Design of learning input shaping technique for residual vibration suppression in an industrial robot

Abstract

In this paper, a practical method is proposed to suppress residual vibrations of industrial robots without a real-time estimation of vibration frequencies. Through theoretical analysis and experiments, we designed an input shaping technique (IST) for the first three axes of a six-degrees-of-freedom industrial robot. Iterative learning IST (LIST) is applied to the first axis to suppress its time-varying nonlinear residual vibration, while conventional IST is applied to the second and third axes. Experimental results show that LIST can suppress residual vibrations to a level similar to that of a time-varying IST which requires complicated real-time estimation of a dynamic model. The LIST is an attractive method for suppression of nonlinear and time-varying residual vibrations in industrial robots which perform repetitive tasks because most industrial robots have limited computing power and memory space in their controllers.