This paper presents a design of a neural controller for industrial level systems. The level process has an asymmetric dynamic and its control is not a simple process of performing. This work presents an advanced control technique using intelligent control with artificial neural networks. The proposal is to implement a network of multilayer perceptron with a PI controller for controlling a level system based on a SMAR® didactic plant with Hart protocol. | International Journal of Computer Networks and Communications Security C , , JANUARY 2014, 46–51 Available online at: ISSN 2308-9830 N C S Design of a Neural Controller Applied a Level System in Hart Protocol MURILLO FERREIRA DOS SANTOS1, KAMILA PERES ROCHA2, MARLON JOSÉ DO CARMO3 1 Intelligent Robotic Group – GRIn, Juiz de Fora Federal University, Juiz de Fora, Minas Gerais - Brazil 2 CEFET-MG Campus III Leopoldina, Department of Electronics, Leopoldina, Minas Gerais - Brazil E-mail: 1murilloferreiradossantos@, 2marloncarmo@ ABSTRACT This paper presents a design of a neural controller for industrial level systems. The level process has an asymmetric dynamic and its control is not a simple process of performing. This work presents an advanced control technique using intelligent control with artificial neural networks. The proposal is to implement a network of multilayer perceptron with a PI controller for controlling a level system based on a SMAR® didactic plant with Hart protocol. The control strategy is implemented with Matlab®. This software makes a communication with the plant through OPC (OLE for process control). The project demonstrates the practical feasibility and applicability of intelligent tools industrial systems, thus generating a gain in experimental learning, commonly found in the labor market. Keywords: Hart Networks, Artificial Neural Networks, Process Control, Nonlinear systems, Industrial Networks. 1 INTRODUCTION Nowadays, industries need more analysis and control of their processes in order to get better quality and speed, lower cost, and flaws. To assist in the design and analysis of the functioning of control systems, it is necessary to obtain their identification to apply a good control strategy to reduce uncertainty and improve the performance of a system. With the development of technology, one of the most widespread control and process automation is the Artificial Intelligence. Modeling
