首页|Findings in Robotics and Automation Reported from Northeastern University (Onlin e Incremental Dynamic Modeling Using Physicsinformed Long Short-term Memory Net works for the Pneumatic Artificial Muscle)
Findings in Robotics and Automation Reported from Northeastern University (Onlin e Incremental Dynamic Modeling Using Physicsinformed Long Short-term Memory Net works for the Pneumatic Artificial Muscle)
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2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Robotics - Ro botics and Automation have been published. According to news reporting out of Sh enyang, People's Republic of China, by NewsRx editors, research stated, "The pne umatic artificial muscle (PAM) is widely applied in various scenarios due to the ir compliance and high-efficiency characteristics. However, the online modeling method which can accommodate online data remains an unresolved issue when data c annot be obtained off-line." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). Our news journalists obtained a quote from the research from Northeastern Univer sity, "This letter proposes an online incremental modeling method based on the p hysics-informed LSTM (PI-LSTM) architecture. The modified three-element model is regarded as the physics knowledge, and integrated into the PI-LSTM architecture , enabling the representation of physical constraints through neural networks. S ubsequently, the elastic weight consolidation (EWC) method is utilized to combin e the online operational data with the offline PI-LSTM model, allowing the model to be updated using the online data. Finally, online dynamic modeling experimen ts conducted on PAMs under different loads and driving conditions demonstrate th e precision of the proposed method."
ShenyangPeople's Republic of ChinaAs iaRobotics and AutomationRoboticsNortheastern University
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