Abstract
The synchronous reluctance motor (SynRM) servo-drive system has highly nonlinear uncertainties owing to a convex construction effect. It is difficult for the linear control method to achieve good performance for the SynRM drive system. The nonlinear backstepping control system using upper bound with switching function is proposed to inhibit uncertainty action for controlling the SynRM drive system. However, this method uses a large upper bound with a switching function, which results in a large chattering. In order to reduce this chattering, a nonlinear backstepping control system using an adaptive law is proposed to estimate the lumped uncertainty. Since this method uses an adaptive law, it cannot achiever satisfactory performance. Therefore, a nonlinear backstepping control system using a reformed recurrent Hermite polynomial neural network with an adaptive law and an error estimated law is proposed to estimate the lumped uncertainty and to compensate the estimated error in order to enhance the robustness of the SynRM drive system. Further, the reformed recurrent Hermite polynomial neural network with two learning rates is derived according to an increment type Lyapunov function to speed-up the parameter convergence. Finally, some experimental results and a comparative analysis are presented to verify that the proposed control system has better control performance for controlling SynRM drive systems.
| Original language | English |
|---|---|
| Article number | JPE 18-5-9 |
| Pages (from-to) | 1380-1397 |
| Number of pages | 18 |
| Journal | Journal of Power Electronics |
| Volume | 18 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - 2018 Sep 1 |
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All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Electrical and Electronic Engineering
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Nonlinear backstepping control of SynRM drive systems using reformed recurrent Hermite polynomial neural networks with adaptive law and error estimated law. / Ting, Jung Chu; Chen, Der-fa.
In: Journal of Power Electronics, Vol. 18, No. 5, JPE 18-5-9, 01.09.2018, p. 1380-1397.Research output: Contribution to journal › Article
TY - JOUR
T1 - Nonlinear backstepping control of SynRM drive systems using reformed recurrent Hermite polynomial neural networks with adaptive law and error estimated law
AU - Ting, Jung Chu
AU - Chen, Der-fa
PY - 2018/9/1
Y1 - 2018/9/1
N2 - The synchronous reluctance motor (SynRM) servo-drive system has highly nonlinear uncertainties owing to a convex construction effect. It is difficult for the linear control method to achieve good performance for the SynRM drive system. The nonlinear backstepping control system using upper bound with switching function is proposed to inhibit uncertainty action for controlling the SynRM drive system. However, this method uses a large upper bound with a switching function, which results in a large chattering. In order to reduce this chattering, a nonlinear backstepping control system using an adaptive law is proposed to estimate the lumped uncertainty. Since this method uses an adaptive law, it cannot achiever satisfactory performance. Therefore, a nonlinear backstepping control system using a reformed recurrent Hermite polynomial neural network with an adaptive law and an error estimated law is proposed to estimate the lumped uncertainty and to compensate the estimated error in order to enhance the robustness of the SynRM drive system. Further, the reformed recurrent Hermite polynomial neural network with two learning rates is derived according to an increment type Lyapunov function to speed-up the parameter convergence. Finally, some experimental results and a comparative analysis are presented to verify that the proposed control system has better control performance for controlling SynRM drive systems.
AB - The synchronous reluctance motor (SynRM) servo-drive system has highly nonlinear uncertainties owing to a convex construction effect. It is difficult for the linear control method to achieve good performance for the SynRM drive system. The nonlinear backstepping control system using upper bound with switching function is proposed to inhibit uncertainty action for controlling the SynRM drive system. However, this method uses a large upper bound with a switching function, which results in a large chattering. In order to reduce this chattering, a nonlinear backstepping control system using an adaptive law is proposed to estimate the lumped uncertainty. Since this method uses an adaptive law, it cannot achiever satisfactory performance. Therefore, a nonlinear backstepping control system using a reformed recurrent Hermite polynomial neural network with an adaptive law and an error estimated law is proposed to estimate the lumped uncertainty and to compensate the estimated error in order to enhance the robustness of the SynRM drive system. Further, the reformed recurrent Hermite polynomial neural network with two learning rates is derived according to an increment type Lyapunov function to speed-up the parameter convergence. Finally, some experimental results and a comparative analysis are presented to verify that the proposed control system has better control performance for controlling SynRM drive systems.
UR - http://www.scopus.com/inward/record.url?scp=85055132201&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85055132201&partnerID=8YFLogxK
U2 - 10.6113/JPE.2018.18.5.1380
DO - 10.6113/JPE.2018.18.5.1380
M3 - Article
AN - SCOPUS:85055132201
VL - 18
SP - 1380
EP - 1397
JO - Journal of Power Electronics
JF - Journal of Power Electronics
SN - 1598-2092
IS - 5
M1 - JPE 18-5-9
ER -