DC Motor Current Control Based on Inverse Model Using Recurrent Neural Network

초록

This paper presents a method for microcontrollers control using a recurrent neural network-based inverse model. Limited computational power in microcontrollers makes applying complex neural network structures challenging. We use the Elman network with a simple structure as an inverse model to address this issue. Elman network was used to model nonlinear control systems. The proposed method constructs a recurrent neural network-based inverse model in parallel to enhance the performance of the PID controller. The recurrent neural network uses the output generated by the PID controller as the past control input and compensates for the control inputs generated by the PID controller. We applied the proposed controller to a DC motor current control system and compared its performance with the PID controller that uses a deep neural network as an inverse model. We evaluated the control performance by applying a sine wave. The results show that the proposed controller has better tracking performance at 1, 3, and 5 Hz than the other controllers. © ICROS 2024.

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