Output Feedback Control of Discrete-Time T-S Fuzzy Affine Systems Using Quantized Measurements

This paper investigates the problem of robust H!fixed-order dynamic output feedback( DOF)controller design for a class of Takagi-Sugeno( T-S) fuzzy affine systems using quantized measurements.Through a state-input augmentation method,some sufficient conditions for controller synthesis are developed based upon piecewise quadratic Lyapunov functions( PQLFs) in terms of LMIs. Two illustrative studies are conducted to verify the effectiveness of the proposed controller synthesis approach.

Experimental and modeling study of kinetics for methane hydrate formation in a crude oil-in-water emulsion

A low-viscosity emulsion of crude oil in water can be believed to be the bulk of a flow regime in a pipeline.To differentiate which crude oil would and which would not counter the blockage of flow due to gas hydrate formation in flow channels,varying amount of crude oil in water emulsion without any other extraneous additives has undergone methane gas hydrate formation in an autoclave cell.Crude oil was able to thermodynamically inhibit the gas hydrate formation as observed from its hydrate stability zone.The normalized rate of hydrate formation in the emulsion has been calculated from an illustrative chemical affinity model,which showed a decrease in the methane consumption（decreased normalized rate constant） with an increase in the oil content in the emulsion.Fourier transform infrared spectroscopy（FTIR） of the emulsion and characteristic properties of the crude oil have been used to find the chemical component that could be pivotal in selfinhibitory characteristic of the crude oil collected from Ankleshwar,India,against a situation of clogged flow due to formation of gas hydrate and establish flow assurance.

Fuzzy Set-Membership Filtering for Discrete-Time Nonlinear Systems

In this article,the problem of state estimation is addressed for discrete-time nonlinear systems subject to additive unknown-but-bounded noises by using fuzzy set-membership filtering.First,an improved T-S fuzzy model is introduced to achieve highly accurate approximation via an affine model under each fuzzy rule.Then,compared to traditional prediction-based ones,two types of fuzzy set-membership filters are proposed to effectively improve filtering performance,where the structure of both filters consists of two parts:prediction and filtering.Under the locally Lipschitz continuous condition of membership functions,unknown membership values in the estimation error system can be treated as multiplicative noises with respect to the estimation error.Real-time recursive algorithms are given to find the minimal ellipsoid containing the true state.Finally,the proposed optimization approaches are validated via numerical simulations of a one-dimensional and a three-dimensional discrete-time nonlinear systems.

OBJECT-BASED SUPER RESOLUTION FOR INTELLIGENT VISUAL SURVEILLANCE VIDEO

Construction of high resolution images from low resolution sequences is often im- portant in surveillance applications. In this letter, an affine based multi-scale block-matching image registration algorithm is first proposed. The images to be registered are divided into overlapped blocks of different size according to its motions. The Least Square (LS) image reg- istration algorithm is extended to match the blocks. Then an object based Super Resolution (SR) scheme is designed, the Maximum A Priori (MAP) super resolution algorithm is extended to enhance the resolution of the interest objects. Experimental results show that the proposed multi-scale registration method provides more accurate registration between frames. Further more, the object based super resolution scheme shows an enhanced performance compared with the traditional MAP method.

SOFC temperature evaluation based on an adaptive fuzzy controller

The operating temperature of a solid oxide fuel cell (SOFC) stack is a very important parameter to be controlled, which impacts the performance of the SOFC due to thermal cycling. In this paper, an adaptive fuzzy control method based on an affine nonlinear temperature model is developed to control the temperature of the SOFC within a specified range. Fuzzy logic systems are used to approximate nonlinear functions in the SOFC system and an adaptive technique is employed to construct the controller. Compared with the traditional fuzzy and proportion-integral-derivative (PID) control, the simulation results show that the designed adaptive fuzzy control method performed much better. So it is feasible to build an adaptive fuzzy controller for temperature control of the SOFC.

A sliding-mode variable-structure controller based on exact feedback linearization for automatic navigation system

In order to improve the path tracking accuracy and robustness of the agricultural machinery navigation system,a sliding-mode variable-structure controller based on exact feedback linearization was presented.Firstly,based on the differential geometry theory and the affine nonlinear kinematics model,the corresponding nonlinear coordinate change matrix and nonlinear state variable feedback equations were deduced,and an exact feedback linearization model was then established.Secondly,based on the exact feedback linearization model,a sliding-mode variable-structure controller was designed by selecting suitable linear switching function and exponential reaching law.Finally,the comparative experiments were carried out.And the experimental results indicated that the proposed method had a high tracking accuracy and robustness.The maximum lateral error of the straight line tracking was less than 0.06 m,and maximum lateral error of the curve path tracking was less than 0.09 m.Experimental results show that the transplanter based on this automatic navigation system can effectively track the predefined path.