Full Text:
<4123>
CLC number: TP273.1
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2014-01-15
Cited: 5
Clicked: 10916
Model predictive control of servo motor driven constant pump hydraulic system in injection molding process based on neurodynamic optimization
| ||||||||||||||
Yong-gang Peng, Jun Wang, Wei Wei. Model predictive control of servo motor driven constant pump hydraulic system in injection molding process based on neurodynamic optimization[J]. Journal of Zhejiang University Science C, 2014, 15(2): 139-146.
@article{title="Model predictive control of servo motor driven constant pump hydraulic system in injection molding process based on neurodynamic optimization",
author="Yong-gang Peng, Jun Wang, Wei Wei",
journal="Journal of Zhejiang University Science C",
volume="15",
number="2",
pages="139-146",
year="2014",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C1300182"
}
%0 Journal Article
%T Model predictive control of servo motor driven constant pump hydraulic system in injection molding process based on neurodynamic optimization
%A Yong-gang Peng
%A Jun Wang
%A Wei Wei
%J Journal of Zhejiang University SCIENCE C
%V 15
%N 2
%P 139-146
%@ 1869-1951
%D 2014
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1300182
TY - JOUR
T1 - Model predictive control of servo motor driven constant pump hydraulic system in injection molding process based on neurodynamic optimization
A1 - Yong-gang Peng
A1 - Jun Wang
A1 - Wei Wei
J0 - Journal of Zhejiang University Science C
VL - 15
IS - 2
SP - 139
EP - 146
%@ 1869-1951
Y1 - 2014
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C1300182
Abstract: In view of the high energy consumption and low response speed of the traditional hydraulic system for an injection molding machine, a servo motor driven constant pump hydraulic system is designed for a precision injection molding process, which uses a servo motor, a constant pump, and a pressure sensor, instead of a common motor, a constant pump, a pressure proportion valve, and a flow proportion valve. A model predictive control strategy based on neurodynamic optimization is proposed to control this new hydraulic system in the injection molding process. Simulation results showed that this control method has good control precision and quick response.
[1]Akesson, B.M., Toivonen, H.T., 2006. A neural network model predictive controller. J. Process Contr., 16(9):937-946.
[2]Draeger, A., Engell, S., Ranke, H., 1995. Model predictive control using neural networks. IEEE Contr. Syst., 15(5):61-66.
[3]Dubay, R., 2002. Self-optimizing MPC of melt temperature in injection moulding. ISA Trans., 41(1):81-94.
[4]Gao, F., Patterson, W.I., Kamal, M.R., 1996. Cavity pressure dynamics and self-tuning control for filling and packing phases of thermoplastics injection molding. Polym. Eng. Sci., 36(9):1272-1285.
[5]Gao, F.R., Yang, Y., Shao, C., 2001. Robust iterative learning control with applications to injection molding process. Chem. Eng. Sci., 56(24):7025-7034.
[6]Hopfield, J.J., Tank, D.W., 1985. “Neural” computation of decisions in optimization problems. Biol. Cybern., 52(3):141-152.
[7]Hu, X.L., Wang, J., 2008. An improved dual neural network for solving a class of quadratic programming problems and its k-winners-take-all application. IEEE Tran. Neur. Networks, 19(12):2022-2031.
[8]Huang, S.N., Tan, K.K., Lee, T.H., 1999a. Predictive control of ram velocity in injection molding. Polym. Plast. Technol. Eng., 38(2):285-303.
[9]Huang, S.N., Tan, K.K., Lee, T.H., 1999b. Adaptive GPC control of melt temperature in injection moulding. ISA Trans., 38(4):361-373.
[10]Huang, S.N., Tan, K.K, Lee, T.H., 2004. Neural-network-based predictive learning control of ram velocity in injection molding. IEEE Trans. Syst. Man Cybern. C, 34(3):363-368.
[11]Kennedy, M.P., Chua, L.O., 1988. Neural networks for nonlinear programming. IEEE Trans. Circ. Syst., 35(5):554-562.
[12]Liao, J.H., Tsai, C.C., Chang, H.C., 1996. Adaptive generalized predictive PI control of injection molding processes. Proc. 4th Int. Conf. on Automation Technology, p.711-718.
[13]Liu, Q.S., Wang, J., 2008. A one-layer recurrent neural network with a discontinuous hard-limiting activation function for quadratic programming. IEEE Trans. Neur. Networks, 19(4):558-570.
[14]Liu, S.B., Wang, J., 2006. A simplified dual neural network for quadratic programming with its KWTA application. IEEE Trans. Neur. Networks, 17(6):1500-1510.
[15]Mahadevan, R., Doyle, F.J., 2003. Efficient optimization approaches to nonlinear model predictive control. Int. J. Robust Nonl. Contr., 13(3-4):309-329.
[16]Morari, M., Lee, J.H., 1999. Model predictive control: past, present and future. Comput. Chem. Eng., 23(4-5):667-682.
[17]Pan, Y.P., Wang, J., 2009. Model predictive control for nonlinear affine systems based on the simplified dual neural network. IEEE Int. Symp. on Intelligent Control and Control Applications, p.683-688.
[18]Pandelidis, I.O., Agrawal, A.R., 1988. Optimal anticipatory control of ram velocity in injection molding. Polym. Eng. Sci., 28(3):147-156.
[19]Qian, S.J., Badgwell, T.A., 2003. A survey of industrial model predictive control technology. Contr. Eng. Pract., 11(7):733-764.
[20]Rafizadeh, M., Patterson, W.I., Kamal, M.R., 1996. Physically-based model of thermoplastics injection molding for control applications. Int. Polym. Proc., 11(4):352-362.
[21]Tan, K.K., Tang, J.C., 2002. Learning-enhanced PI control of ram velocity in injection molding machines. Eng. Appl. Artif. Intell., 15(1):65-72.
[22]Tan, K.K., Huang, S.N., Jiang, X., 2001. Adaptive control of ram velocity for the injection moulding machine. IEEE Tran. Contr. Syst. Tech., 9(4):663-671.
[23]Tank, D., Hopfield, J.J., 1986. Simple ‘neural’ optimization networks: an A/D converter, signal decision circuit, and a linear programming circuit. IEEE Trans. Circ. Syst., 33(5):533-541.
[24]Varela, A.E., 2000. Self-tuning pressure control in an injection moulding cavity during filling. Chem. Eng. Res. Des., 78(1):79-86.
[25]Xia, Y., Wang, J., 2001. A dual neural network for kinematic control of redundant robot manipulators. IEEE Trans. Syst. Man Cybern. B, 31(1):147-154.
[26]Yang, Y., Gao, F., 2000. Adaptive control of the filling velocity of thermoplastics injection molding. Contr. Eng. Pract., 8(11):1285-1296.
[27]Zhang, S., Constantinides, A.G., 1992. Lagrange programming neural networks. IEEE Trans. Circ. Syst. II, 39(7):441-452.
Open peer comments: Debate/Discuss/Question/Opinion
<1>