This paper considers the problem of designing robust H∞ controllers for for HIV/AIDS infection system. The Takagi-Sugeno (TS) fuzzy model is adopted for fuzzy modelling of the HIV infection dynamic systems. A sufficient condition of the controller for this system is given in term of Linear Matrix Inequalities (LMIs). The effectiveness of the proposed controller design methodology is finally dem...

In this paper we design a state-feedback controller for the nonlinear benchmark problem. Our approach relies on the use of Takagi-Sugeno fuzzy models to approximate the nonlinear system. Once €he fuzzy model is obtained, we develop a guaranteed-cost framework to design the controller using Linear Matrix Inequality methods and recently obtained relaxed stability conditions. We show that our prop...

In the case of widely-uncertain non-linear system control design, it was very difficult to design a single controller to overcome control design specifications in all of its dynamical characteristics uncertainties. To resolve these problems, a new design method of robust fuzzy control proposed. The solution offered was by creating multiple soft-switching with Takagi-Sugeno fuzzy model for optim...

This work presents a method to incorporate standard neuro-fuzzy learning for Takagi–Sugeno fuzzy systems that evolve under a grammar driven genetic programming (GP) framework. This is made possible by introducing heteroglossia in the functional GP nodes, enabling them to switch behavior according to the selected learning stage. A context-free grammar supports the expression of arbitrarily sized...

It is shown that the Takagi-Sugeno-Kang (TSK) fuzzy inference mechanism is optimal with respect to a certain criterion that quantiies the controversy between the inference made, and the knowledge represented in the knowledge-base.

This paper presents a tool condition monitoring approach using Takagi-Sugeno-Kang (TSK) fuzzy logic incorporating a subtractive clustering method. The experimental results show its effectiveness and satisfactory comparisons with several other artificial intelligence methods.

This paper considers the problem of filter design for two-dimensional (2D) discrete-time non-linear systems in Takagi-Sugeno (T-S) fuzzy mode. The to be solved is find a H ∞ model such that filtering error system asymptotically stable. A numerical example employed illustrate validity proposed methods.

In this paper, subtractive clustering method is combined with least squares estimation algorithms to pre-identify a type-1 Takagi-Sugeno-Kang (TSK) fuzzy model from input/output data. Then the type-2 fuzzy theory is used to expand the type-1 model to a type-2 model. A sensitivity analysis is used to ascertain how a type-1 TSK model output depends upon the pre-initialized parameters and determin...

System identification is the task to map several related components of a real world system into a model. If this is done by transferring human expertise into a model, the process is called knowledge-driven modeling. If the system information is embedded in data-bases and the implicit existent expertise is mapped by algorithms into a model, the process is called data-driven modeling. This thesis...

This paper deals with the synthesis of fuzzy state feedback controller of induction motor with optimal performance. First, the Takagi-Sugeno (T-S) fuzzy model is employed to approximate a non linear system in the synchronous d-q frame rotating with electromagnetic field-oriented. Next, a fuzzy controller is designed to stabilise the induction motor and guaranteed a minimum disturbance attenuati...