Field programmable gate array hardware in the loop validation of fuzzy direct torque control for induction machine drive

Abstract

Introduction. Currently, the direct torque control is very popular in industry and is of great interest to scientists in the variable speed drive of asynchronous machines. This technique provides decoupling between torque control and flux without the need to use pulse width modulation or coordinate transformation. Nevertheless, this command presents two major importunities: the switching frequency is highly variable on the one hand, and on the other hand, the amplitude of the torque and stator flux ripples remain poorly controlled throughout the considered operating speed range. The novelty of this article proposes improvements in performance of direct torque control of asynchronous machines by development of a fuzzy direct torque control algorithm. This latter makes it possible to provide solutions to the major problems of this control technique, namely: torque ripples, flux ripples, and failure to control switching frequency. Purpose. The emergence of this method has given rise to various works whose objective is to show its performance, or to provide solutions to its limitations. Indeed, this work consists in validation of a fuzzy direct torque control architecture implemented on the ML402 development kit (based on the Xilinx Virtex-4 type field programmable gate array circuit), through hardware description language (VHDL) and Xilinx generator system. The obtained results showed the robustness of the control and sensorless in front of load and parameters variation of induction motor control. The research directions of the model were determined for the subsequent implementation of results with simulation samples.

Application of the type-2 fuzzy logic controller and the fractional order controller to regulate the DTC speed in an induction motor

“…FLCs introduce a degree of "fuzziness" or uncertainty into decision making, which can be advantageous in various scenarios. We need to use FLCs for many reasons, such as: handling uncertainty, human-like reasoning, tolerance to noise and adaptive control (Aib et al, 2023). Knowledge base A linguistic control rule base plus a database make up a knowledge base.…”

“…The defuzzification interface performs the following functions: • A scale mapping that converts the distribution of output variable values into the associated discourse universe, and • Defuzzification, which involves changing an implied fuzzy control action into an explicit control action (Aib et al, 2023;Kamalapur & Aspalli, 2023).…”

“…While a traditional FLC uses linguistic variables and fuzzy rules to make decisions, a type 2-FLC (T2-FLC) goes a step further by considering the uncertainties associated with the linguistic variables. In a T2-FLC, each linguistic variable has a fuzzy set associated with it, and each fuzzy set has a footprint of uncertainty (FOU) associated with it (Aib et al, 2023). The FOU represents the level of confidence or uncertainty in the membership values of the fuzzy set.…”