Online Estimation and Control for Feed Drive Systems With Unmeasurable Parameter Variations

• Main Authors: Tiancheng Zhong, Ryozo Nagamune, Alexander Yuen, Wencheng Tang
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Extended-state-observer; feed drive systems; H∞ optimization; parameter estimation; state feedback control
• Online Access: https://ieeexplore.ieee.org/document/9000562/

This paper develops an online parameter estimation and control method for both rigid and flexible feed drive systems with unmeasurable parameter variations. The perturbations of the state-space model caused by the parameter variations are formulated, thereby making it possible to obtain the parameter variations in real-time by estimating the perturbations. To estimate the perturbations, they are regarded as the extended states and estimated through the extended-state-observer. With the estimation method, a novel state feedback control structure with double integrators is further proposed, which can eliminate the steady-state tracking error at constant velocities. The H$_\infty $ optimization technique is used to design the proposed state feedback controller. A simulation is conducted that integrates the estimator and the controller for a ball screw setup with mass-dependent resonant modes, where several proposed state feedback controllers are linearly interpolated into a gain-scheduling controller and scheduled by the estimated mass. The results demonstrate that the designed gain-scheduling state feedback controller outperforms a linear-time-invariant state feedback controller and an adaptive backstepping sliding mode controller. The proposed method is experimentally validated on a rigid linear-motor-driven motion stage, of which the results indicate the proposed estimation method can accurately estimate the parameter variations.