Speed Control of a Multi-Motor System Based on Fuzzy Neural Model Reference Method

Abstract

The direct-current (DC) motor has been widely utilized in many industrial applications, such as a multi-motor system, due to its excellent speed control features regardless of its greater maintenance costs. A synchronous regulator is utilized to verify the response of the speed control. The motor speed can be improved utilizing artificial intelligence techniques, for example fuzzy neural networks (FNNs). These networks can be learned and predicted, and they are useful when dealing with nonlinear systems or when severe turbulence occurs. This work aims to design an FNN based on a model reference controller for separately excited DC motor drive systems, which will be applied in a multi-machine system with two DC motors. The MATLAB/Simulink software package has been used to implement the FNMR and investigate the performance of the multi-DC motor. moreover, the online training based on the backpropagation algorithm has been utilized. The obtained results were good for improving the speed response, synchronizing the motors, and applying load during the work of the motors compared to the traditional PI control method. Finally, the multi-motor system that was controlled by the proposed method has been improved where its speed was not affected by the disturbance.

Keywords

• backpropagation algorithm
• fuzzy neural network
• speed control
• model reference control
• multi-motor system
• separately excited DC motor (SEDCM)