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Technical Brief

Takagi–Sugeno Fuzzy Predictive Control for a Class of Nonlinear System With Constrains and Disturbances

[+] Author and Article Information
Bin Wang, Jianwei Zhang, Delan Zhu

Department of Electrical Engineering,
Northwest Agriculture and Forestry University,
Yangling IN 712100, China

Diyi Chen

Department of Electrical Engineering,
Northwest Agriculture and Forestry University,
Yangling IN 712100, China
e-mail: diyichen@nwsuaf. edu.cn

1Corresponding author.

Contributed by the Design Engineering Division of ASME for publication in the JOURNAL OF COMPUTATIONAL AND NONLINEAR DYNAMICS. Manuscript received October 1, 2014; final manuscript received January 30, 2015; published online April 16, 2015. Assoc. Editor: Hiroshi Yabuno.

J. Comput. Nonlinear Dynam 10(5), 054505 (Sep 01, 2015) (8 pages) Paper No: CND-14-1227; doi: 10.1115/1.4029783 History: Received October 01, 2014; Revised January 30, 2015; Online April 16, 2015

This paper investigates the fuzzy predictive control for a class of nonlinear system with constrains under the condition of noise. Based on the fuzzy linearization theory, a class of nonlinear systems can be described by the Takagi–Sugeno (T–S) fuzzy model. The T–S fuzzy model and predictive control are combined to stabilize the proposed class of nonlinear system, and the detailed mathematical derivation is given. Moreover, the designed controller has been optimized even if the system is constrained by output and control input, or perturbed by external disturbances. Finally, numerical simulations including three-dimensional Lorenz system, four-dimensional Chen system and five-dimensional nonlinear system with external disturbances are presented to demonstrate the universality and effectiveness of the proposed scheme. The approach proposed in this paper is simple and easy to implement and also provides reference for relevant nonlinear systems.

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Figures

Grahic Jump Location
Fig. 1

Triangle membership function

Grahic Jump Location
Fig. 5

State trajectories of hyperchaotic system (16) with controller (5) in Ref. [28]

Grahic Jump Location
Fig. 3

State trajectories of Lorenz system (14) with controller (5) in Ref. [28]

Grahic Jump Location
Fig. 2

State trajectories of Lorenz system (14) via the proposed control method

Grahic Jump Location
Fig. 4

State trajectories of hyperchaotic system (16) via the proposed control method

Grahic Jump Location
Fig. 6

State trajectories of five-dimensional system (17) via the proposed control method

Grahic Jump Location
Fig. 7

State trajectories of five-dimensional system (17) with controller (5) in Ref. [28]

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