Direct Inverse Control for a Conical Tank by Using Takagi-Sugeno Fuzzy Model

This paper deals with the design of a model reference direct inverse control that is applied to the liquid level process of a conical tank. We approximate the process by linear local models based on Takagi-Sugeno fuzzy modeling. Therefore, a fuzzy identification is performed by means of a fuzzy clustering algorithm. From the obtained fuzzy model and the specifications of the reference model, we implement in a neural network the controller. A set of comparisons against published results demonstrates the advantages of the proposed approach. In particular, the neural network is obtained without training and testing and its complexity in terms of neuron number is reduced. Furthermore, the robustness of the proposed controllers to changes in the plant model is demonstrated.