Nonlinear identification and optimal feedforward friction compensation for a motion platform
Embargo Lift Date: 2020-12-09
Güç, Ahmet Furkan
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We present a method of nonlinear identiﬁcation and optimal feedforward friction compensation procedure for an industrial single degree of freedom motion platform. The platform suﬀers from nonlinear dynamic eﬀects, such as friction and backlash in the driveline, along with precise reference tracking requirements. In order to eliminate the nonlinear dynamic eﬀects and obtain precise reference tracking, we ﬁrst identiﬁed the system using nonparametric identiﬁcation with Best Linear Approximation (BLA). Next, the feedback controller is implemented as a classical PI controller and it is designed using loop shaping techniques so that the system meets the linear system requirements. Then, we identiﬁed the nonlinear dynamics of the platform using Higher Order Sinusoidal Input Describing Function (HOSIDF) based system identiﬁcation and we present optimal feedforward compensation design to improve reference tracking performance. We modeled the friction characteristics using the Stribeck friction model and identiﬁed through a procedure with a special reference signal and the Nelder-Mead algorithm. Results indicate that the RMS trajectory error decreased from 0.0431 deg/s to 0.0117 deg/s, and standart deviation of speed reference error integral decreased from 0.0382 deg to 0.0051 deg, when the proposed nonlinear identiﬁcation and friction compensation method is used.