Adaptive neural speed controller for direct drive with PMSM

The paper presents selected properties of the adaptive speed neural controller trained online for direct drive during mechanical changes of the object parameters. In the article was compared different algorithms for learning neural networks such as: backpropagation algorithm BP, momentum backpropagation MBP, Quickprop and RPROP. The authors proposed an effective method of supervision of learning neural network, which does not lead to its overfitting. The algorithms were implemented on a laboratory stand.

[1]  Tien-Chi Chen,et al.  Model reference neural network controller for induction motor speed control , 2002 .

[2]  M. A. Rahman,et al.  On-line adaptive artificial neural network based vector control of permanent magnet synchronous motors , 1998 .

[3]  Tomasz Pajchrowski.  Application of an internal model Speed Control for PMSM with variable mechanical paremeters , 2015, CYBCONF.

[4]  Lech M. Grzesiak,et al.  On-line Trained Neural Speed Controller with Variable Weight Update Period for Direct-Torque-Controlled AC Drive , 2006, 2006 12th International Power Electronics and Motion Control Conference.

[5]  N. Mort,et al.  Inverse model neural network-based control of dynamic systems , 1994 .

[6]  Martin A. Riedmiller,et al.  A direct adaptive method for faster backpropagation learning: the RPROP algorithm , 1993, IEEE International Conference on Neural Networks.

[7]  Dingguo Chen,et al.  Adaptive Neural Inverse Control Applied to Power Systems , 2006 .

[8]  H. Harry Asada,et al.  Direct-Drive Robots: Theory and Practice , 1987 .

[9]  Teresa Orlowska-Kowalska,et al.  Control of the Drive System With Stiff and Elastic Couplings Using Adaptive Neuro-Fuzzy Approach , 2007, IEEE Transactions on Industrial Electronics.

[10]  Tomasz Pajchrowski,et al.  Application of adaptive neural controller for drive with elastic shaft and variable moment of inertia , 2015, 2015 17th European Conference on Power Electronics and Applications (EPE'15 ECCE-Europe).

[11]  George W. Younkin.  Industrial servo control systems : fundamentals and applications , 1996 .

[12]  T. C. Chen,et al.  Model reference neural network controller for induction motor speed control , 2002 .

[13]  Scott E. Fahlman,et al.  An empirical study of learning speed in back-propagation networks , 1988 .

[14]  T. Pajchrowski.  The direct drive with variable moment of inertia in the structure of the reference model , 2014, 2014 16th International Power Electronics and Motion Control Conference and Exposition.

[15]  J. Deskur,et al.  Speed controller for a drive with complex mechanical structure and variable parameters , 2014, 2014 16th International Power Electronics and Motion Control Conference and Exposition.

[16]  Mats Isaksson,et al.  A touching movement : force control turns machining robots into universal tools , 2007 .

[17]  Werner Leonhard,et al.  Control of Electrical Drives , 1990 .

[18]  Teresa Orlowska-Kowalska,et al.  FPGA Implementation of ADALINE-Based Speed Controller in a Two-Mass System , 2013, IEEE Transactions on Industrial Informatics.

[19]  Bimal K. Bose,et al.  Neural Network Applications in Power Electronics and Motor Drives—An Introduction and Perspective , 2007, IEEE Transactions on Industrial Electronics.

[20]  Ren Hai-yan,et al.  Research of PMSM controller based on 2DOF-PID algorithm , 2007, 2007 International Conference on Electrical Machines and Systems (ICEMS).

[21]  Tien Chi Chen,et al.  Model reference robust speed control for induction-motor drive with time delay based on neural network , 2001, IEEE Trans. Syst. Man Cybern. Part A.

[22]  Tomasz Pajchrowski,et al.  Neural Speed Controller Trained Online by Means of Modified RPROP Algorithm , 2015, IEEE Transactions on Industrial Informatics.

[23]  Weiping Li,et al.  Adaptive high-precision control of positioning tables-theory and experiments , 1994, IEEE Trans. Control. Syst. Technol..

[24]  Ah Chung Tsoi,et al.  Lessons in Neural Network Training: Overfitting May be Harder than Expected , 1997, AAAI/IAAI.

[25]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.