Autors: Yordanova, S. T.
Title: An Approach to Observability and Controllability Analysis of Nonlinear Plants on the Basis of TSK Models
Keywords: Nonlinear plant; observability and controllability analysis;

Abstract: Most industrial plants are nonlinear, multivariable, inertial and with model uncertainty. They are difficult to model using classical approaches and thus their observability and controllability necessary for the design of the controller are hard to analyze. The aim of the present research is to derive conditions for the analysis of the observability and the controllability of nonlinear plants, represented by state space Takagi-Sugeno-Kang (TSK) models. The main results are a simple and general approach to observability and controllability study of nonlinear plants, which is based on equivalent linear systems and illustrated on a two-variable nonlinear plant – a laboratory two-tank system. The TSK plant model needed can be derived from an existing nonlinear plant model or applying a suggested procedure for development of modified transfer-functions-based TSK models from expert and experimentation data.

References

    Issue

    Information Technologies and Control, issue 1, pp. 35-45, 2015, Bulgaria,

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    Цитирания (Citation/s):
    1. Yinping Che, Zhonggai Zhao, Zhiguo Wang, Fei Liu (2022) Iterative learning model predictive control for multivariable nonlinear batch processes based on dynamic fuzzy PLS model, J Process Contr, 119(6):1-12, DOI:10.1016/j.jprocont.2022.09.005 - 2022 - в издания, индексирани в Scopus или Web of Science

    Вид: статия в списание, публикация в реферирано издание