Optimal probability density function control for NARMAX stochastic systems

L. Guo, H. Wang, A. P. Wang

Research output: Contribution to journalArticlepeer-review

49 Scopus citations

Abstract

This paper presents a new control strategy for a class of non-Gaussian stochastic systems so that the output probability density function (PDF) of the system can be made to follow a desired PDF. The system considered is represented by an Nonlinear AutoRegressive and Moving Average with eXogenous (NARMAX) inputs with input channel time-delay and non-Gaussian noise. A multi-step-ahead nonlinear cumulative cost function is used to improve tracking performance. For this purpose, a relationship between the PDFs of all the inputs and the PDFs of multiple-step-ahead output is formulated by constructing an auxiliary multivariate mapping. By minimizing this performance function, a new explicit predictive controller design algorithm is established with less conservatism than some previous results. Furthermore, an improved approach is developed to guarantee the local stability of the closed-loop system by tuning the weighting parameters recursively. Simulations are given to demonstrate the effectiveness of the proposed control algorithm and desired results have been obtained.

Original languageEnglish
Pages (from-to)1904-1911
Number of pages8
JournalAutomatica
Volume44
Issue number7
DOIs
StatePublished - Jul 2008
Externally publishedYes

Keywords

  • Non-Gaussian systems
  • Nonlinear systems
  • Optimal control
  • Probability density functions
  • Stability analysis
  • Stochastic control
  • Time-delay systems

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