Control and Tracking Techniques for Switched Reluctance Machines

Control and Tracking Techniques for Switched Reluctance Machines provides detailed and practical instructions for implementing drive and control techniques for switched reluctance machines (SRMs), which can be immediately applied in real-world projects. It presents the latest innovations in control techniques for SRMs, which are essential for the efficiency and sustainability of modern electrical systems. The book includes case studies and practical examples that enhance the understanding of concepts and their application in real scenarios, making the content accessible to both students and experienced professionals. It emphasizes techniques that optimize SRM performance and promote the sustainability of electrical systems, a topic of increasing importance in engineering. With a focus on the current and future needs of the energy sector, this authoritative guide is a key reference for practicing engineers, researchers, and practitioners in the renewable energy industry.

• Presents the latest innovations in control techniques for switched reluctance machines;
• Emphasizes techniques and innovation with a focus on sustainability;
• Offers case studies and a practical approach allowing immediate technology applications in real-world projects.

Wesley Pacheco Calixto, Ph.D., is a permanent faculty member in the graduate program at the Federal University of Goiás and a full professor at the Federal Institute of Goiás. He holds a B.Sc. in Physics from the Pontifical Catholic University of Goiás, an M.Sc. in Electrical and Computer Engineering from the Federal University of Goiás, and a Dr. in Electrical Engineering from the Federal University of Uberlândia, with a research period at the University of Coimbra, Portugal. Dr. Calixto completed postdoctoral research in electromagnetic system modeling applied to geophysical prospecting at the Visualization and Simulation Centre at Carleton University. His research focuses on system modeling, intelligent systems, optimization, computational models, and artificial intelligence.

Wanderson Rainer Hilário Araújo, Ph.D., is a faculty member in the Graduate Program in Production and Systems Engineering at the Pontifical Catholic University of Goiás and also teaches in the graduate programs at SENAI Ítalo Bologna. Dr. Araújo has experience in software-hardware integration and industrial automation, having developed systems in these areas for industries in Goiás. He holds a B.Sc. in Electrical Engineering from the Pontifical Catholic University of Goiás and an M.Sc. and Ph.D. in Electrical and Computer Engineering from the Federal University of Goiás. His research focuses on switched reluctance machines, control systems, system identification, and optimization.

Lucas Diniz Silva Morais is a member of the Science and Technology Study and Research Group at the Federal Institute of Goias, conducting research in systems modeling. Currently, he serves as a laboratory specialist in electrical engineering, actively contributing to research and technical development and focusing on integrating new technologies and processes into the academic environment. He holds a B.Sc. in Electrical Engineering from Anhanguera Educacional College and an M.Sc. in Technology, Management, and Sustainability from the Federal Institute of Goiás.

Marcio Rodrigues Cunha Reis, Ph.D., serves as the leader and researcher of the Science and Technology Studies Group, a researcher at the Technological Experimentation Center of the Federal Institute of Goiás, and a collaborator at the Systems Modeling Laboratory of the Federal University of Goiás. He also teaches Industrial Automation at the Federal Institute of Goiás. Dr. Reis holds a Ph.D. and M.Sc. in Electrical Engineering from the Federal University of Goiás and a B.Sc. in Electrical Engineering from the Pontifical Catholic University of Goiás. His research areas include evolutionary computing, systems modeling, and artificial intelligence, with active involvement at the Albert Einstein Israelite Hospital, focusing on applying artificial intelligence to medical image diagnostics.