A neural networks based controller for Electron Beam welding power supply

Welding is an unavoidable unit practically for every manufacturing industry. Electron Beam welding (EBW) are very important unit of some specific manufacturing processes where high degree of accuracy and flawless welding is highly desirable like aerospace engineering. The power supply unit (PSU) used for EBW are very important unit, for which high degree of stability and accuracy is a must. Since EBW is absolutely nonlinear system, for better performance, adopting non linear control methods could be a good solution. In the current study a robust adaptive controller based on multi-layer feed-forward neural network is developed for real-time voltage regulation. Simulation shows a better characteristic in terms of settling time and overshoot for the neural controller compared to that of a conventional PI controller based on Ziegler Nichols [1,2,3,4] frequency response tuning method. The controller has the unique advantages of nonlinear mapping and adaptive learning.

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

[2]  Bo-Hyung Cho,et al.  Novel zero-current-switching (ZCS) PWM switch cell minimizing additional conduction loss , 2002, IEEE Trans. Ind. Electron..

[3]  R. Ortega,et al.  Analysis and experimentation of nonlinear adaptive controllers for the series resonant converter , 2000 .

[4]  Bo-Hyung Cho,et al.  Novel zero-current-switching (ZCS) PWM switch cell minimizing additional conduction loss , 2001, 2001 IEEE 32nd Annual Power Electronics Specialists Conference (IEEE Cat. No.01CH37230).

[5]  Xinbo Ruan,et al.  A novel zero-voltage and zero-current-switching PWM full-bridge converter using two diodes in series with the lagging leg , 2001, IEEE Trans. Ind. Electron..

[6]  Chanchal Dey,et al.  An improved auto-tuning scheme for PI controllers. , 2008, ISA transactions.

[7]  Yun Li,et al.  PID control system analysis, design, and technology , 2005, IEEE Transactions on Control Systems Technology.

[8]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[9]  Marvin Minsky,et al.  Perceptrons: An Introduction to Computational Geometry , 1969 .

[10]  C. Hang,et al.  Refinements of the Ziegler-Nichols tuning formula , 1991 .

[11]  P.R.K. Chetty Resonant power supplies: their history and status , 1992, IEEE Aerospace and Electronic Systems Magazine.

[12]  J. G. Ziegler,et al.  Optimum Settings for Automatic Controllers , 1942, Journal of Fluids Engineering.

[13]  S Mangrulkar,et al.  Artificial neural systems. , 1990, ISA transactions.

[14]  Leopoldo García Franquelo,et al.  Implementation of a neural controller for the series resonant converter , 2002, IEEE Trans. Ind. Electron..

[15]  Bo-Hyung Cho,et al.  Novel zero-voltage and zero-current-switching (ZVZCS) full-bridge PWM converter using coupled output inductor , 2002 .

[16]  Thomas G. Habetler,et al.  A fast on-line neural network training algorithm for a rectifier regulator , 1995, Proceedings of IECON '95 - 21st Annual Conference on IEEE Industrial Electronics.

[17]  Myung-Joong Youn,et al.  An energy feedback control of series resonant converter , 1990, 21st Annual IEEE Conference on Power Electronics Specialists.