Nonlinear Backstepping Control of SynRM Drive Systems Using Reformed Recurrent Hermite Polynomial Neural Networks with Adaptive Law and Error Estimated Law

다국어 초록 (Multilingual 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.

목차 (Table of Contents)

• Abstract
• I. INTRODUCTION
• II. CONFIGURATION OF A SYNRM DRIVE SYSTEM
• III. CONTROL SYSTEM DESIGN
• IV. EXPERIMENTAL RESULTS
• V. CONCLUSIONS
• REFERENCES

참고문헌 (Reference)

1 V. Kazakbaev, "The feasibility study of the application of a synchronous reluctance motor in a pump drive" 2016

2 I. Kanellakopoulos, "Systematic design of adaptive controller for feedback linearizable system" 36 (36): 1241-1253, 1991

3 Amir Farrokh Payam, "Robust DTC Control of Doubly-Fed Induction Machines Based on Input-Output Feedback Linearization Using Recurrent Neural Networks" 전력전자학회 11 (11): 719-725, 2011

4 T. Hagglund, "Revisiting the Ziegler-Nichols tuning rules for PI control-part II: The frequency response method" 6 (6): 469-482, 2004

5 T. Hagglund, "Revisiting the Ziegler-Nichols tuning rules for PI control" 4 (4): 364-380, 2002

6 H. K. Chiang, "Reference model with an adaptive Hermite fuzzy neural network controller for tracking a synchronous reluctance motor" 43 (43): 770-780, 2015

7 C. H. Lin, "Recurrent modified Elman neural network control of PM synchronous generator system using wind turbine emulator of PM synchronous servo motor drive" 52 : 143-160, 2013

8 Bartolini, "Peoperties of a combined adaptive/second-order sliding mode control algorithm for some classes of uncertain nonlinear systems" 45 (45): 1334-1341, 2000

9 K. J. Astrom, "PID Controller: Theory, Design, and Tuning" Instrument Society of America 1995

10 Y. H. Kim, "Optimum design criteria of an ALA-SynRM for the maximum torque density and power factor improvement" 53 (53): S279-S288, 2017

11 W. Chai, "Optimal design of wound field synchronous reluctance machines to improve torque by increasing the saliency ratio" 53 (53): 2017

12 F. J. Lin, "On-line gain-tuning IP controller using RFNN" 37 (37): 655-670, 2001

13 C. H. Lin, "Novel adaptive recurrent Legendre neural network control for PMSM servo-drive electric scooter" 137 : 011010-011011, 2015

14 Chih-Hong Lin, "Nonlinear Backstepping Control Design of LSM Drive System Using Adaptive Modified Recurrent Laguerre Orthogonal Polynomial Neural Network" 제어·로봇·시스템학회 15 (15): 905-917, 2017

15 F. L. Lewis, "Neuro-Fuzzy Control of Industrial Systems with Actuator Nonlinearities" SIAM Frontiers in Applied Mathematics 2002

16 Chih-Hong Lin, "Hybrid Recurrent Wavelet Neural Network Control of PMSM Servo-Drive System for Electric Scooter" 제어·로봇·시스템학회 12 (12): 177-187, 2014

17 S. M. Siniscalchi, "Hermitian polynomial for speaker adaptation of connectionist speech recognition systems" 21 (21): 2152-2161, 2013

18 G. G. Rigatos, "Feed-forward neural networks using Hermite polynomial activation functions" 323-333, 2006

19 C. C. Ku, "Diagonal recurrent neural networks for dynamic system control" 6 (6): 144-156, 1995

20 V. Dmitrievskii, "Development of a high efficient electric drive with synchronous reluctance motor" 876-881, 2015

21 M. Y. Wei, "Design and implementation of an online tuning adaptive controller for synchronous reluctance motor drives" 60 (60): 3644-3657, 2013

22 L. Ma, "Constructive feedforward neural networks using Hermite polynomial activation functions" 16 (16): 821-833, 2005

23 J. J. E. Slotine, "Applied Nonlinear Control" Prentice-Hall 1991

24 C. H. Lin, "Adaptive recurrent fuzzy neural network control for synchronous reluctance motor servo drive" 151 (151): 712-724, 2004

25 L. Ma, "Adaptive constructive neural networks using Hermite polynomials for image compression" 713-722, 2005

26 J. Astrom, "Adaptive Control" Addison-Wesley 1995

27 K. C. Kim, "A study on the optimal design of SynRM for the high torque and power factor" 43 (43): 2543-2545, 2007

28 T. Matsuo, "A new control strategy for optimum-efficiency operation of a synchronous reluctance Motor" 33 (33): 1146-1153, 1997

29 E. M. Rashad, "A maximum torque per ampere vector control strategy for synchronous reluctance motors considering saturation and iron losses" 2411-2417, 2004

30 Chih-Hong Lin, "A PMSM Driven Electric Scooter System with a V-Belt Continuously Variable Transmission Using a Novel Hybrid Modified Recurrent Legendre Neural Network Control" 전력전자학회 14 (14): 1008-1027, 2014

31 S. I. Amer, "A Comparison of Different Intelligent Control Techniques For a PM dc Motor" 전력전자학회 5 (5): 1-10, 2005

32 Chih-Hong Lin, "A Backstepping Control of LSM Drive Systems Using Adaptive Modified Recurrent Laguerre OPNNUO" 전력전자학회 16 (16): 598-609, 2016