Deep Neural Network Input Shaper for Residual Vibration Suppression

被引：1
|
作者：
Zhang T. [1 ]
Kang Z. [1 ]
Zou Y. [1 ]
Liao C. [1 ]
机构：
[1] School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou
来源：
Huanan Ligong Daxue Xuebao/Journal of South China University of Technology (Natural Science) | 2021年 / 49卷 / 08期
关键词：
Forgetting factor; Multi-axis servo system; Neural network; Post input shaper; Recursive least square method;
D O I：
10.12141/j.issn.1000-565X.200690
中图分类号：
学科分类号：
摘要：
A widely applicable post-adaptive input shaper algorithm was proposed to solve the residual vibration of multi-axis servo system caused by the flexibility of the system in the emergency stop section of high-speed motion. The algorithm does not need to identify the system modal parameters and it is based on the recursive least square method (RLS). The residual vibration signal was used as the input of the algorithm to optimize the shaper coefficient vector with the best vibration suppression effect under the current trajectory. The adaptive forgetting factor updating algorithm was introduced to improve the tracking performance of the shaper in non-stationary environment. A full connection multilayer neural network model was established and trained by using multiple sets of excitation tra-jectory samples. It solves the problem of the original algorithm that the cost of time is significantly increased due to multiple changes of trajectory and reoptimization. The experimental results show that the residual vibration amplitude of the post adaptive input shaper with adaptive forgetting factor is reduced by 28.3% averagely and 36.9% maximally, and the convergence time of residual vibration is reduced by 28.4%, compared with the traditional post adaptive input shaper. The residual vibration amplitude of the input shaper predicted by the multilayer perceptron model is reduced by 21.6% averagely and 29.8% maximally, and the convergence time of residual vibration is reduced by 23.7%, compared with the ordinary post adaptive input shaper. The algorithm proposed in this paper has certain application significance for improving the positioning accuracy of multi-axis servo system and shortening the positioning waiting time. The introduction of multilayer neural network model improves the overall work efficiency under the condition that the expected trajectory changes frequently. © 2021, Editorial Department, Journal of South China University of Technology. All right reserved.
引用
收藏
页码：103 / 112
页数：9
相关论文
共 18 条
  • [1] SINGER N C, SEERING W P., Preshaping command inputs to reduce system vibration, Journal of Dynamic Systems Measurement and Control, 112, 1, pp. 76-82, (1990)
  • [2] SINGHOSE W, ELOUNDOU R, LAWRENCE J., Command generation for flexible systems by input shaping and command smoothing, Journal of Guidance Control & Dynamics, 33, 6, pp. 1697-1707, (2012)
  • [3] VAUGHAN J, YANO A, SINGHOSE W., Comparison of robust input shapers, Journal of Sound & Vibration, 315, 4, pp. 797-815, (2007)
  • [4] PEREIRA E, TRAPERO J R, DIAZ I M, Et al., Adaptive input shaping for manoeuvring flexible structures using an algebraic identification technique, Automatica, 45, 4, pp. 1046-1051, (2009)
  • [5] TZES A, YURKOVICH S., An adaptive input shaping control scheme for vibration suppression in slewing flexible structures, IEEE Transactions on Control Systems Technology, 1, 2, pp. 114-121, (2015)
  • [6] KHORRAMI F, JAIN S, TZES A., Experimental results on adaptive nonlinear control and input preshaping for multi-link flexible manipulators, Automatica, 31, 1, pp. 83-97, (1995)
  • [7] KOJIMA H, IEDA S, KASAI S., Frequency-tuning input-shaped manifold-based switching control for underactuated space robot equipped with flexible appendages, Acta Astronautica, 101, pp. 42-54, (2014)
  • [8] FASIH S M, MOHAMED Z, HUSAIN A R, Et al., Payload swing control of a tower crane using a neural network-based input shaper, Measurement and Control, 53, 7, pp. 1171-1182, (2020)
  • [9] CHANG P H, PARK H S., Time-varying input shaping technique applied to vibration reduction of an industrial robot, Control Engineering Practice, 13, 1, pp. 121-130, (2005)
  • [10] MAGEE D P, BOOK W J., Optimal filtering to minimize the elastic behavior in serial link manipulators, Proceedings of American Control Conference, (1998)
12