Deep Supervised Learning based Feature Extraction and PID tuning for Stable Second Order Mechanical Systems.

In this paper, we focus on the design and implementation of a deep learning based model for the identification and control of stable second order mechanical systems. To do so, we propose a model composed of two neural networks. A first neural network performs the identification of the mass, the elastic and damping constants of the system from its open-loop response. After that, a second network predicts the PID controller parameters which are necessary for the correct operation of the system. The proposed system is able to estimate process parameters with a reduced error and provide a resulting PID controller offering a satisfactory system response.