Resilient Model Predictive Control (RMPC) Technique Based Induction Motor Monitoring and Control using Labview

Abstract

Nowadays induction motor is the most popular type of motor for industrial applications. The main advantage of the induction motor is its straightforward rotor construction leading to low cost, hard work, and low maintenance requirements. This work presents a remote control and monitoring the electrical and mechanical faults of an induction motor based on Labview for safe and economic data communication in industrial fields. In this work, the utilization of the Resilient Model Predictive Control (RMPC) to tackle the electrical and mechanical issue in an induction motor is proposed. This strategy allows for synchronization and active fault detection for the important and exclusion of a particular setting. Different importance levels of different control models of performance are evaluated. We continuously monitor technical motor parameters such as the sensor's peak timing, increment duration, peak overflow, and the load current and voltage relaxation error in an induction motor. The measured values are then sent to the processing unit, which displays the processing and displaying parameters that the Gateway module communicates with the Gateway module to send information to the remote monitoring cloud. The system also presents automatic and manual control methods to stop or start the induction motor using Lab view to avoid system failure. The applicability of the proposed framework is to use MATLAB in the creation of simulation results and experiments to approve MATLAB2017a programming and its execution evaluation results show that under actual operating conditions.