CONTROL OF INDUSTRIAL ROBOTS USING PD ITERATIVE LEARNING TYPES USING SMART ONLINE LEARNING PARAMETERS

Tóm tắt

A robot motion system always depends on mathematical model parameters that are uncertain or not precisely known and are unavoidably affected by external disturbances. There are many traditional control methods, modern control methods, and intelligent control methods to handle this case, however, those control methods are all based on the uncertain robot mathematical model, which requires at least estimating those parameters or assuming the parameters are constant and uncertain. The article's content is to present a control method for moving robots to follow precise orbits without relying entirely on the model, which is an adaptive controller thanks to iterative learning types PD. Used smart online learning parameters. Simulation results for a two-degree-of-freedom manipulator have provided the required tracking quality, and after 50 trials, the output signal can track the signal set at both joints