Analysis of Improve Performance and Dynamics of an Induction Motor using an Artificial Neural Network Controller and a Conventional Proportional Integral Derivative Controller

Abstract

Systems vary depending on the changing operating conditions. Some include linear systems, which previous studies have proven can be controlled using conventional systems, while non-linear systems require expert and intelligent controllers. To verify this, the current study compares expert artificial neural networks (ANNs) with traditional PID controllers for controlling the rotational speed of an induction motor. Traditional PID controllers are simple and easy to implement, but they lack the ability to handle changing operating conditions and do not have the capacity to adapt to load fluctuations as expert systems such as neural networks do. They also have the ability to handle load disturbances and are considered more effective, efficient, and robust compared to traditional PID controllers. PID controllers are easy to adjust and simple in structure, and are widely used with linear industrial systems. PID controllers have degraded performance when the load changes, i.e., when the system is non-linear, their performance deteriorates. ANN, on the other hand, are characterized by their ability to adapt to varying conditions and changing loads. In non-linear systems, they have the ability to adapt and handle system disturbances. ANNs are expensive and require precise design, data for network architecture, and training. The feasibility of tracking induction motor speed is investigated using motor simulation models, conventional PID controllers, and expert neural networks, and the simulation results are analyzed and compared. The simulation results demonstrate that ANNs outperform PIDs in response speed and lower overshoot and undershoot limits under various operating conditions. From the above, it can be concluded that expert neural networks can effectively control and improve dynamic response of induction motors due to their adaptive and learning capabilities, and they can handle nonlinear systems such as changing load conditions. It is proposed to conduct simulation tests of an electric motor using MATLAB engineering software, by mathematically representing it using a transfer function according to characteristics suitable for applications similar to the proposed characteristics. Simulation tests are conducted for an open circuit system, a closed circuit system without control, and a closed circuit system with control. The second method involves self-tuning the conventional controller to achieve the best design by optimizing performance, response speed, overshoot rate, and rise time, according to the proposed operating algorithm. The results demonstrate the superiority of the neural network over conventional controllers.