A strong tracking predictor for nonlinear processes with input time delay


Research output: Journal PublicationsJournal Article (refereed)peer-review

34 Citations (Scopus)


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. © 2004 Elsevier Ltd. All rights reserved.
Original languageEnglish
Pages (from-to)2523-2540
Number of pages18
JournalComputers and Chemical Engineering
Issue number12
Early online date31 Jul 2004
Publication statusPublished - 15 Nov 2004
Externally publishedYes

Bibliographical note

This work was mainly supported by the NSFC (Grant No. 60025307, 60234010, 60228001), partially supported by the national 863 program, RFDP (Grant No. 20020003063) and the national 973 program (Grant No. 2002CB312200) of China.


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


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