Type-1 and Type-2 effective Takagi-Sugeno fuzzy models for decentralized control of multi-input-multi-output processes

L 2 Gain Control of Takagi-sugeno Fuzzy Models

This paper presents L 2 gain controller synthesis for the fuzzy T-S model (Takagi-Sugeno). Both the state-feedback controller and the output feedback controller are discussed. They are in the format of PDC (parallel distributed compensation) controller which is essentially a nonlinear controller. Based on the bounded-real lemma, the synthesis procedure is expressed in LMI conditions which can b...

Stable multi-input multi-output adaptive fuzzy/neural control

In this letter, stable direct and indirect adaptive controllers are presented that use Takagi–Sugeno (T–S) fuzzy systems, conventional fuzzy systems, or a class of neural networks to provide asymptotic tracking of a reference signal vector for a class of continuous time multi-input multi-output (MIMO) square nonlinear plants with poorly understood dynamics. The direct adaptive scheme allows for...

On Multi-objective Identification of Takagi-sugeno Fuzzy Model Parameters

The problem of identifying the parameters of the constituent local linear models of Takagi-Sugeno fuzzy models is considered. In order to address the tradeoff between global model accuracy and interpretability of the local models as linearizations of a nonlinear system, two multi-objective identification algorithms are studied. Particular attention is paid to the analysis of conflicts between o...

Takagi-Sugeno Fuzzy Modeling for Process Control

Multiobjective identification of Takagi-Sugeno fuzzy models

The problem of identifying the parameters of the constituent local linear models of Takagi–Sugeno fuzzy models is considered. In order to address the tradeoff between global model accuracy and interpretability of the local models as linearizations of a nonlinear system, two multiobjective identification algorithms are studied. Particular attention is paid to the analysis of conflicts between ob...