Online adaptive fuzzy logic controller using neural network for Networked Control Systems

Networked Control Systems are used for controlling remote plants via shared data communication networks such as Ethernet. These systems have found many applications in industrial, medical and space sciences fields. However there are some drawbacks in these systems, which make them challenging to design. One of the most common problems in these systems is the stochastic time delay. Packet switching in internet brings about the randomly varying delay time and consequently makes these systems instable. Convenient controllers such as PID and PI type controllers which are just match with a constant delay time could not be a solution for these systems. Fuzzy logic controllers due to their none-linear characteristic which is compatible with these systems are potentially a wise option for their control purpose. Fuzzy logic controller could become adaptive by means of neural networks and beneficial to deal with the varying time delay problem. Further, they do have more capabilities to tackle packet dropouts and dynamically system variables. This paper introduces a novel control method which addresses the time delay varying problem effectively. This novel method suggests an online adaptive fuzzy logic controller which have been controlled and adapted through the neural network. This designed controller is applied to an AC 400 W servo motor as a remote plant in order to position control it via Ethernet. The measurement of round-trip time (RTT) is used to estimate the online time delay as a parameter in online adaptive fuzzy logic controller. The rule-based table of designed fuzzy logic controller rotates in relation to this estimated time delay. The value of rotating is obtained from a trained neural network. Comparison of results from simulations of different controllers and their comparison indicate that this novel designed controller provides a better performance over the varying time delay. The proposed method follows the input easily, despite classical methods which resu- t in an unstable system especially over the large time delays as large as 600 ms.