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The objective of this thesis is to investigate and find a solution by designing the intelligent controller for controlling water level system, such as neural network. The controller 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 PID and neural network control algorithms are performed. In PID control, Ziegler Nichols tuning method is used to control the system. In neural network control, the approach of Model Reference Adaptive Neural Network (ANN) Control based on the back propagation 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.

Design and Implementation of a Neural Control System and Performance Characterization with PID Controller for Water Level Control