Name
Abstract | ||
|---|---|---|
The inherent hysteresis property of piezoelectric actuator (PEA) brings challenges to its modeling and control. This paper proposes a model learning method that is suitable for both forward and inverse PEA models. The hysteresis property is learned based on least squares support vector machines (LS-SVMs). A larger dataset is used for training LS-SVM to guarantee a good generalization performance. Support vectors pruning is utilized to reduce the model complexity. The rate-dependent property of PEA is identified as a linear dynamic submodel. Moreover, a pointing control system with two dual-PEA-axis steering mirrors is developed, which can regulate the 4-degree-of-freedom pose of a laser beam. The coordinated control of four PEAs is realized based on the Jacobian matrix. The learned inverse PEA models are used for the feedforward compensation of each PEA’s nonlinearity. A series of experiments were conducted to evaluate the proposed method’s effectiveness. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TSMC.2017.2754863 | IEEE Transactions on Systems, Man, and Cybernetics |
Keywords | Field | DocType |
Laser beams,Hysteresis,Laser modes,Training,Control systems,Computational modeling,Support vector machines | Least squares,Inverse,Nonlinear system,Jacobian matrix and determinant,Control theory,Support vector machine,Hysteresis,Control system,Mathematics,Feed forward | Journal |
Volume | Issue | ISSN |
50 | 3 | 2168-2216 |
Citations | PageRank | References |
1 | 0.36 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fangbo Qin | 1 | 11 | 4.78 |
| Dapeng Zhang | 2 | 25 | 4.00 |
| Dengpeng Xing | 3 | 16 | 9.48 |
| De Xu | 4 | 90 | 10.98 |
| Jianquan Li | 5 | 7 | 5.26 |