首页|Findings from Tongji University Yields New Data on Robotics (Adaptive Approximat ion Tracking Control of a Continuum Robot With Uncertainty Disturbances)
Findings from Tongji University Yields New Data on Robotics (Adaptive Approximat ion Tracking Control of a Continuum Robot With Uncertainty Disturbances)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Robotics have been published.According to news reporting originating in Shanghai,People's R epublic of China,by NewsRx journalists,research stated,"Continuum robot has c ertain compliance and intrinsic safety,which makes it an excellent substitute f or the traditional rigid robot in the various tasks,such as human-robot interac tion or medical surgery.However,due to the complex nonlinearities which are in duced by the compliance,parameter uncertainties,and unknown disturbances,good performance control scheme for the continuum robot is always a challenging task for the practical applications." Financial support for this research came from National Natural Science Foundatio n of China (NSFC).The news reporters obtained a quote from the research from Tongji University,"T hus,to overcome this challenge of the uncertain dynamics and unknown external d isturbances,this article develops a novel adaptive control scheme for a continu um robot using the function approximation technique (FAT).Specifically,for the proposed continuum robot,an adaptive FAT control (AFATC) strategy with no upda te laws is proposed to handle the uncertain parameters of the robot dynamics and external disturbances.The control law is expressed as a finite linear combinat ion of the orthogonal basis functions by the FAT.The proposed AFATC scheme uses a fixed control structure,and the weight matrices are not updated in time.The n,the stability of the proposed controller is proved based on the Lyapunov func tion.Afterwards,the simulation results indicate the proposed AFATC scheme has good control performance compared with the regressor-free adaptive control (RFAC ) method."
ShanghaiPeople's Republic of ChinaAs iaEmerging TechnologiesMachine LearningRobotRoboticsTongji University
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