework for designing learning filters in conjunction with DOB. The validity of the framework is then confirmed through numerical studies and e xperimental tests conducted on unmanned aerial vehicles (UAVs). Although UAVs ar e nonlinear systems, the study employs a linearized controller as they operate i n proximity to the hover condition.”
College StationTexasUnited StatesN orth and Central AmericaRobotics and AutomationRoboticsTexas A& M University
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