A passivity-based neural control using genetic algorithm for a DC-DC boost power converter

Abstract

In this paper, a passivity – based neural control using genetic algorithm of a DC-DC boost power converter is proposed. The output of a DC-DC boost power converter is an inductor current. The control input is the duty ratio. Using a co-ordinate transformation of state variables and control input, a DC-DC boost power converter is passive. A new plant is zero-state observable and the equilibrium point at origin of this plant is asymptotically stable. A passivity - based control law is constructed by using neural network. The goal is that the capacitor voltage is equal to the desired voltage. The neural network has three layers: the input layer, the hidden layer and the output layer. The activation function of the hidden layer is tangent-hyperbolic and the activation of the output layer is linear. The weights of neural network are also adjusted optimally by genetic algorithm using decimal encoder. The general algorithm of neural network for a passivity-based control is also constructed. Simulation results are done with Simulink in MATLAB. Simulation results of the passivity-based neural control without using genetic algorithm are good and show that the capacitor voltage v is kept at the desired voltage V_d when the input voltage E and the load resistor R vary. Further, the simulation results of the passivity – based neural control using genetic algorithm have good performance such as short settling time, 3 ms and small steady-state error, which is indicated by IAE (integral absolute error (IAE) of the desired voltage and the capacitor voltage), 0.0343.  The results of passivity-based neural control using genetic algorithm shows that the capacitor voltage is kept at the desired value V_d. Finally, simulation results show that the passivity-based neural control using genetic algorithm is better than the passivity-based neural control.

Keywords: dc-dc boost power converter, neural control, passivity – based control, genetic algorithm.