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41.
Robert B. Anderson Julius A. Marshall Andrea L’Afflitto 《Journal of The Franklin Institute》2021,358(12):6281-6308
In classical model reference adaptive control (MRAC), the adaptive rates must be tuned to meet multiple competing objectives. Large adaptive rates guarantee rapid convergence of the trajectory tracking error to zero. However, large adaptive rates may also induce saturation of the actuators and excessive overshoots of the closed-loop system’s trajectory tracking error. Conversely, low adaptive rates may produce unsatisfactory trajectory tracking performances. To overcome these limitations, in the classical MRAC framework, the adaptive rates must be tuned through an iterative process. Alternative approaches require to modify the plant’s reference model or the reference command input. This paper presents the first MRAC laws for nonlinear dynamical systems affected by matched and parametric uncertainties that constrain both the closed-loop system’s trajectory tracking error and the control input at all times within user-defined bounds, and enforce a user-defined rate of convergence on the trajectory tracking error. By applying the proposed MRAC laws, the adaptive rates can be set arbitrarily large and both the plant’s reference model and the reference command input can be chosen arbitrarily. The user-defined rate of convergence of the closed-loop plant’s trajectory is enforced by introducing a user-defined auxiliary reference model, which converges to the trajectory tracking error obtained by applying the classical MRAC laws before its transient dynamics has decayed, and steering the trajectory tracking error to the auxiliary reference model at a rate of convergence that is higher than the rate of convergence of the plant’s reference model. The ability of the proposed MRAC laws to prescribe the performance of the closed-loop system’s trajectory tracking error and control input is guaranteed by barrier Lyapunov functions. Numerical simulations illustrate both the applicability of our theoretical results and their effectiveness compared to other techniques such as prescribed performance control, which allows to constrain both the rate of convergence and the maximum overshoot on the trajectory tracking error of uncertain systems. 相似文献
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This paper describes 3 language learning approaches common in many urban and rural Ojibwe communities, as well as the ideologies of endangerment that drive and sustain them. Drawing from collaborative language revitalization work with teachers, learners, and community leaders, we analyze some of the teaching and learning practices that lead to the common mismatch between language learner goals and expectations, on the one hand, and the outcomes of language learning, on the other. We outline how these 3 approaches to language learning relate to cultural identities and place-based notions of authenticity as well as to current findings in the field of second language acquisition. We then profile 2 speakers who have learned Ojibwe successfully as adults to illustrate how their success was possible largely because they were able to engage with the Ojibwe language in interactive ways that run counter to common language learning approaches. We suggest that for language revitalization efforts, and individual learners, to experience higher levels of success, greater attention needs to be paid to how ideologies of endangerment impact language learning approaches. 相似文献
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Michael J. Lichtenstein Steven V. Owen Cheryl L. Blalock Yan Liu Kacy A. Ramirez Linda A. Pruski Carolyn E. Marshall Mary Anne Toepperwein 《科学教学研究杂志》2008,45(5):600-616
The central purposes of this study were to review the development and evolution of the Scientific Attitude Inventory (SAI) and then reevaluate the psychometric properties of the revised form of the SAI, the Scientific Attitude Inventory II (SAI‐II). The SAI‐II was administered to a convenience sample of 543 middle and high school students from five teachers in four schools in four school districts in San Antonio, Texas, at the beginning of the 2004–2005 school year. Confirmatory factor analysis on the full data set failed to support the existence of a 12‐factor structure (as proposed by the scale developers) or a one‐factor structure. The data were then randomly divided into exploratory [exploratory factor analysis (EFA)] validation and confirmatory [confirmatory factor analysis (CFA)] cross‐validation sets. Exploratory and confirmatory models yielded a three‐factor solution that did not fit the data well [χ2 (321) = 646, p < .001; RMSEA = .061 (.90 CI = .054–.068); and CFI = .81]. The three factors were labeled “Science is About Understanding and Explaining” (13 items), “Science is Rigid” (6 items), and “I Want to Be a Scientist” (8 items). The α‐coefficients for these three factors ranged from 0.59 to 0.85. Whether these identified subscales are valid will require independent investigation. In this sample, and consistent with prior publications, the SAI‐II in its current form did not have satisfactory psychometric properties and cannot be recommended for further use. © 2008 Wiley Periodicals, Inc. J Res Sci Teach 45: 600–616, 2008 相似文献
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