Observer-based tracking control for linear system with input delays and disturbances using predictors and sub-predictors

This paper proposes a novel dynamic output feedback control algorithm for linear systems with input time delay in the presence of external disturbances. The algorithm based on observer-state and disturbance predictors ensures the tracking control with unknown reference model parameters. The accuracy in steady state depends on the highest-order derivative of disturbance and reference model signals, therefore, the amplitude of these signals can be sufficiently large. Further, the proposed algorithm is extended on the observer-state and disturbance sub-predictors which implement multi-step prediction. Compared with the predictors-based algorithm the sub-predictor-based algorithm allows to control plants with a larger input time-delay and allows to predict the disturbance in less time. The sufficient conditions in terms of linear matrix inequalities (LMIs) provide the estimate of the maximum time-delay that preserves the closed-loop system stability. Numerical simulations are presented to demonstrate the effectiveness of the proposed method.