Neural estimators of two-mass system optimized using the Levenberg-Marquardt training and genetic algorithm

This paper presents application of neural networks for state variables estimation of two-mass system. Two stages of applied design process can be distinguished: the Levenberg-Marquardt method was implemented for weights adaptation, moreover topologies of the neural models were optimized using genetic algorithm (number of the nodes in each hidden layer was determined). Analyzed estimators were tested in structure with state space controller. Simulation results, presenting robustness against changes of the plant parameters, are presented. Furthermore, implementation of neural estimators in programmable device (dSPACE1103 card) was done and experimental tests were prepared.

[1]  Shuzhi Sam Ge,et al.  Adaptive Neural Output Feedback Control of Uncertain Nonlinear Systems With Unknown Hysteresis Using Disturbance Observer , 2015, IEEE Transactions on Industrial Electronics.

[2]  Wei Wu.  Neural network structure optimization based on improved genetic algorithm , 2012, 2012 IEEE Fifth International Conference on Advanced Computational Intelligence (ICACI).

[3]  Damian Giaouris,et al.  Stator current model reference adaptive systems speed estimator for regenerating-mode low-speed operation of sensorless induction motor drives , 2013 .

[4]  Peter Liu,et al.  Fuzzy Virtual Reference Model Sensorless Tracking Control for Linear Induction Motors , 2013, IEEE Transactions on Cybernetics.

[5]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[6]  K. C. Ho,et al.  A formal selection and pruning algorithm for feedforward artificial neural network optimization , 1999, IEEE Trans. Neural Networks.

[7]  Gregory J. Wolff,et al.  Optimal Brain Surgeon and general network pruning , 1993, IEEE International Conference on Neural Networks.

[8]  Tomaso A. Poggio,et al.  Regularization Theory and Neural Networks Architectures , 1995, Neural Computation.

[9]  Seung-Ki Sul,et al.  Kalman filter and LQ based speed controller for torsional vibration suppression in a 2-mass motor drive system , 1995, IEEE Trans. Ind. Electron..

[10]  Hak-Keung Lam,et al.  Tuning of the structure and parameters of a neural network using an improved genetic algorithm , 2003, IEEE Trans. Neural Networks.

[11]  Marcin Kaminski,et al.  A Modified Fuzzy Luenberger Observer for a Two-Mass Drive System , 2015, IEEE Transactions on Industrial Informatics.

[12]  Jorge A. Solsona,et al.  On state estimation in electric drives , 2010 .

[13]  H. Grotstollen,et al.  COMPARISON OF METHODS FOR STATE ESTIMATION AND ON-LINE IDENTIFICATION IN SPEED AND POSITION CONTROL LOOPS , 1999 .

[14]  Ehud D. Karnin,et al.  A simple procedure for pruning back-propagation trained neural networks , 1990, IEEE Trans. Neural Networks.

[15]  Chang Wook Ahn,et al.  On the practical genetic algorithms , 2005, GECCO '05.

[16]  Teresa Orlowska-Kowalska,et al.  Vibration Suppression in a Two-Mass Drive System Using PI Speed Controller and Additional Feedbacks—Comparative Study , 2007, IEEE Transactions on Industrial Electronics.

[17]  Tung-Kuan Liu,et al.  Tuning the structure and parameters of a neural network by using hybrid Taguchi-genetic algorithm , 2006, IEEE Trans. Neural Networks.

[18]  Christian Lebiere,et al.  The Cascade-Correlation Learning Architecture , 1989, NIPS.

[19]  Yann LeCun,et al.  Optimal Brain Damage , 1989, NIPS.