Real-time application of a fuzzy adaptive control to one level in a three-tank system

Abstract

In this experimental work, a fuzzy combined control method has been described and applied to test the ability of the artificial intelligence technique in controlling nonlinear systems in real-time applications. A direct feedback linearization ideal control law is approximated with a Takagi–Sugeno fuzzy inference system. The adaptation law of the Takagi–Sugeno controller parameters is computed based on a fuzzy approximation term of the control error. This approximation is computed using a Mamdani fuzzy system. The experiment is applied on one level in a three-tank system and a test of the controller ability against perturbations is considered. The obtained results were compared to those leaded by a classical proportional–integral controller. The basic idea of this work is the use of the control error (between the actual control signal and the perfect control signal) instead of the tracking error (between the output of the one level in a three-tank system and the reference signal). As our approach is model free, that is, we do not use the mathematical model of the three-tank system, in the application part of this work, the output of the constructed Takagi–Sugeno fuzzy controller is injected to the real three-tank system.

Keywords

Artificial intelligence application feedback linearization fuzzy control technique fuzzy approximation term affine nonlinear system real-time experimental test

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