Neuro-Fuzzy-Based Auto-Tuning Proportional Integral Controller for Induction Motor Drive

This study presents a novel neuro-fuzzy (NF)-based auto-tuning proportional integral controller (NFATPI) for accurate speed control, and to ensure optimal drive performances of the indirect field controlled induction motor drive, under system disturbances and uncertainties. The training mechanism of the proposed NF have been developed and illustrated through mathematical formulations. Then, the NF parameters have been updated on-line using a suitable training algorithm. The learning rates of the NF are derived on the basis of the discrete Lyapunov function is also illustrated, in order to confirm the stability and the performance of prediction of the proposed NFATPI. The simulation results confirm the effectiveness of the strategy NFATPI as a robust controller for high performance industrial motor drive systems.

[1]  D. Lewin,et al.  Simple PI and PID tuning for open-loop unstable systems , 1991 .

[2]  Leonard G. C. Hamey,et al.  XOR has no local minima: A case study in neural network error surface analysis , 1998, Neural Networks.

[3]  Kuo-Kai Shyu,et al.  A novel motor drive design for incremental motion system via sliding-mode control method , 2005, IEEE Transactions on Industrial Electronics.

[4]  Mohamed Najim,et al.  A fast feedforward training algorithm using a modified form of the standard backpropagation algorithm , 2001, IEEE Trans. Neural Networks.

[5]  Girish Kumar Singh,et al.  A simple indirect field-oriented control scheme for multiphase induction machine , 2005, IEEE Transactions on Industrial Electronics.

[6]  Alessandro Goedtel,et al.  Neural Approach for Speed Estimation in Induction Motors , 2007, Seventh International Conference on Intelligent Systems Design and Applications (ISDA 2007).

[7]  Ji-Yoon Yoo,et al.  Speed-sensorless vector control of an induction motor using neural network speed estimation , 2001, IEEE Trans. Ind. Electron..

[8]  A. Goedtel,et al.  Speed estimation for induction motor using neural networks method , 2013, IEEE Latin America Transactions.

[9]  Michael N. Vrahatis,et al.  On the alleviation of the problem of local minima in back-propagation , 1997 .

[10]  R. Toscano.  A simple robust PI/PID controller design via numerical optimization approach , 2004 .

[11]  Chi-Chung Cheung,et al.  Magnified gradient function with deterministic weight modification in adaptive learning , 2004, IEEE Transactions on Neural Networks.

[12]  Ali Assi,et al.  Modelling the XOR/XNOR Boolean Functions Complexity Using Neural Network , 2006, 2006 13th IEEE International Conference on Electronics, Circuits and Systems.

[13]  Romeo Ortega,et al.  Nonlinear control of induction motors: torque tracking with unknown load disturbance , 1993, IEEE Trans. Autom. Control..

[14]  Yasar Becerikli,et al.  Modeling and prediction with a class of time delay dynamic neural networks , 2007, Appl. Soft Comput..

[15]  H B Kazemian,et al.  Comparative study of a learning fuzzy PID controller and a self-tuning controller. , 2001, ISA transactions.

[16]  Manish Manoria,et al.  Improved Back Propagation Algorithm to Avoid Local Minima in Multiplicative Neuron Model , 2010 .

[17]  Tony R. Martinez,et al.  Improved Backpropagation Learning in Neural Networks with Windowed Momentum , 2002, Int. J. Neural Syst..

[18]  Rong-Jong Wai,et al.  Fuzzy neural network position controller for ultrasonic motor drive using push-pull DC-DC converter , 1999 .

[19]  Y.A.-R.I. Mohamed.  Adaptive Self-Tuning Speed Control for Permanent-Magnet Synchronous Motor Drive With Dead Time , 2006, IEEE Transactions on Energy Conversion.

[20]  Chih-Hong Lin,et al.  Self-constructing fuzzy neural network speed controller for permanent-magnet synchronous motor drive , 2001, IEEE Trans. Fuzzy Syst..

[21]  Suman Maiti,et al.  Simulation studies on model reference adaptive controller based speed estimation technique for the vector controlled permanent magnet synchronous motor drive , 2009, Simul. Model. Pract. Theory.

[22]  A. S. Bharadwaj,et al.  A review of parameter sensitivity and adaptation in indirect vector controlled induction motor drive systems , 1990, 21st Annual IEEE Conference on Power Electronics Specialists.

[23]  Aitor J. Garrido,et al.  A sensorless variable structure control of induction motor drives , 2004 .

[24]  Mitsuyuki Nakao,et al.  Error measures of the back-propagation learning algorithm , 2002 .

[25]  B. V. Manikandan,et al.  Adaptive Neuro-Fuzzy Inference System based speed controller for brushless DC motor , 2014, Neurocomputing.

[26]  Piotr Wach,et al.  Dynamics and Control of Electrical Drives , 2011 .

[27]  M.N. Uddin,et al.  Development of a Self-Tuned Neuro-Fuzzy Controller for Induction Motor Drives , 2004, IEEE Transactions on Industry Applications.