Advanced polynomial trajectory design for high precision control of flexible servo positioning systems

被引:1
作者
Bashash, Saeid [1 ]
机构
[1] San Jose State Univ, Dept Mech Engn, San Jose, CA 95192 USA
来源
PRECISION ENGINEERING-JOURNAL OF THE INTERNATIONAL SOCIETIES FOR PRECISION ENGINEERING AND NANOTECHNOLOGY | 2023年 / 84卷
关键词
Flexible actuators; Precision positioning; Polynomial trajectories; Feedforward control; LEARNING CONTROL; TRACKING;
D O I
10.1016/j.precisioneng.2023.07.006
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
This paper develops a new approach for the design of polynomial-based reference and feedforward control trajectories for the high precision control of flexible servo actuators. First, a robust frequency-domain controller is designed for reference tracking and disturbance rejection. This controller is designed to achieve a desirable loop shape with reasonable bandwidth and stability margins. Then, the polynomial trajectories are derived based on a simplified rigid body representation of the system. To avoid the excitation of the system's resonant modes, this paper proposes the design and implementation of a set of feedforward notch filters. We further investigate the main cause of tracking error resulted from the rigid body approximation, and propose an alternative model for the derivation of the polynomial input trajectories. This model accounts for the cumulative DC gain of the resonant modes ignored in the rigid body approximation. A method for deriving the new polynomial trajectories with the appropriate initial and final time conditions is developed and evaluated for a representative flexible servo system model. Simulation results indicate that the proposed scheme provides significant improvement in the performance of system compared to the conventional methods.
引用
收藏
页码:81 / 90
页数:10
相关论文
共 24 条
  • [21] Trapezoidal Motion Profile to Suppress Residual Vibration of Flexible Object Moved by Robot
    Yoon, Hyun Joong
    Chung, Seong Youb
    Kang, Han Sol
    Hwang, Myun Joong
    [J]. ELECTRONICS, 2019, 8 (01)
  • [22] Neural network based iterative learning control for magnetic shape memory alloy actuator with iteration-dependent uncertainties
    Yu, Yewei
    Zhang, Chen
    Cao, Wenjing
    Huang, Xiaoliang
    Zhang, Xiuyu
    Zhou, Miaolei
    [J]. MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2023, 187
  • [23] Neural-Network-Based Iterative Learning Control for Hysteresis in a Magnetic Shape Memory Alloy Actuator
    Yu, Yewei
    Zhang, Chen
    Wang, Yifan
    Zhou, Miaolei
    [J]. IEEE-ASME TRANSACTIONS ON MECHATRONICS, 2022, 27 (02) : 928 - 939
  • [24] Time-Varying Trajectory Tracking Boundary Control of a Flexible Rotation Beam Based on Servomechanism
    Zhao, Xuena
    Liu, Zhijie
    Zhang, Shuang
    Li, Qing
    [J]. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2022, 69 (09) : 9185 - 9195