Model-predictive control (MPC) has emerged as a promising control method in power
electronics, particularly for multiobjective control problems such as multilevel inverter (MLI) …

In this article, a novel robust data-driven model-free predictive control framework based on
the I/O data of the controlled plants, which is performed by incorporating the neural predictor …

An event-triggered control technique has been developed recently. This technique explicitly
reduced the signal transmission by introducing a flexible design of threshold inequalities. It …

This article proposes a novel data-driven neural predictors-based robust finite control-set
model predictive control (FCS-MPC) methodology for power converters, which aims to …

In this article, an improved multiobjective ant colony optimization (ACO) algorithm is
proposed to design the weighting factors (WFs) in the model predictive control of power …

The increasing deployment and exploitation of distributed renewable energy source (DRES)
units and battery energy storage systems (BESS) in DC microgrids lead to a promising …

Standard model predictive control is an optimization-based control strategy that can handle
multiple control objectives and system nonlinear constraints. However, it typically suffers …

State‐of‐the‐art model‐based predictive control techniques for AC motor drives are
reviewed in this paper. A plethora of MPC algorithms with vast number of complex ideas has …

Recently, event-triggered model predictive control (MPC) is addressed as a promising and
powerful technique for power converter systems. The core idea behind this technique is to …

In the conventional finite control set model predictive torque control, the cost function
consists of different control objectives with varying units of measurements. Due to presence …