One-Step Prediction for Discrete Time-Varying Nonlinear Systems With Unknown Inputs and Correlated Noises

Abstract

One of the most important problems in the estimation theory is the one-step prediction. The goal of this problem is to determine the predictions of states in the next time step. This paper focuses on the one-step prediction for nonlinear dynamic systems. The system under investigation involves unknown inputs and the system noises are correlated. In this approach, using the Taylor series expansion for nonlinear functions, a new augmented state nonlinear predictor is proposed for discrete time-varying nonlinear systems. This predictor is obtained by solving a deterministic min-max optimization based on the regularized least squares problem. Moreover, to reduce the computational complexity of the prediction solution, using a nonlinear transformation, we propose a two-stage predictor including lower order estimators for states and unknown inputs. Finally, a frequency modulated signal model is considered to illustrate the effectiveness and performance of the proposed approaches in comparison with the existing estimation methods.