In recent years, there has been a growing interest in using model-free deep reinforcement
learning (DRL)-based controllers as an alternative approach to improve the dynamic …

Traditional identification methods make it difficult to accurately determine the parameters of
the friction model. Based on the LuGre friction model, a hybrid optimization algorithm (HO) is …

In this paper, a design method using a neural network of a zero-voltage-switching buck
quasi-resonant DC–DC converter is presented. The use of this innovative approach is …

This paper investigates the stability problem of delayed neural networks (DNNs) with time-
varying delay. In the stability analysis of DNNs, the stability conditions are usually in form of …

This article presents a novel control architecture for an enhanced closed-loop speed
tracking of a DC–DC buck power converter fed Permanent Magnet DC motor (PMDC) motor …

Dear Editor, Tracking control in networked environment is a very challenging problem due to
the contradiction of rapid response to the time-varying signal and the inevitable delay …

This paper presents an elitism-crossover barnacle mating optimization (ECBMO). It is an
improvement of barnacle mating optimization (BMO). The original BMO suffers from local …

Distributed power systems are affected by weather changes and load variations, both of
which can affect the power balance of a DC microgrid. The voltage on the DC bus is directly …

Optimization of control systems for power electronic systems using evolutionary algorithms
has gained significant attention in current research. However, the effectiveness of such …

This paper introduces a novel approach for adaptive trajectory tracking of a non inverting
buck boost converter, leveraging a pulse width modulation (PWM) technique and an …