Electric Drives: From Basic Models to Fuzzy and Neural Network Controllers

Today’s undergraduate students must understand electric drives for controlling electric vehicles, drones, robots, and more. These advanced applications require complex experimental and theoretical knowledge to develop industrial or academic applications that can help improve quality of life. However, conventional courses often fail to meet this need or cover certain transversal (reasoning to face complexity, self-knowledge, communication, innovative solutions) and disciplinary skills. Thus, a theoretical course that allows students to understand and gain experimental knowledge can help achieve competencies that students can use in their professional lives. This course has demonstrated that students can propose practical solutions using MATLAB^® and Simulink^®—some have presented their proposals in conferences. We will show how to create an active approach in which students gain knowledge for designing rapid prototypes using the Tec-21 Challenge-Based Learning framework and Model-Based Design.

This course covers basic and advanced controllers such as PID, fuzzy logic, and neural network controllers. Some experiments are conducted using low-cost components such as Arduino^® boards or DC motors. Students can use the same Simulink program to perform an advanced simulation in real time and rapidly move from primary controllers to advanced ones. Furthermore, the students learn how to create a rapid prototype in a short period to find solutions to industrial problems. This course also gives an overview of real-time simulations to achieve a high-fidelity simulation.