Signal Generation for Switched Reluctance Motors using Parallel Genetic Algorithms

• Published in: IFAC-PapersOnLine Vol. 53; no. 2; pp. 8193 - 8198
• Main Authors: Eichhorn, Mike, Purfürst, Sandro, Shardt, Yuri A.W.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 2020
• Subjects: Genetic algorithms; Measuring span; Multi-objective optimization; Parallel programs; Switched reluctance motors; Switched reluctance motors; Parallel programs; Multi-objective optimization; Measuring span; Genetic algorithms
• ISSN: 2405-8963; 2405-8963
• DOI: 10.1016/j.ifacol.2020.12.2328

Switched reluctance motors (SRM) are an inherent part in robotics and automation systems where energy and cost efficiency is required. This motor type has no windings and permanent magnets on the rotor which results in a simple and robust structure. However, SRMs require a complex electronic control system to generate a specified number of voltage pulses for each motor phase. This paper presents the signal generation of multiple phases using only one current sensor in an asymmetric half bridge (AHB). In addition to maintain the predetermined phase voltages, sufficient current measurement windows and a minimal current ripple for the individual phases are further optimization criteria for signal generation. The generation of a state vector which controls the individual semiconductor for each motor phase to achieve a required phase voltage and simultaneously fulfill the multi-objective optimization criteria is challenging. Due to the vast number of possible solutions, a genetic algorithm (GA) was used to find state combinations that are suitable for the formulated optimization criteria. The results were discussed and recommendations about the genotype representation and the used genetic operators were given. Interested readers will find detailed information about the software technical implementation using the Global Optimization Toolbox from MATLAB.