Name
Abstract | ||
|---|---|---|
Recently, many of plastic products are mostly manufactured using injection molding machines. The quality of plastic products depends on the injection force. In a general force control system of the injection molding machine, the force information from the environment is detected by a force sensor. However, the force sensors present problems related to signal noise, sensor cost, narrow bandwidth, and other factors. We have proposed the reaction torque observer using two-inertia resonant model in our previous works. In the conventional estimation method, some estimated error causes at the pressure keeping process and the backing pressure process by the influence of nonlinear characteristics. The estimation accuracy of the state observer depends on the system identification because the actual system has the parameter variation and non-linear friction phenomenon. In this paper, we propose the new estimation method in which the influence of nonlinear factor is considered for high performance sensor-less force control. The effectiveness of the proposed method is confirmed by the simulation and experimental results. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/AMC.2010.5464059 | AMC |
Keywords | Field | DocType |
force control,friction,injection moulding,nonlinear control systems,observers,plastic products,pressure control,torque control,backing pressure process,friction phenomenon,injection molding machines,nonlinear characteristics,pressure keeping process,reaction torque observer,sensor-less injection force control,state observer,two-inertia resonant model,force sensor,system identification,force,torque,manufacturing | State observer,Nonlinear system,Torque,Computer science,Control theory,Injection molding machine,Pressure control,Bandwidth (signal processing),Control system,System identification | Conference |
ISSN | ISBN | Citations |
1943-6572 E-ISBN : 978-1-4244-6669-6 | 978-1-4244-6669-6 | 3 |
PageRank | References | Authors |
0.46 | 3 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tetsuya Asai | 1 | 79 | 26.75 |
| Yuzuru Ohba | 2 | 21 | 2.06 |
| Kiyoshi Ohishi | 3 | 415 | 71.48 |
| katsuyuki majima | 4 | 3 | 0.46 |
| Shiro Urushihara | 5 | 13 | 2.23 |
| Koichi Kageyama | 6 | 22 | 2.83 |