A NARMAX model-based state-space self-tuning control for nonlinear stochastic hybrid systems

Jason Sheng Hong Tsai, Chu Tong Wang, Chi Chieh Kuang, Shu Mei Guo, Leang San Shieh, Chia Wei Chen

Research output: Contribution to journalArticlepeer-review

25 Citations (Scopus)

Abstract

A novel state-space self-tuning control methodology for a nonlinear stochastic hybrid system with stochastic noise/disturbances is proposed in this paper. via the optimal linearization approach, an adjustable NARMAX-based noise model with estimated states can be constructed for the state-space self-tuning control in nonlinear continuous-time stochastic systems. Then, a corresponding adaptive digital control scheme is proposed for continuous-time multivariable nonlinear stochastic systems, which have unknown system parameters, measurement noise/external disturbances, and inaccessible system states. The proposed method enables the development of a digitally implementable advanced control algorithm for nonlinear stochastic hybrid systems.

Original languageEnglish
Pages (from-to)3030-3054
Number of pages25
JournalApplied Mathematical Modelling
Volume34
Issue number10
DOIs
Publication statusPublished - 2010 Oct

All Science Journal Classification (ASJC) codes

  • Modelling and Simulation
  • Applied Mathematics

Fingerprint Dive into the research topics of 'A NARMAX model-based state-space self-tuning control for nonlinear stochastic hybrid systems'. Together they form a unique fingerprint.

Cite this