Robust Recurrent Wavelet Interval Type-2 Fuzzy-Neural-Network Control for DSP-Based PMSM Servo Drive Systems

Fayez F. M. El-Sousy

Robust Recurrent Wavelet Interval Type-2 Fuzzy-Neural-Network Control for DSP-Based PMSM Servo Drive Systems

Fayez F. M. El-Sousy

Vol. 13, No. 1, pp. 139-160, Jan. 2013

10.6113/JPE.2013.13.1.139

L2 tracking performance

Lyapunov satiability theorem

Permanent-magnet synchronous motor (PMSM) servo drive

Recurrent interval type-2 fuzzy-neural-network (RIT2FNN)

Wavelet neural networks

PDF

Abstract

In this paper, an intelligent robust control system (IRCS) for precision tracking control of permanent-magnet synchronous motor (PMSM) servo drives is proposed. The IRCS comprises a recurrent wavelet-based interval type-2 fuzzy-neural-network controller (RWIT2FNNC), an RWIT2FNN estimator (RWIT2FNNE) and a compensated controller. The RWIT2FNNC combines the merits of a self-constructing interval type-2 fuzzy logic system, a recurrent neural network and a wavelet neural network. Moreover, it performs the structure and parameter-learning concurrently. The RWIT2FNNC is used as the main tracking controller to mimic the ideal control law (ICL) while the RWIT2FNNE is developed to approximate an unknown dynamic function including the lumped parameter uncertainty. Furthermore, the compensated controller is designed to achieve L2 tracking performance with a desired attenuation level and to deal with uncertainties including approximation errors, optimal parameter vectors and higher order terms in the Taylor series. Moreover, the adaptive learning algorithms for the compensated controller and the RWIT2FNNE are derived by using the Lyapunov stability theorem to train the parameters of the RWIT2FNNE online. A computer simulation and an experimental system are developed to validate the effectiveness of the proposed IRCS. All of the control algorithms are implemented on a TMS320C31 DSP-based control computer. The simulation and experimental results confirm that the IRCS grants robust performance and precise response regardless of load disturbances and PMSM parameters uncertainties.

Statistics

Show / Hide Statistics

Cumulative Counts from September 30th, 2019
Multiple requests among the same browser session are counted as one view. If you mouse over a chart, the values of data points will be shown.

Cite this article

[IEEE Style]

F. F. M. El-Sousy, "Robust Recurrent Wavelet Interval Type-2 Fuzzy-Neural-Network Control for DSP-Based PMSM Servo Drive Systems," Journal of Power Electronics, vol. 13, no. 1, pp. 139-160, 2013. DOI: 10.6113/JPE.2013.13.1.139.

[ACM Style]

Fayez F. M. El-Sousy. 2013. Robust Recurrent Wavelet Interval Type-2 Fuzzy-Neural-Network Control for DSP-Based PMSM Servo Drive Systems. Journal of Power Electronics, 13, 1, (2013), 139-160. DOI: 10.6113/JPE.2013.13.1.139.