Aplikace moderních procesorových systémů v řízení elektrických regulovaných pohonů

Abstract

The main content of the doctoral thesis is the design and implementation of a new control system for controlling electrical controlled drives and small converter systems of the power electronics, which is also capable of applying computationally demanding algorithms based on soft computing methods based on the theory of artificial neural network, Fuzzy logic, and especially the Genetic Algorithm, which is the subject of the implementation of a real application to verify a newly designed control system. The first part of the article is focused on a brief overview of current Hardware developments in the field of control systems and defines the general requirements for control systems for power electronics and electrical controlled drives. It also introduces the definition of the concept of design and development of a prototype of a new modular control system with a modern dual-core DSC from Texas Instruments Delfino TMS320 family, F2837xD series. The second part briefly introduces an overview of general methods of sensorless control, and is also devoted to a general overview of Meta-Heuristic optimization methods, where the problem of Genetic Algorithm is described in more detail, as a representative of a subset of evolutionary algorithms of Meta-Heuristic optimization methods. The following sections are devoted to the applications of the Genetic Algorithm in the structure of Electrical Controlled Drives and the simulation results are presented here. The last part presents a detailed description and analysis of the structure a real implementation of the Genetic Algorithm in the newly designed modular control system, distribution of control algorithm between microprocessors cores, and achieved results of experimental verification of the real application of Genetic Algorithm in sensorless control of Electrical Controlled Drive with Induction motor.