Dynamic Takagi-Sugeno Fuzzy Models Research Articles

Internal Model Control Design for Nonlinear Systems Based on Inverse Dynamic Takagi–Sugeno Fuzzy Model

Internal Model Control Design for Nonlinear Systems Based on Inverse Dynamic Takagi–Sugeno Fuzzy Model

Robust H∞ Control for Bilinear Systems Using the Dynamic Takagi-Sugeno Fuzzy Models Based on Linear Matrix Inequalities

Robust H∞ Control for Bilinear Systems Using the Dynamic Takagi-Sugeno Fuzzy Models Based on Linear Matrix Inequalities

Non‐linear adaptive control of a heat exchanger

AbstractIn this contribution, two methods for adaptation of non‐linear adaptive controllers are presented and compared, namely the data‐driven and the knowledge‐based adaptation. A dynamic Takagi–Sugeno fuzzy model is utilized to model the non‐linear process behaviour. Based on this model, a non‐linear predictive controller is designed to control the process. In the presence of time‐variant process behaviour and changing unmodelled disturbances, high control performance can be achieved by performing an on‐line adaptation of the fuzzy model. First, a local weighted recursive least‐squares algorithm is used for adaptation. It exploits the local linearity of the Takagi–Sugeno fuzzy model. In the second approach, process knowledge that is obtained from theoretical insights is utilized to design a knowledge‐based adaptation strategy. Both approaches are compared and their effectiveness and real‐world applicability are demonstrated by application to temperature control of a heat exchanger. Copyright © 2001 John Wiley & Sons, Ltd.

On the interpretation and identification of dynamic Takagi-Sugeno fuzzy models

Dynamic Takagi-Sugeno fuzzy models are not always easy to interpret, in particular when they are identified from experimental data. It is shown that there exists a close relationship between dynamic Takagi-Sugeno fuzzy models and dynamic linearization when using affine local model structures, which suggests that a solution to the multiobjective identification problem exists. However, it is also shown that the affine local model structure is a highly sensitive parametrization when applied in transient operating regimes. Due to the multiobjective nature of the identification problem studied here, special considerations must be made during model structure selection, experiment design, and identification in order to meet both objectives. Some guidelines for experiment design are suggested and some robust nonlinear identification algorithms are studied. These include constrained and regularized identification and locally weighted identification. Their usefulness in the present context is illustrated by examples.

Quadratic stability analysis of the Takagi-Sugeno fuzzy model

The nonlinear dynamic Takagi-Sugeno fuzzy model with offset terms is analyzed as a perturbed linear system. A sufficient criterion for the robust stability of this nominal system against nonlinear perturbations guarantees quadratic stability of the fuzzy model. The criterion accepts a convex programming formulation of reduced computational cost compared to the common Lyapunov matrix approach. Parametric robust control techniques suggest synthesis tools for stabilization of the fuzzy system. Application examples on fuzzy models of nonlinear plants advocate the efficiency of the method. The examples demonstrate reduced conservatism compared to norm-based criteria.