A robust learning control for SISO Nonlinear systems with T-S Fuzzy Model : C02-robust control

In this paper, a robust learning control is developed for a class of single input single output (SISO) nonlinear systems with T-S fuzzy model. It is seen that the proposed sliding mode learning control with the powerful Lipshitz-like condition can guarantee the stability, convergence and robustness of the closed-loop system without involving any assumptions on uncertain system dynamics. In addition, theconcept that the local system with the maximum membership function dominates the system dynamic behaviours helps to greatly simplify the control system design. It will be further seen that the continuous learning control ensures the advantage of chattering-free that may occur in conventional sliding mode systems. Simulation examples are presented to demonstrate the effectiveness of the proposed learning control through the comparison with the H-infinity control.

Robust fault-tolerant leader-follower control of four-wheel-steering mobile robots using terminal sliding mode

This paper addresses the actuator failure compensation problem of non-linear fourwheel-steering mobile robots based on vehicle kinematics, undergoing both known and unknown failures causing degenerated steering performance or wheels stuck at some observable angles. Terminal sliding mode control technique is used to guarantee the tracking stability infinite time with the presence of actuator fault. Simulation results are given to illustrate the effectiveness of the proposed control scheme. © Institution of Engineers Australia 2012.

Derivative-free optimization and neural networks for robust regression

Large outliers break down linear and nonlinear regression models. Robust regression methods allow one to filter out the outliers when building a model. By replacing the traditional least squares criterion with the least trimmed squares (LTS) criterion, in which half of data is treated as potential outliers, one can fit accurate regression models to strongly contaminated data. High-breakdown methods have become very well established in linear regression, but have started being applied for non-linear regression only recently. In this work, we examine the problem of fitting artificial neural networks (ANNs) to contaminated data using LTS criterion. We introduce a penalized LTS criterion which prevents unnecessary removal of valid data. Training of ANNs leads to a challenging non-smooth global optimization problem. We compare the efficiency of several derivative-free optimization methods in solving it, and show that our approach identifies the outliers correctly when ANNs are used for nonlinear regression.

Robust channel estimation algorithm for dual-hop MIMO relay channels

In conventional two-phase channel estimation algorithms for dual-hop multiple-input multiple-output (MIMO) relay systems, the relay-destination channel estimated in the first phase is used for the source-relay channel estimation in the second phase. For these algorithms, the mismatch between the estimated and the true relay-destination channel affects the accuracy of the source-relay channel estimation. In this paper, we investigate the impact of such channel state information (CSI) mismatch on the performance of the two-phase channel estimation algorithm. By explicitly taking into account the CSI mismatch, we develop a robust algorithm to estimate the source-relay channel. Numerical examples demonstrate the improved performance of the proposed algorithm. © 2012 IEEE.

The robust result in meta-analysis of aid effectiveness : a response to Mekasha and Tarp

In this response to Mekasha and Tarp (2013) we show that contrary to what they state, their study validates our basic analysis. They confirm that the literature finds that aid is of little economic importance in generating growth. The results also show that the literature systematically selects control variables for their effect on aid effectiveness. We argue that their choice of the random effects model is not appropriate for the problem at hand, and that the way they use multiple meta-regression analysis contradicts the robust results reached at the basic analysis.

New results in robust functional state estimation using two sliding mode observers in cascade

 This paper presents a functional observer scheme using two sliding mode observers in cascade. A coordinate transformation is performed on the system such that existing sliding mode observer theory can be directly applied to achieve functional state estimation. The necessary and sufficient existence conditions for the scheme (in terms of the original system matrices) are also investigated, and they are found to be less stringent than earlier work on functional state estimation using one sliding mode observer; this could have benefits in terms of cost and simplicity. A numerical example verifies the effectiveness of the scheme.

Robust, electro-conductive, self-healing superamphiphobic fabric prepared by one-step vapour-phase polymerisation of poly(3,4-ethylenedioxythiophene) in the presence of fluorinated decyl polyhedral oligomeric silsesquioxane and fluorinated alkyl silane

A robust, electrically conductive, superamphiphobic fabric was prepared by vapour-phase polymerisation of 3,4-ethylenedioxythiophene (EDOT) on fabric in the presence of fluorinated decyl polyhedral oligomeric silsesquioxane (FD-POSS) and a fluorinated alkyl silane (FAS). The coated fabric had contact angles of 169° and 156° respectively to water and hexadecane, and a surface resistance in the range of 0.8–1.2 kΩ o⁻¹ . The incorporation of FD-POSS and FAS into the PEDOT layer showed a very small influence on the conductivity but improved the washing and abrasion stability considerably. The coated fabric can withstand at least 500 cycles of standard laundry and 10000 cycles of abrasion without apparently changing the superamphiphobicity, while the conductivity only had a small reduction after the washing and abrasion. More interestingly, the coating had a self-healing ability to auto-repair from chemical damages to restore the liquid repellency.

Robust, self-healing superamphiphobic fabrics prepared by two-step coating of fluoro-containing polymer, fluoroalkyl silane, and modified silica nanoparticles

A robust, superamphiphobic fabric with a novel self-healing ability to autorepair from chemical damage is prepared by a two-step wet-chemistry coating technique using an easily available material system consisting of poly(vinylidene fluoride-co-hexafluoropropylene), fluoroalkyl silane, and modified silica nanoparticles. The coated fabrics can withstand at least 600 cycles of standard laundry and 8000 cycles of abrasion without apparently changing the superamphiphobicity. The coating is also very stable to strong acid/base, ozone, and boiling treatments. After being damaged chemically, the coating can restore its super liquid-repellent properties by a short-time heating treatment or room temperature ageing. This simple but novel and effective coating system may be useful for the development of robust protective clothing for various applications.

A modified moment-based image watermarking method robust to cropping attack

Developing a watermarking method that is robust to cropping attack is a challenging task in image watermarking. The moment-based watermarking schemes show good robustness to common signal processing attacks and some geometric attacks but are sensitive to cropping attack. In this paper, we modify the moment-based approach to deal with cropping attack. Firstly, we find the probability density function (pdf) of the pixel value distribution from the original image. Secondly, we reshape and normalize the pdf of the pixel value distribution (PPVD) to form a two dimensional image. Then, the moment invariants are calculated from the PPVD image. Since PPVD is insensitive to cropping, the proposed method is robust to cropping attack. Besides, it also has high robustness against other common attacks. Experimental results demonstrate the effectiveness of the proposed method.

Vesselness features and the inverse compositional AAM for robust face recognition using thermal IR

Over the course of the last decade, infrared (IR) and particularly thermal IR imaging based face recognition has emerged as a promising complement to conventional, visible spectrum based approaches which continue to struggle when applied in the real world. While inherently insensitive to visible spectrum illumination changes, IR images introduce specific challenges of their own, most notably sensitivity to factors which affect facial heat emission patterns, e.g. emotional state, ambient temperature, and alcohol intake. In addition, facial expression and pose changes are more difficult to correct in IR images because they are less rich in high frequency detail which is an important cue for fitting any deformable model. In this paper we describe a novel method which addresses these major challenges. Specifically, to normalize for pose and facial expression changes we generate a synthetic frontal image of a face in a canonical, neutral facial expression from an image of the face in an arbitrary pose and facial expression. This is achieved by piecewise affine warping which follows active appearance model (AAM) fitting. This is the first publication which explores the use of an AAM on thermal IR images; we propose a pre-processing step which enhances detail in thermal images, making AAM convergence faster and more accurate. To overcome the problem of thermal IR image sensitivity to the exact pattern of facial temperature emissions we describe a representation based on reliable anatomical features. In contrast to previous approaches, our representation is not binary; rather, our method accounts for the reliability of the extracted features. This makes the proposed representation much more robust both to pose and scale changes. The effectiveness of the proposed approach is demonstrated on the largest public database of thermal IR images of faces on which it achieved 100% identification rate, significantly outperforming previously described methods