The Control Strategy of Manipulator Based on Fractional-Order Iterative Learning

Abstract

In order to improve the control accuracy between the joints of the manipulator for non-linear uncertain systems such as single-joint manipulators. The ${{{\text{D}}}^{\alpha }}$ type and ${\text{P}}{{{\text{D}}}^{\alpha }}$ type fractional order iterative learning control (ILC) strategies are proposed based on combining fractional order calculus and ILC. The experimental verification was carried out with the two-joint manipulator as the research object. The experimental results are as follows: the fractional order ILC is compared with the traditional ILC, in which the minimum value of the position error of the ${{{\text{D}}}^{\alpha }}$ type fractional order ILC is reduced by 0.00016 and 0.00111 rad compared with the traditional ${\text{D}}$ type ILC, and the ${\text{P}}{{{\text{D}}}^{\alpha }}$ type fractional order ILC compared with the traditional ${\text{PD}}$ type ILC, the minimum value of the position error is reduced by 0.00022 and 0.00024 rad respectively. It shows that the algorithm proposed in this paper can improve the tracking performance of the system and improve system control accuracy.