A neural network multiagent architecture applied to fieldbus intelligent control

Abstract

This paper presents a multiagent architecture applied to factory automation. These agents detect faults in process automation and allocate intelligent algorithms in field device function blocks to solve these faults. It is also present a dynamic function block parameter exchange strategy which allows agent fieldbus allocation. The objective is to enable problem detection activities independent of user intervention. The use of Artificial Neural Network (ANN)-based algorithms enables the agents to learn about fault problem patterns and adapt an algorithm that can be used in fault situations. With this, the intention is reduce supervisor intervention in selecting and implementing an appropriate structure of function block algorithms. These algorithms, when implemented in device function blocks, provide a solution at fieldbus level, reducing data traffic between gateway and device.

A Neural Network Multiagent Architecture Applied to Industrial Networks for Dynamic Allocation of Control Strategies Using Standard Function Blocks

“…The test process contains portions that are similar to industrial processes. This environment was presented for the first time in our previous work [34] and the results were shown in [35]. Unlike these approaches, in this paper, we reexecute the examples using the LABVIEW-FIPA layer instead of the JADE-FIPA layer.…”

A Multiagent Architecture Based in aFoundation Fieldbus Network Function Blocks

“…The test process contained parts that were similar to industrial processes. This environment was presented for the first time in our previous work (Machado et al, 2008a), and the results were showed in Machado et al (2008b). Table Exchange As we can see in Figure 14, the plant level is composed of two cascading tanks.…”