Design and analysis of control system using neural network for regulated DC power supply

Abstract

Conventional control systems used for regulated power supplies, including the proportional integral and derivation (PID) controller, have some serious disadvantages. The PID controller has a delayed feedback associated with the control action and requires a lot of mathematical derivations. This paper presents a novel controlling system based on the artificial neural network (ANN), which can be used to regulate the output voltage of the DC power supply. Using MATLAB^TM, the designed control system was tested and analyzed with two types of back-propagation algorithms. This paper presents the results of the simulation that includes sum-squared error (SSE) and mean-squared error (MSE), and gives a detailed comparison of these values for the two algorithms. Hardware verification of the new system, using RS232 interface and Microsoft Visual Basic 6.0, was implement- ed, showing very good consistency with the simulation results. The proposed control system, compared to PID and other conventional controllers, requires less mathematical derivation in design and it is easier to implement.

Keywords

regulated power supply / neural network / proportional integral and derivation (PID) controller / multi-layer perceptron (MLP) network

References

[1] Malmstadt H V, Enke C G, Crouch S R. Electronics and Instrumentation for Scientists. San Francisco: The Benjamin-Cummings Publishing Company, Inc., 1981

[2] Ashan K J. PID Controllers. North Carolina: Instrument Society of America, 1995

[3] Astrom K J, Hagglund T. Automatic tuning of simple regulators with specifications on phase and amplitude margins. Automatica, 1984, 20(5): 645-651

[4] Mohan N, Undeland T, Robbins W. Power Electronics. New York: John Wiley & Sons, 1995

[5] Warwick K,Rees D.Industrial Digital Control System. London: Institution of Electrical Engineers, 1988

[6] Rowe J, Partridge D. Creativity: A survey of AI approaches.Artificial Intelligence Review, 1993, 7(1): 43-70

[7] Howard D, Mark B. Neural Network Toolbox for Use with Matlab. Natick: MathWorks, Inc., 1998

[8] Fu L M. Neural Networks in Computer Intelligence. New York: McGraw-Hill, Inc., 1994

[9] Suratgar A A, Tavakoli M B, Hoseinabadi A. Modified Levenberg-Marquardt method for neural networks training. World Academy of Science, Engineering and Technology, 2005, (6): 46-48

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Higher Education Press and Springer-Verlag Berlin Heidelberg