Worst Case Uncertainty Construction via Multifrequency Gain Maximization With Application to Flutter Control

In the analysis of uncertain systems, we often search for a worst case perturbation that drives the H∞ gain of the system to the maximum over the set of allowable uncertainties. Employing the classical technique, an uncertainty sample is obtained, which, indeed, maximizes the gain but only at the single frequency where that maximum occurs. In contrast, this article considers a method to calculate a worst case perturbation that maximizes the gain of a system with mixed uncertainty at multiple frequencies simultaneously. This approach involves a nonlinear optimization that selects the worst case value of the uncertain parameters and the application of the boundary Nevanlinna-Pick interpolation to calculate the dynamic uncertainty sample. Such a perturbation can be used to augment Monte Carlo simulations of uncertain systems, especially if the system has multiple resonance frequencies. The worst case analysis of a flutter control system designed for a small flexible aircraft is provided to demonstrate the applicability of the proposed method

Model Based Automatic Control Design for the T-FLEX Demonstrator Using RCE Environment

The main goal of the paper is to develop automatic control design methods for flexible aircraft. The motivation for the research is that such automatic control generation enables the inclusion of the control design algorithms into the multidisciplinary design optimization (MDO) of aircraft design. In such an extended MDO framework, called co-design, the sizing, structural dynamics, aerodynamics and the controllers of the aircraft are optimized in one single step. This way control technologies can be included early-on in the preliminary design stage of aircraft design. Since the control design is model based, first a control oriented aeroservoelastic model needs to be developed. The modeling is done via the bottom-up modelling approach. The model generation also needs to be automatic due to parameter changes resulting from theMDOprocess. The research focuses on flexible aircraft, therefore, the control algorithms include baseline, manoeuvre load alleviation, gust load alleviation and flutter suppression controllers. All of these algorithms needs to be developed in such way that they can automatically executed in the MDO process. The overall MDO framework is based on the Remote Component Environment (RCE) environment and the aircraft investigated is the T-Flex demonstrator of the FLIPASED project. The paper presents the main concepts of the modeling and control synthesis, analysis for the above mentioned four controllers and the most important aspects of integrating such automatic control design methods into the RCE environment

Conjugation of haloalkanes by bacterial and mammalian glutathione transferases: Mono- and dihalomethanes

A primary route of metabolism of dihalomethanes occurs via glutathione (GSH) transferase-catalyzed conjugation. Mammalian theta class GSH transferases and a group of bacterial dichloromethane dehalogenases are able to catalyze the hydrolytic dehalogenation of dihalomethanes via GSH conjugation and subsequent formation of HCHO. Dihalomethanes have been shown to induce revertants in Salmonella typhimurium TA 1535 expressing theta class GSH transferases. Two mammalian theta class GSH transferases (rat GST 5-5 and human GST T1) and the bacterial dehalogenase DM11 were compared in the in vitro conjugation of CH3Cl and using in vitro assays (HCHO formation) and the S. typhimurium mutagenesis assay with the dihalomethanes CH2Cl2, CH2Br2, CH2BrCl, CH2ICl, CH2I2, and CH2ClF. GSTs 5-5 and TI had similar characteristics and exhibited first-order rather than Michaelis-Menten kinetics for HCHO formation over the range of dihalomethane concentrations tested. In contrast, the DM11 enzyme displayed typical hyperbolic Michaelis-Menten kinetics for all of the compounds tested. A similar pattern was observed for the conjugation of CH3Cl The reversion tests with S. typhimurium expressing DM11 or GST 5-5 showed a concentration-dependent increase in revertants for most of the dihalomethanes, and DM11 produced revertants at dihalomethane concentrations lower than GST 5-5. Collectively, the results indicate that rates of conversion of dihalomethanes to HCHO are not correlated with mutagenicity and that GSH conjugates are genotoxic. The results are compared with the conjugation and genotoxicity of haloethanes in the preceding paper in this issue [Wheeler, J. B., Stourman, N. V., Armstrong, R. N., and Guengerich, F. P. (2001) Chem. Res. Toxicol. 14, 1107-1117]. The halide order appears most important in the dihalomethane conjugation reactions catalyzed by GST 5-5 and less so in GST T1 and DM11, probably due to changes in the rate-limiting steps