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Research Papers

Robust Tracking Control for Takagi–Sugeno Fuzzy Systems With Unmeasurable Premise Variables: Application to Tank System

[+] Author and Article Information
H. Ghorbel

STA Laboratory,
University of Sfax,
National School of Engineers of Sfax
,Rue Soukra,
BP 1173, 3038, Tunisia
e-mail: hana.ghorbel@yahoo.fr

A. El Hajjaji

MIS Lab,
University of Picardie Jules Vernes,
7, Rue Moulin Neuf 80000,
Amiens, France
e-mail: hajjaji@u-picardie.fr

M. Souissi, M. Chaabane

STA Lab,
University of Sfax,
National School of Engineers of Sfax,
Rue Soukra,
BP 1173, 3038, Tunisia

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received February 27, 2013; final manuscript received January 7, 2014; published online April 4, 2014. Assoc. Editor: Luis Alvarez.

J. Dyn. Sys., Meas., Control 136(4), 041011 (Apr 04, 2014) (8 pages) Paper No: DS-13-1088; doi: 10.1115/1.4026467 History: Received February 27, 2013; Revised January 07, 2014

In this paper, a robust fuzzy observer-based tracking controller for continuous-time nonlinear systems presented by Takagi–Sugeno (TS) models with unmeasurable premise variables, is synthesized. Using the H norm and Lyapunov approach, the control design for TS fuzzy systems with both unmeasurable premises and system states is developed to guarantee tracking performance of closed loop systems. Sufficient relaxed conditions for synthesis of the fuzzy observer and the fuzzy control are driven in terms of linear matrix inequalities (LMIs) constraints. The proposed method allows simplifying the design procedure and gives the observer and controller gains in only one step. Numerical simulation on a two tank system is provided to illustrate the tracking control design procedure and to confirm the efficiency of the proposed method.

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Grahic Jump Location
Fig. 1

Structural diagram for the algorithm of fuzzy observer-based tracking control

Grahic Jump Location
Fig. 4

Trajectories of the states of the reference model, the states of the system, and their estimates

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