Soft Computing Using Neural Estimation with LMI-Based Model Transformation for OMR-Based Control of the Buck Converter

This paper introduces a new method of intelligent control to control the Buck converter using newly developed small signal model of the pulse width modulation (PWM) switch. The new method uses recurrent supervised neural network to estimate certain parameters of the transformed system matrix [ A ~ ]. Then, a numerical algorithm used in robust control called linear matrix inequality (LMI) optimization technique is used to determine the permutation matrix [P] so that a complete system transformation {[ B ~ ], [ C ~ ], [ E ~ ]} is possible. The transformed model is then reduced using the method of singular perturbation, and state feedback control is applied to enhance system performance. The experimental simulation results show that the new control methodology simplifies the model in the Buck converter and thus uses a simpler controller that produces the desired system response for performance enhancement. Index Terms Buck Converter, Linear Matrix Inequality (LMI), Neural Network (NN), Order Model Reduction (OMR), State Feedback Control, Supervised Learning.