Sequential Predictors for Uncertain Euler–Lagrange Systems with Large Transmission Delays

Abstract

This paper investigates the state prediction problems for uncertain Euler–Lagrange systems with large time delays during data transmissions. A set of sequential predictors is proposed to estimate the actual real-time states of the systems by using the delayed information of measurements. The arbitrarily large delays are handled by applying adequate numbers of serial sub-predictors. Meanwhile, the novel prediction structure of each subsystem is designed to deal with nonlinearities and unknown dynamics in the systems. Then, the predictor design is extended to the case without using delayed velocity measurements by updating the structure of the first sub-predictor. Sufficient conditions for the design of predictor gains, ensuring the boundness of prediction errors, are obtained through Lyapunov–Krasovskii functionals. The effectiveness and robustness of the uncertainties of the proposed method are verified by comparative results in simulations.