In this paper, a fast, intelligent model is proposed for the order determination of linear dynamical systems by using convolutional neural networks. This model estimates the dynamic order of the system with considerably lower excitation order of stimulation signal and without any prior knowledge in comparison to former works. To this end, only step response of the system is taken to estimate the dynamic order for both stable and unstable linear systems. Unlike the conventional methods, in this deep-based approach, the order determination is performed quickly, automatically, at a low cost, and without any iteration. In addition, it is demonstrated that the proposed approach has low sensitivity against delay and noise. Such an intelligent model can satisfy the demands for a fast identifier in online and plug-and-play controllers.

There is an increasing demand to develop fast and reliable models to identify the class of control systems in developing online and plug-and-play controllers. In this paper, to perform automatic, reliable, and fast classification of linear processes, it is proposed to use Convolutional Neural Networks (CNNs). A process can be: unstable or stable, integrally or self-regulated, non-minimum phase or minimum phase, first-order or second-order, oscillatory damping, or over-damping. We consider six different classes of linear processes accordingly. The CNN is designed and trained to predict the class of linear processes by taking only their step responses. The results show clearly that the CNN has high generalization and accuracy in determining the behavior class of the process even in the presence of noise, delay, and high order dynamics.