DESIGN AND IMPLEMENTATION OF A NEURAL CONTROL SYSTEM AND PERFORMANCE CHARACTERIZATION WITH PID CONTROLLER FOR WATER LEVEL CONTROL

The objective of this thesis is to investigate and find a solution by designing the intelligentcontroller for controlling water level system, such as neural network. The controller also can bespecifically run under the circumstance of system disturbances. To achieve these objectives, a prototypeof water level control system has been built and implementations of both PID and neural network controlalgorithms 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) Controlbased on the back propagation algorithm is applied on training the system. Both control algorithms aredeveloped to embed into a standalone DSP-based micro-controller and their performances arecompared. Fig.1: Configuration of water level control system. The system consists of servo motor, valves, pump, infrared sensor and a DSP controller.