Xie Yuanlong, Tang Xiaoqi, Song Bao, Zhou Xiangdong, Guo Yixuan
National NC System Engineering Research Center, State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science & Engineering, Huazhong University of Science & Technology, 1037 Luoyu Road, Wuhan, China.
National NC System Engineering Research Center, State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science & Engineering, Huazhong University of Science & Technology, 1037 Luoyu Road, Wuhan, China.
ISA Trans. 2018 Apr;75:172-188. doi: 10.1016/j.isatra.2018.02.018. Epub 2018 Feb 24.
In this paper, data-driven adaptive fractional order proportional integral (AFOPI) control is presented for permanent magnet synchronous motor (PMSM) servo system perturbed by measurement noise and data dropouts. The proposed method directly exploits the closed-loop process data for the AFOPI controller design under unknown noise distribution and data missing probability. Firstly, the proposed method constructs the AFOPI controller tuning problem as a parameter identification problem using the modified l norm virtual reference feedback tuning (VRFT). Then, iteratively reweighted least squares is integrated into the l norm VRFT to give a consistent compensation solution for the AFOPI controller. The measurement noise and data dropouts are estimated and eliminated by feedback compensation periodically, so that the AFOPI controller is updated online to accommodate the time-varying operating conditions. Moreover, the convergence and stability are guaranteed by mathematical analysis. Finally, the effectiveness of the proposed method is demonstrated both on simulations and experiments implemented on a practical PMSM servo system.
