Processor in the loop implementation of artificial neural network controller for BLDC motor speed control
Meriem Megrini, Ahmed Gaga, Youness Mehdaoui
Abstract
This paper's goal is to use a proportional integral derivative controller and an artificial neural network controller (ANNC) to control the speed of a BLDC motor. The outcomes of the MATLAB/SIMULINK simulation are used as the basis for comparing the two controllers. On the other hand, the ANNC makes the BLDC motor's reaction more consistent and dependable. In other words, it reduces peak time, overshoot, and settle time while speeding up system responses. ANNC is implemented in Processor in the Loop (PIL) experimenting with Arduino Mega. It is based on the generated C-code and executed it in the embedded card. The experiment's outcomes match those of the MATLAB/SIMULINK simulation.
Topics & Concepts
Overshoot (microwave communication)MATLABComputer scienceController (irrigation)Electronic speed controlArtificial neural networkControl theory (sociology)DC motorControl engineeringPID controllerControl (management)EngineeringArtificial intelligenceAgronomyTemperature controlBiologyElectrical engineeringOperating systemTelecommunicationsSensor Technology and Measurement SystemsSensorless Control of Electric MotorsIndustrial Automation and Control Systems