首页|Faculty of Electrical Engineering Researcher Targets Robotics (Improving the Qua lity of Industrial Robot Control Using an Iterative Learning Method with Online Optimal Learning and Intelligent Online Learning Function Parameters)
Faculty of Electrical Engineering Researcher Targets Robotics (Improving the Qua lity of Industrial Robot Control Using an Iterative Learning Method with Online Optimal Learning and Intelligent Online Learning Function Parameters)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New study results on robotics have been published . According to news reporting originating from Hanoi, Vietnam, by NewsRx corresp ondents, research stated, "It is inevitable that the characteristics of a robot system change inaccurately or cannot be accurately determined during movement an d are affected by external disturbances." Our news reporters obtained a quote from the research from Faculty of Electrical Engineering: "There are many adaptive control methods, such as the exact linear ization method, sliding control, or neural control, to improve the quality of tr ajectory tracking for a robot's motion system. However, those methods require a great deal of computation to solve the constrained nonlinear optimization proble m. This article first presents some techniques for determining the online learni ng function parameters of an intelligent controller, including two circuits: the inner circuit is an uncertain function component estimator to compensate for th e robot's input, and the outer circuit is an iterative learning controller and d oes not use a mathematical model of the robot with optimal online learning funct ion parameters. The optimal condition is based on the model in the time domain t o determine the learning function parameters that change adaptively according to the sum of squared tracking errors of each loop. As for the intelligent online learning function parameters, they closely follow the general model to stabilize the robot system, based on the principle of intelligent estimation of the uncer tainty component and total noise. This method is built on Taylor series analysis for the state vector and does not use a mathematical model of the system at all ."
Faculty of Electrical EngineeringHanoiVietnamAsiaEmerging TechnologiesMachine LearningMathematicsRobotRo botics
NETL
NSTL
