Water Level Control by Fuzzy Logic and Neural Networks

The objective of this paper is to investigate and find a solution by designing the intelligent controllers for controlling water level system, such as fuzzy logic and neural network. The controllers also can be specifically run under the circumstance of system disturbances. To achieve these objectives, a prototype of water level control system has been built and implementations of both fuzzy logic and neural network control algorithms are performed. In fuzzy logic control, Sugeno model is used to control the system. In neural network control, the approach of Model Reference Adaptive Neural Network Control based on the backpropagation algorithm is applied on training the system. Both control algorithms are developed to embed into a standalone DSP-based micro-controller and their performances are compared.