Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology

A fuzzy control approach to a class of continuous time nonlinear systems is presented in this paper. The design of control law consists of two parts: one is fuzzy control using quadratic stabilization techniques based on the so-called fuzzy dynamic model, the other is compensating control to deal with the modeling error between the nonlinear systems and their corresponding fuzzy dynamic models. An example of nonlinear system control is given to demonstrate the performance of the methods.

This paper describes the design of the robust adaptive fuzzy controller for uncertain nonlinear dynamical systems with unknown nonlinearities. These unknown nonlinearities are approximated by the fuzzy system with a set of fuzzy IF-THEN rules whose parameters are adjusted on-line according to some adaptive laws for the purpose of controlling the output of the nonlinear system to track a given trajectory. The Lyapunov synthesis approach is used to develop a state-feedback adaptive control algorithm based on the adaptive fuzzy model. The key assumption is that the reconstruction errors satisfy certain bounding conditions. The control law consists of two terms. One is the certainty equivalent control and the other is the bounding control. A bounding parameter adaptive law is used to obtain this bounding control. The overall control system guarantees that the tracking error converges in the small neighborhood of zero and that all signals involved are uniformly bounded. The theoretical results are illustrated through a simulation example.

In many practical applications, dynamic systems to be controlled have uncertain parameters. To cope with the parametric uncertainties, analysis and design methods of a robust control scheme are proposed using the feedback linearization of a class of nonlinear systems represented by Takagi-Sugeno fuzzy model. A fuzzy feedback linearization controller is proposed to stabilize the nominal fuzzy system, and a sufficient condition is derived to guarantee the stability of the closed-loop system with bounded parameter uncertainties. For the systems with bounded uncertainty, we can design a robust fuzzy controller by choosing the control parameters satisfying the robust stability condition. Because the design scheme is not a trial and error manner, it provides us freedom of selecting the control parameters showing the desired characteristics. The design methodologies are illustrated by examples and the simulation results are given to verify the validity and effectiveness of the proposed control scheme.

An approach to detecting colour specks in an image taken from a pulp sample of recycled paper is presented. The task is solved through pixel-wise colour classification by an artificial neural network and post-processing based on the evidence theory. The network is trained using possibilistic target values, which are determined through a self-organising process in a 2D and 1D map of chromaticity and lightness, respectively. The problem of post-processing of a pixelwise-classified image is addressed from the point of view of the Dempster-Shafer theory of evidence. Each neighbour of a pixel being analysed is considered as an item of evidence supporting particular hypotheses regarding the class label of that pixel. The strength of support is defined as a function of the degree of uncertainty in class label of the neighbour, and the distance between the neighbour and the pixel being considered. The experiments performed have shown that the colour classification results correspond well with the human perception of colours of the specks.