A contribution to model predictive controllers with fixed switching frequency and low computational cost

Abstract

This thesis proposes a new Model Predictive Control (MPC) technique for powerconverter applications, based on a recent meta-heuristic algorithm, called the Jaya algorithm, as the optimizer of MPC. The proposed solution presents three bene ts: simple implementation, viable computational cost, and xed switching frequency. The proposed MPC strategy, referred to as Jaya-MPC, stands out as an alternative to the classical Finite-Control-Set Model Predictive control (FCS-MPC), which presents a variable switching frequency. In addition, this work proposes a secondary contribution: a new metric, which assesses the spread of the switching frequency pro- le of power converters, i.e., it evaluates the variability of the switching frequency produced by MPC. This work uses this metric to investigate the switching frequency pro le of FCS-MPC. A straightforward parameter-setting method of the proposed algorithm rises from a parametric analysis, resulting in a high-power-quality solution. Also, this work compares the computational cost, based on the number of predictions, of both the proposed controller and the paradigm of MPC in power electronics. Experimental outcomes prove that Jaya-MPC is a simple-implementation technique with viable computational cost and high power quality.