Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (12): 2806-2815.doi: 10.13229/j.cnki.jdxbgxb20210479

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Model predictive control algorithm for rigid⁃flexible coupling positioning stage

Zhi-jun YANG1(),Zhong-yi GAO1,Li-jun WANG2,Guan-xin HUANG1(),Yu-tai WEI1   

  1. 1.State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment,Guangdong University of Technology,Guangzhou 510006,China
    2.Key Laboratory of Industrial Process Knowledge Automation,Ministry of Education,University of Science and Technology Beijing,Beijing 100083,China
  • Received:2021-05-31 Online:2022-12-01 Published:2022-12-08
  • Contact: Guan-xin HUANG E-mail:yangzj@gdut.edu.cn;guanxinhuang@gdut.edu.cn

Abstract:

For the feature that the RFCS has different models in motion and positioning stage, an extended state observer assisted model predictive control (ESO-MPC) method was proposed. The time-domain discrete difference equation was used to predict the dynamic response of RFCS, and an adjustable parameter during the time-domain discretization process was introduced to deal with uncertain factors of the experimental model. The feedback information was obtained through the Expanded State Observer (ESO), which can observe the position and speed of the RFCS as well as the spring and damping forces of the flexible hinge in real time. The performance of traditional PID, feedforward PID, LADRC and ESO-MPC was compared by 25 sets of RFCS point-to-point experiments and load experiments. The experimental results show that the four control schemes can achieve a steady-state error of ±0.1 μm, and ESO-MPC has the smallest setting time and the robustness of ESO-MPC is verified by load experiment.

Key words: mechatronic engineering, rigid-flexible coupling positioning stage, model predictive control, extended state observer

CLC Number: 

  • TP273

Fig.1

Structure of RFCS"

Fig.2

Dynamics model of RFCS"

Fig.3

Block diagram of the ESO-MPC system"

Fig.4

Identification output signal"

Fig.5

Frequency domain characteristic curve of the system"

Fig.6

Experimental device diagram"

Fig.7

Experimental setup connection"

Fig.8

S-curve motion planning"

Table 1

S-curve motion planning parameter"

参数定义
Jmax/(m·s-3最大急动度126.45
Amax/(m·s-2最大加速度8.11
Vmax/(m·s-1最大速度0.78
Q/m行程0.15

Table 2

Control parameter"

PID前馈PIDLADRCESO-MPC
kP=3.2×104kP=3.2×104ωc=800α=900
kI=2.0×106kI=2.8×106ωo=1000m=1.4
kD=16kD=17ωo=1000

Table 3

Comparison of setting time of different control algorithms"

控制算法最小值/ms最大值/ms平均值/ms众数/ms
PID30.644.534.3133.0
前馈PID25.935.831.5031.5
LADRC5.344.627.6335.8
ESO-MPC0.429.514.2329.4

Fig.9

Comparison of displacement curve"

Fig.10

Comparison of error curve"

Table 4

ESO-MPC control parameter"

负载质量/kgαωcm
190010002.4
290010003.4
390010004.4
490010005.4

Fig.11

Load experiment graph"

Fig.12

Load response curve"

Table 5

Comparison of setting time of different load"

负载质量/kg最小值/ms最大值/ms平均值/ms标准差
17.318.610.280.003 591 0
28.522.215.760.005 224 0
320.522.921.800.000 726 0
430.233.031.520.000 796 9
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