Model based control of a water tank system

Abstract Neural network with a specific restricted connectivity structure is used to identify a model of a real-life process. Parameters of the identified model are used to design a controller based on dynamic feedback linearization. The designed neural network based controller is verified on mathematical model within MATLAB/Simulink environment and applied to the real-time control of a plant. The static error is eliminated retuning input signal in the steady-state mode. Liquid level tank system was chosen as a case study to illustrate the applicability of the proposed approach. Experimental results have shown a good performance of the proposed technique. The designed controller is capable of tracking the desired water level for all set points with high degree of accuracy and without significant over/undershoot.

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