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The paper focuses on the design of a robust H-infinity attitude controller for an unmanned small-scale helicopter. To take into account the salient nonlinearities, a model with six-degrees-of-freedom nonlinear dynamics and some linear approximation of the aerodynamic parts are used when extracting a linear model and performing simulations to check the performance of the designed controller. To design a robust H-infinity controller, an augmented plant is constructed by adjusting several weighting functions. Then, a robust controller is synthesized utilizing the augmented system with the weighting functions and H-infinity control methodology. Using computer simulation it is shown that the H-infinity controller works well when applied to the nonlinear model even though it is designed using a linear model approximation. Through frequency response analysis, it is shown that the proposed controller can overcome more than half of the uncertainty variations around a nominal point at the input side. The time-domain simulation with the nonlinear model demonstrates that the proposed controller is very robust in relation to the uncertainties, as was expected, overcoming large gain uncertainties and time delay in each input channel. The analysis and simulation results also show that the control system satisfies the Level 1 handling requirements, as defined in Aeronautical Design Standard ADS-33E-PRF. [ABSTRACT FROM AUTHOR]

Both the crushing and indentation behaviors of sandwich materials are important aspects in failure analysis and energy absorption. In this paper, the core crushing strength of honeycomb materials from the experiment is briefly introduced, and the indentation of sandwich structures is studied in detail. Using the beam on elastic-plastic foundation models, theoretical formulations for predicting indentation behavior of sandwich materials are proposed, from which the mechanical response of elastic-plastic sandwich beams are obtained. Three stages of failure are clearly depicted by the global stiffness changes in the load versus displacement curve of elastic-plastic beams. The models are compared with the available experimental data and numerical simulation, and relatively close agreements are achieved. The compliance and compliance gradient derived form the indentation models are then incorporated with the equation of motion of the projectile to study impact response of elastic-plastic beams, and the impact energy dissipation due to the plasticity of elastic-plastic sandwich beam is uniquely recovered from the derived damping ratio. The beam on elastic-plastic foundation models proposed can be used to predict the indentation behavior of honeycomb sandwich materials, and the unique incorporation of the compliance and compliance gradient of the elastic-plastic sandwich beams in the standard mass-spring model can be utilized to characterize the effect of core compression and plasticity in the impact process. [ABSTRACT FROM AUTHOR]

The integration of modeling and simulation tools with robust and efficient methods of optimal design offers a rational approach to explore new concepts and designs. However, a widespread adaptation of these tools in the industry design environment will require that they incorporate a systematic analysis of uncertainty in all aspects of the design process. A lack of confidence in designs generated in a simulation-based approach is the result of uncertainties in the predictive capabilities of physics-based models used in the simulations, and poor representation of uncertainties and their propagation in a coupled systems engineering design problem. A data- and knowledge-lean environment, typical of a design process involving novel concepts, further exacerbates the situation; design engineers often make gross assumptions about distributional information of random variables and parameters, thereby adding to the uncertainty associated with the design results. The paper focuses on numerical and analytical tools by which to model uncertainty and risk in a simulation-based design environment, including cases where the uncertainty does not conform to standard probabilistic distributions. A specific focus of the modeling effort is an approach to establish confidence intervals for response predictions available from analytical and numerical models, as well as surrogate approximations used in the design process. Innovative adaptations of formal optimization methods in a nondeterministic design setting are discussed, including design problem formulations that examine the nondeterministic design problem in a multicriteria optimization framework. Simple design problems are used to illustrate the concepts and to underscore the deficiencies in a purely deterministic approach to the design problem. [ABSTRACT FROM AUTHOR]