首页|New Robotics Data Have Been Reported by Investigators at Northeastern University (Adaptive Super Twisting Observer-based Prescribed Time Integral Sliding Mode T racking Control of Uncertain Robotic Manipulators)
New Robotics Data Have Been Reported by Investigators at Northeastern University (Adaptive Super Twisting Observer-based Prescribed Time Integral Sliding Mode T racking Control of Uncertain Robotic Manipulators)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Ro botics. According to news reporting originating from Shenyang, People's Republic of China, by NewsRx correspondents, research stated, "A novel integral sliding mode control (ISMC) strategy combined with an adaptive super twisting observer ( ASTO) for an uncertain robotic manipulator tracking control system is presented in this article. The comprehensive uncertainties including both parameter pertur bations and external disturbances are considered during the controller design." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). Our news editors obtained a quote from the research from Northeastern University , "Firstly, a new nominal control law with prescribed time convergent property b ased on time varying scaling function is presented for the system without uncert ainties. Then this nominal control law constitutes the prescribed time convergen t sliding surface for ISMC. As the reaching phase is eliminated in ISMC, leading to the prescribed time stability of the whole control system without uncertaint ies. Secondly, take the system uncertainties (both the matched and unmatched unc ertainties) into consideration, two ASTOs are designed for handling them. So, th e lumped uncertainties of the robotic manipulator control system can be well est imated and compensated in finite time with the help of backstepping method. Besi des, the finite time convergent adaptive switching gains of the ASTO make the sy stem stable without knowing the bounds of the uncertainties exactly and suppress the chattering phenomenon of control input. Finally, the proposed control algor ithm is validated by simulation and experiment on a robotic manipulator."
ShenyangPeople's Republic of ChinaAs iaEmerging TechnologiesMachine LearningRoboticsRobotsNortheastern Univ ersity
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