Open AccessPerformance Improvement of a DC/DC Converter Using Neural Network Controller in comparison with Different Controllers
Author(s) -
Muhanad D. Almawlawe,
Muhammad Al-Badri,
Issam Hayder Alsakini
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/870/1/012119
Subject(s) - control theory (sociology) , controller (irrigation) , pid controller , transient (computer programming) , matlab , computer science , artificial neural network , nonlinear system , open loop controller , voltage , control engineering , engineering , control (management) , artificial intelligence , temperature control , physics , electrical engineering , closed loop , quantum mechanics , agronomy , biology , operating system
Controlling a system is a complicated job, especially when we talk about the nonlinearity of the system introduced by the external changes. This paper presents the procedure of designing, analysis, and verification of nonlinear autoregressive moving average controller (NARMA L2) as an artificial intelligence technique to track the output voltage of a Buck dc/dc converter in comparison with PID controller, digitalized sliding mode controller so as to reduce the ripples in output voltage and to suppress the transient overshoots, or in other words, enhance the transient response diversity of the plant in the case of load and line changes. In this technique, a back-propagation learning algorithm is derived to increase the effectiveness of the proposed controller. Finally, the proposed method of control using a neural network controller is designed using MATLAB/SIMULINK and the results of the converter for the Neuro controller are compared with different techniques of control.