Deep Learning Techniques for Model Reference Adaptive Control and Identification of Complex Systems

Abstract

Although many mathematical and analytical techniques have been presented to control and identify the dynamic systems, there are vast fields of research needing to be developed and extended through Deep Learning (DL) approaches. In this paper, we try to describe how intelligent controllers can interact under control systems in a unique DL-based package. Despite the fact that conventional techniques have some advantages, such as the appropriate reliability and simple implementation for industrial goals, intelligent methods have potential to solve complex problems and identify nonlinear systems. Hence the concentration of this research is on the use of DL techniques to improve the system identification and control in model reference adaptive controllers. A dataset is also used to validate the responses of the proposed techniques. The simulation results demonstrate that not only are the proposed methods consistently appropriate to control the complex systems but also they have acceptable responses in order to utilize for system identification.