Sampled Characteristic Modeling and Forgetting Gradient Learning Algorithm for Robot Servo Systems

Abstract

Servo systems of robotic exhibit nonlinear coupling with multidimensional characteristics, which poses a challenge to existing modeling and identification techniques. According to a kind of robot servo system which runs repetitively operations over a prespecified finite time interval, a low-order sampling characteristic modeling method is derived in this work. Characteristic parameters are allowed to vary from both time axis and iteration one; the forgetting gradient learning algorithm is utilized to estimate characteristic parameters. Furthermore, the effectiveness of the proposed algorithms is proved via theoretical analyses and numerical simulations.