Neural network PID control for a water level system
by Xiaoli Li, Longhui Shi, Ji Li
International Journal of Modelling, Identification and Control (IJMIC), Vol. 11, No. 1/2, 2010

Abstract: Water level system is a classic device for the research of non-linear system control. It is always represented by a First-Order plus Dead Time (FOPDT) model around the equilibrium point. Based on the water level system, different PID strategies (empirical method and Ziegler-Nichols method) are studied for the improvement of control performance. Optimised by gradient descent method, a PID controller based on a radial based function (RBF) neural network is given and applied to an actual A3000 three-tank water level system. From the experiment result, the effectiveness of the proposed method is tested.

Online publication date: Mon, 20-Sep-2010

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