Rules based adaptation of Smith predictor

The investigation deals with the employment of a Smith predictor as an improved working mechanism for overcoming the negative effects arising in control systems with a long time delay. As a model-based algorithm it imposes a stringent restriction over systems behavior by parametric uncertainty. The adaptation algorithm proposed in this paper uses data, derived by analysis of control system performance. It determines a quantitative correction for uncertainty compensation of model parameters and the allowable rate of increasing the gain coefficient of the conventional PI-controller. Special attention is paid and a significant experimental volume of the investigation is carried out for estimation and excluding the effects of the available noise in the system with a specific intensity and spectral density on method accuracy.