Two adaptive approaches of nonlinear system control

Generally the artificial neural networks (ANN) are regarded as highly computational demanding method. The usage of ANN in model predictive control as an adaptive predictor is mostly impossible. The aim of this paper is to present and compare one possible way how to reduce computational costs of adaptive predictors based on artificial neural networks. This paper presents real-time system control by two adaptive control methods. The first method is based on the model predictive method with adaptive artificial neural network as a predictor. This artificial neural network offers interesting solution of the computation time problem while using artificial neural network as an adaptive (online) predictor. The second method is established on self-tuning approach. Both these methods are applied to a problem of control liquid level in interconnected tanks. Real-time experiments are performed using Amira DTS200 - three tank system. This system is characterized by non-linear behavior.