FFANN Optimization by ABC for Controlling a 2nd Order SISO System's Output with a Desired Settling Time

Abstract

In this study, a control strategy is aimed to ensure the settling time of a 2nd order system's output value while its input reference value is changed. Here, Feed Forward Artificial Neural Network (FFANN) nonlinear structure has been chosen as a control algorithm. In order to implement the intended control strategy, FFANN's normalization coefficient (K), learning coefficients (LATIN SMALL LETTER ENG), momentum coefficients (mu) and the sampling time (Ts) were optimized by Artificial Bee Colony (ABC) but FFANN's values of weights were chosen arbitrary on start time of control system. After optimization phase, the FFANN behaves as an adaptive optimal discrete time non-linear controller that forces the system output to take the same value with the input reference for a desired settling time (ts). The success of the optimization algorithm was proved with close loop feedback control simulations on Matlab's Simulink platform based on 2nd order transfer functions. Also, the success was proved with a 2nd order physical system (buck converter) that was structured with power electronics elements on Simulink platform. Finally, the success of the control process was discussed by observing results.