Interval Type-2 Fuzzy Model Predictive Control of Nonlinear Networked Control Systems

In this paper, the problem of fuzzy predictive control of nonlinear networked control systems subject to parameter uncertainties and defective communication links is studied. Stochastic variables with Bernoulli random binary distribution are used to represent the defective communication links with p...

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Published inIEEE transactions on fuzzy systems Vol. 23; no. 6; pp. 2317 - 2328
Main Authors Lu, Qing, Shi, Peng, Lam, Hak-Keung, Zhao, Yuxin
Format Journal Article
LanguageEnglish
Published New York IEEE 01.12.2015
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Fuzzy logic
Input constraints
Interval type-2 T-S fuzzy model
Model predictive control
Optimization
Packets loss
Parameter uncertainties
Predictive control
Predictive models
Stability analysis
Symmetric matrices
Systems stability
Uncertain systems
Uncertainty
Vectors
Input constraints
parameter uncertainties
interval type-2 T–S fuzzy model
packets loss
model predictive control (MPC)
Online AccessGet full text
ISSN1063-6706
1941-0034
DOI10.1109/TFUZZ.2015.2417975

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Abstract In this paper, the problem of fuzzy predictive control of nonlinear networked control systems subject to parameter uncertainties and defective communication links is studied. Stochastic variables with Bernoulli random binary distribution are used to represent the defective communication links with packets loss occurring intermittently between the controller and the physical plant. An interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy model is employed to describe the nonlinear plant subject to parameter uncertainties, which can be captured with the lower and upper membership functions. The IT2 fuzzy model and IT2 fuzzy controller are not required to share the same lower and upper membership functions. In order to design the state-feedback fuzzy model predictive controller, an optimization problem which minimizes the upper bound of a quadratic objective function subject to input constraints and packets dropout is formulated and solved at every sampling instant in the finite time horizon. By introducing some slack matrices, less conservative conditions are developed for system stability analysis. Two examples are given to demonstrate the effectiveness and merits of the proposed new design techniques.
AbstractList In this paper, the problem of fuzzy predictive control of nonlinear networked control systems subject to parameter uncertainties and defective communication links is studied. Stochastic variables with Bernoulli random binary distribution are used to represent the defective communication links with packets loss occurring intermittently between the controller and the physical plant. An interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy model is employed to describe the nonlinear plant subject to parameter uncertainties, which can be captured with the lower and upper membership functions. The IT2 fuzzy model and IT2 fuzzy controller are not required to share the same lower and upper membership functions. In order to design the state-feedback fuzzy model predictive controller, an optimization problem which minimizes the upper bound of a quadratic objective function subject to input constraints and packets dropout is formulated and solved at every sampling instant in the finite time horizon. By introducing some slack matrices, less conservative conditions are developed for system stability analysis. Two examples are given to demonstrate the effectiveness and merits of the proposed new design techniques.
Author Peng Shi
Qing Lu
Hak-Keung Lam
Yuxin Zhao
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parameter uncertainties
interval type-2 T–S fuzzy model
packets loss
model predictive control (MPC)
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SubjectTerms Fuzzy logic
Input constraints
Interval type-2 T-S fuzzy model
Model predictive control
Optimization
Packets loss
Parameter uncertainties
Predictive control
Predictive models
Stability analysis
Symmetric matrices
Systems stability
Uncertain systems
Uncertainty
Vectors
Title Interval Type-2 Fuzzy Model Predictive Control of Nonlinear Networked Control Systems
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