Offset-free adaptive nonlinear model predictive control with disturbance observer for DC-DC buck converters

Abstract

Abstract:The aim of this paper is to design a nonlinear model predictive control for DC-DC buck converters to track constant reference signals with zero steady-state error. The online trained neural network (NN) model is employed as the predictor and the steady-state error, which is called the offset, is studied in the presence of the changes in system parameters and the external disturbances. The stability of the closed-loop system is investigated using the Lyapunov direct theory. The proposed method can provide offset-free behavior in the presence of constant disturbances. For rejecting nonconstant disturbances, a nonlinear disturbance observer based on the NN inverse model is proposed. Due to wide applications of the DC-DC converter in power electronics, control of its output voltage is considered in this paper.The effectiveness of the proposed control method is demonstrated by experimental results.

A nonlinear disturbance observer scheme for discrete time control systems

“…Robust control of systems having time delays has been an important research problem for many years [1][2][3] and disturbance observers have constituted effective solutions to the problem. Disturbance observer based control (DOBC) has been very popular during the last two decades and many contributions to the field have been made.…”

“…The disturbance prediction subsystem is implemented in discrete time and the technique is studied for AC machines. In another study, the disturbance observation problem has been considered in the framework of neural network aided model predictive control in discrete time in [2], where the goal is to follow constant reference signals with zero steady state error. The disturbance observer problem for telerobotic systems is studied in [3], where the transmission delay is considered as a difficulty to be alleviated and a continuous time approach with Smith predictor is developed.…”

A Compound Controller Design for a Buck Converter

“…This is because most of them are based on proportional integral derivative (PID) control methods, while PID controllers may not overcome the adverse effect of large disturbance signals [5,6]. To this end, many scholars have devoted themselves to researching nonlinear controller designs for DC-DC converters, such as sliding mode control [7][8][9], fuzzy control [10][11][12], neural network [13,14], and intelligent control [15][16][17]. Among them, sliding mode control has been found to be one of the most effective methods to handle nonlinear uncertain systems, since sliding mode control is insensitive to system uncertainties, external disturbances, and parameter perturbations [18].…”