Supervised neural computing and LMI optimisation for order model reduction-based control of the Buck switching-mode power supply

This article introduces a new method of intelligent control to control the Buck converter using a newly developed small-signal model of the pulse width modulation (PWM) switch. The new method uses a recurrent supervised neural network to estimate certain parameters of the transformed system matrix [ ]. Then, a numerical algorithm used in robust control called the linear matrix inequality (LMI) optimisation technique is used to determine the permutation matrix [P], so that a complete system transformation {[ ], [ ], [ ]} is possible. The transformed model is then reduced using the method of singular perturbation, and state feedback control is applied to enhance the system's performance. The eigenvalues of the resulting reduced model are a subset of the original non-transformed full-order system, and this is important since the eigenvalues in the non-transformed reduced order model will be different from the eigenvalues of the original full-order system. 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.