Artificial Intelligent Control for DC Motor via Dynamic Neural Networks

Artificial intelligent control has recently been applied to various fields such as railways, traffic, and electric devices. In this paper, a new DC motor control method with system model is proposed, that uses artificial neural networks to identify the DC motor model. Controlled by the proposed method with feedback loop control, the closed-loop system follows the reference model based on system identification using the neural network and without direct calculation or estimation of DC motor parameters. The reference model is determined as a formulation of low-pass filters that reject the sensor noise with high frequency band. The proposed control method can be applied to controller design using the plant model of DC motors such as internal model control, model reference adaptive control, model predictive control, and so on. The procedure and effectiveness of the proposed method were verified by numerical simulations.