Abstract

Nonlinear state prediction is of crucial importance to design controllers for nonlinear processes with input time delay. In this paper, the extended nonlinear state predictor (ENSP) we proposed is first outlined, which is used to predict the future states of a class of nonlinear processes with input time delay. A new concept of strong tracking predictor (STP) is then proposed, and an orthogonality principle is given as a criterion to design the STP. On the basis of the orthogonality principle, the ENSP is modified, which results in a STP. After the detailed STP algorithm is presented, we prove that the STP is locally asymptotically convergent for a class of nonlinear deterministic processes if some sufficient conditions are satisfied. In the presence of measurement noise, it is further proved that the proposed STP is exponentially bounded under certain conditions. Finally, computer simulations with a MIMO nonlinear model are presented, which illustrate that the proposed STP can predict accurately the future states of a class of nonlinear time delay processes no matter whether the states change suddenly or slowly, in addition, it has definite robustness against model/plant mismatches.

Keywords: Nonlinear processes; Input time delay; State predictor; Extended Kalman filter; Orthogonality principle; Strong tracking predictor; Convergence analysis

Article Outline

1. Introduction

2. Preliminaries

2.1. Outline of the extended nonlinear state predictor

2.2. Orthogonality principle

3. Strong tracking predictor for nonlinear time delay processes

4. Convergence analysis of the strong tracking predictor without noise

5. Stochastic boundedness of the strong tracking predictor

6. Simulation studies

6.1. Simulation 1: nominal case

6.2. Simulation 2: robustness test

6.3. Quantitative analysis

7. Concluding remarks

Acknowledgements

References