摘要：This paper analyses the climate change projected for the near and distant future in South America using MRI/JMA (Japanese Meteorological Agency) global model simulations with resolutions of 20 and 60 km. Changes in mean climate, as well as in the annual cycles and interannual variability of temperature and precipitation are discussed. An analysis is also made of the uncertainties of the 60 km resolution model experiments. For the near and distant future, both, the 20 km and 60 km resolution MRI/JMA models project that temperature changes will be positive in all seasons. The greatest values of change are over the Andes and over tropical and subtropical latitudes of the study region. In all the subregions analysed, the 20 km model projects greater changes in the annual cycle of mean temperature than the 60 km model. Changes in summer precipitation are positive over most of the continent, except for southern Chile. Autumn precipitation is projected to increase over northern Argentina and north-western South America and to decrease over central Chile in winter, which might be due to the southward shift of the Pacific storm-track. The most significant positive change in Southeastern South America (SESA) is projected to occur in spring precipitation. In general, projected changes in the annual cycle are greater in the rainy seasons of each subregion. No significant changes are expected in the interannual variability of temperature and precipitation. La Plata basin is projected to experience increased runoff, which would indicate that the projected rise in precipitation would have stronger effect than projected warming. The analysis of climate projection uncertainties revealed that temperature projections are more reliable than precipitation projections;and that uncertainty in near future simulations is greater than in simulations of the end of the century.

摘要：Performance robustness problems via the state feedback controller are investigated for a class of uncertain nonlinear systems with time-delay in both state and control, in which the neural networks are used to model the nonlinearities. By using an appropriate uncertainty description and the linear difference inclusion technique, sufficient conditions for existence of such controller are derived based on the linear matrix inequalities （LMIs）. Using solutions of LMIs, a state feedback control law is proposed to stabilize the perturbed system and guarantee an upper bound of system performance, which is applicable to arbitrary time-delays.

摘要：Functional performance variations of products and systems are often used to measure the qualities of products and systems considering the changes in the design parameter values caused by uncertainties.A robust design approach has been developed in this research to minimize the functional performance variations considering the design parameter uncertainties by identifying the boundaries of the functional performance variations through *** this work,a mathematical model is developed to describe the relationships among functional performance,design configurations and parameters,and design parameter uncertainties.A multi-level optimization model is established to identify:(1)The optimal design configuration,(2)the optimal values of design parameters,and(3)the boundaries of functional performance *** analysis considering the impact of parameter uncertainties on functional performance variation boundaries has also been conducted.A case study on the design of a truss system has been *** study results show that the sensitivities of functional performance variation boundaries to the design parameter uncertainties can be reduced significantly using the new robust design approach.

摘要：An adaptable product can satisfy different customer requirements by changing its configuration and parameter values during the operation stage. Design of adaptable products aims at reducing the environment impact through replacement of multiple different products with single adaptable ones. Due to the complex architec- ture, multiple functional requirements, and changes of product configurations and parameter values in operation, impact of uncertainties to the functional performance measures needs to be considered in design of adaptable products. In this paper, a robust design approach is introduced to identify the optimal design configuration and parameters of an adaptable product whose functional performance measures are the least sensitive to uncertain- ties. An adaptable product in this paper is modeled by both configurations and parameters. At the configuration level, methods to model different product configuration candi- dates in design and different product configuration states in operation to satisfy design requirements are introduced. At the parameter level, four types of product/operating parameters and relations among these parameters are discussed. A two-level optimization approach is developed to identify the optimal design configuration and its parameter values of the adaptable product. A case study is implemented to illustrate the effectiveness of the newly developed robust adaptable design method.

摘要：We propose a new method for robust adaptive backstepping control of nonlinear systems with parametric uncertainties and disturbances in the strict feedback form. The method is called dynamic surface control. Traditional backstepping algorithms require repeated differentiations of the modelled nonlinearities. The addition of n first order low pass filters allows the algorithm to be implemented without differentiating any model nonlinearities, thus ending the complexity arising due to the 'explosion of terms' that makes other methods difficult to implement in practice. The combined robust adaptive backstepping/first order filter system is proved to be semiglobally asymptotically stable for sufficiently fast filters by a singular perturbation approach. The simulation results demonstrate the feasibility and effectiveness of the controller designed by the method.