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Controller Design For Induction And Brushless Motors Using Matlab With Digital Signal Processor (DSP)

Published in: Prospective and trends in technology and skills for sustainable social development. Leveraging emerging technologies to construct the future: Proceedings of the 19th LACCEI International Multi-Conference for Engineering, Education and Technology
Date of Conference: July 19-23, 2021
Location of Conference: Virtual
Authors: Bianca Rashell Claros Poveda (Universidad Tecnológica Centroamericana (UNITEC), HN)
Rigoberto Castro Castro (Universidad Tecnológica Centroamericana (UNITEC), HN)
Full Paper: #283

Abstract:

The automation process is a very important pillar for Industry 4.0. One of the first steps is the control of motors to improve production efficiency and generate energy savings. In mass production industries, techniques such as digital signal processing (DSP) systems are implemented to control motors. These systems are efficient but very expensive for certain applications. From this arises the need for a controller capable of handling AC and DC motors that improves efficiency and maintains low energy consumption. This project presents the design of an adaptive control system for brushless AC induction and DC motors, which is functional to any type of plant in the industry. The design was possible by implementing Matlab software and tools such as digital signal processor (DSP) and Simulink. Through an extensive investigation of the state of the art, three models needed to represent the control system have been specified. The first model for the AC motor, the second for the DC motor and the third for the DSP control; this is done in this way so that the probability of failure is lower. Subsequently, these models have been programmed in Simulink, integrating the three main models into one. In this way, the design of a controller for use in AC induction motors, specifically squirrel cage and brushless DC motors, has been achieved. The final model represents a response time of 0.25 seconds, which is optimal for this type of application, where response times of 2e-3 to 3 seconds are expected.