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Thematische Bibliographien / Control uncertainty / Zeitschriftenartikel

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Zeitschriftenartikel zum Thema „Control uncertainty“

Autor: Grafiati

Veröffentlicht am 4. Juni 2021

Zuletzt aktualisiert am 13. Februar 2022

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1

Savkin, Andrey V., and Ian R. Petersen. "Minimax optimal control of uncertain systems with structured uncertainty." International Journal of Robust and Nonlinear Control 5, no. 2 (1995): 119–37. http://dx.doi.org/10.1002/rnc.4590050204.

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2

Amore, Mario Daniele, and Alessandro Minichilli. "Local Political Uncertainty, Family Control, and Investment Behavior." Journal of Financial and Quantitative Analysis 53, no. 4 (2018): 1781–804. http://dx.doi.org/10.1017/s002210901800025x.

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Estimating difference-in-differences models on a comprehensive data set of Italian companies, we provide novel insights into the literature on political uncertainty and firm investment. We first establish thatlocalpolitical uncertainty leads to declining investment. Next, we show thatfamily controlneutralizes this effect: Family firms are more likely than other firms to invest during politically uncertain times, especially when operating in industries dependent on public spending and/or managed by family members. Finally, we document that this investment resilience of family firms under politi

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3

Khlebalin, N. A. "Control of nonlinear and uncertain systems modal control under interval uncertainty of parameters." Computational Mathematics and Modeling 5, no. 1 (1994): 44–51. http://dx.doi.org/10.1007/bf01128578.

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4

Savkin, Andrey V., and Ian R. Petersen. "An uncertainty averaging approach to optimal guaranteed cost control of uncertain systems with structured uncertainty." Automatica 31, no. 11 (1995): 1649–53. http://dx.doi.org/10.1016/0005-1098(95)00080-g.

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5

Rosolia, Ugo, Xiaojing Zhang, and Francesco Borrelli. "Data-Driven Predictive Control for Autonomous Systems." Annual Review of Control, Robotics, and Autonomous Systems 1, no. 1 (2018): 259–86. http://dx.doi.org/10.1146/annurev-control-060117-105215.

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In autonomous systems, the ability to make forecasts and cope with uncertain predictions is synonymous with intelligence. Model predictive control (MPC) is an established control methodology that systematically uses forecasts to compute real-time optimal control decisions. In MPC, at each time step an optimization problem is solved over a moving horizon. The objective is to find a control policy that minimizes a predicted performance index while satisfying operating constraints. Uncertainty in MPC is handled by optimizing over multiple uncertain forecasts. In this case, performance index and o

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6

Pesch, Alexander H., та Jerzy T. Sawicki. "3B11 Uncertainty Range Estimation for μ-Synthesis Control of AMB Spindle". Proceedings of the Symposium on the Motion and Vibration Control 2010 (2010): _3B11–1_—_3B11–9_. http://dx.doi.org/10.1299/jsmemovic.2010._3b11-1_.

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7

Wang, Yilin, Hamid Reza Karimi, and Zhengrong Xiang. "Delay-Dependent Control for Networked Control Systems with Large Delays." Mathematical Problems in Engineering 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/643174.

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We consider the problems of robust stability and control for a class of networked control systems with long-time delays. Firstly, a nonlinear discrete time model with mode-dependent time delays is proposed by converting the uncertainty of time delay into the uncertainty of parameter matrices. We consider a probabilistic case where the system is switched among different subsystems, and the probability of each subsystem being active is defined as its occurrence probability. For a switched system with a known subsystem occurrence probabilities, we give a stochastic stability criterion in terms of

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8

Glavin, Paul, and Scott Schieman. "Control in the Face of Uncertainty." Social Psychology Quarterly 77, no. 4 (2014): 319–43. http://dx.doi.org/10.1177/0190272514546698.

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The mental health benefits of the sense of personal control are well documented, but do these benefits persist in social contexts of powerlessness and uncertainty? Drawing from two national panel surveys of American and Canadian workers, we examine whether the association between perceived control and reduced distress is undermined by the uncertainty of threatened employment. While we find evidence that higher levels of perceived control are associated with reduced distress, the association is curvilinear among insecure workers, such that subsequent increases in control produce diminishing red

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9

Hartl, Richard F., Sean Holly, and Andrew Hughes-Hallet. "Optimal Control, Expectations and Uncertainty." Economic Journal 101, no. 407 (1991): 976. http://dx.doi.org/10.2307/2233873.

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10

Pedersini, R. "Perceived control and visual uncertainty." Journal of Vision 12, no. 9 (2012): 5. http://dx.doi.org/10.1167/12.9.5.

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11

Lahiri, Kajal. "Optimal control, expectations and uncertainty." International Journal of Forecasting 6, no. 2 (1990): 255–56. http://dx.doi.org/10.1016/0169-2070(90)90012-z.

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12

Hansen, Lars Peter, and Thomas J. Sargent. "Robust Control and Model Uncertainty." American Economic Review 91, no. 2 (2001): 60–66. http://dx.doi.org/10.1257/aer.91.2.60.

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13

Metcalfe, A. V., S. Holly, and A. H. Hallett. "Optimal Control, Expectations and Uncertainty." Statistician 40, no. 3 (1991): 347. http://dx.doi.org/10.2307/2348293.

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14

Kanyamibwa, Felicien, and J. Keith Ord. "ECONOMIC PROCESS CONTROL UNDER UNCERTAINTY." Production and Operations Management 9, no. 2 (2000): 184–202. http://dx.doi.org/10.1111/j.1937-5956.2000.tb00333.x.

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15

Tzes, Anthony, and Ke Le. "Weighted minimum uncertainty prediction control." Automatica 32, no. 5 (1996): 793–95. http://dx.doi.org/10.1016/0005-1098(95)00201-4.

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16

Huang, Hening. "Uncertainty-based measurement quality control." Accreditation and Quality Assurance 19, no. 2 (2014): 65–73. http://dx.doi.org/10.1007/s00769-013-1032-5.

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17

Orukpe, Patience E. "Model Predictive Control of Uncertain Constrained Linear System Based on Mixed ℋ2/ℋ∞Control Approach". Journal of Control Science and Engineering 2012 (2012): 1–12. http://dx.doi.org/10.1155/2012/402948.

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Uncertain constrained discrete-time linear system is addressed using linear matrix inequality based optimization techniques. The constraints on the inputs and states are specified as quadratic constraints but are formulated to capture hyperplane constraints as well. The control action is of state feedback and satisfies the constraints. Uncertainty in the system is represented by unknown bounded disturbances and system perturbations in a linear fractional transform (LFT) representation. Mixed ℋ2/ℋ∞method is applied in a model predictive control strategy. The control law takes account of disturb

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18

Chen, Y. H., and C. Y. Kuo. "Positive Uncertain Systems With One-Sided Robust Control." Journal of Dynamic Systems, Measurement, and Control 119, no. 4 (1997): 675–84. http://dx.doi.org/10.1115/1.2802377.

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We consider a control design problem for a class of uncertain systems. The salient features of the problem are fourfold. First, the uncertainty is time-varying. Second, the system is nonlinear. Third, the state is constrained to be only positive. Fourth, the control input may be constrained to be one-sided (i.e., either positive or negative). We start with a state transformation to release the state constraint. A partial sign-definiteness knowledge of the uncertainty is then shown to be critical to meet the control constraint. Three classes of controls are constructed: one for state constraint

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19

Chen, Y. H. "Decentralized Adaptive Robust Control Design: The Uncertainty is Time Varying." Journal of Dynamic Systems, Measurement, and Control 113, no. 3 (1991): 515–18. http://dx.doi.org/10.1115/1.2896441.

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We consider a class of nonlinear uncertain interconnected systems with time-varying uncertainty. The uncertainty may arise within each system as well as in the interconnections. The uncertainty is assumed bounded but the bound is unknown. No a priori statistical information is imposed. Decentralized adaptive robust control is proposed for each system. The control has two parts. First, an adaptive scheme for the estimation of the bound is constructed. Second, a robust control, which is based on the adaptive parameter, is adopted for each system.

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20

Jiao, Yan, Kevin Reid, and Tom Nudds. "Consideration of uncertainty in the design and use of harvest control rules." Scientia Marina 74, no. 2 (2010): 371–84. http://dx.doi.org/10.3989/scimar.2010.74n2371.

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21

Leonessa, Alexander, Wassim M. Haddad, and Vijaysekhar Chellaboina. "Nonlinear robust hierarchical control for nonlinear uncertain systems." Mathematical Problems in Engineering 5, no. 6 (2000): 499–542. http://dx.doi.org/10.1155/s1024123x99001210.

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A nonlinear robust control-system design framework predicated on a hierarchical switching controller architecture parameterized over a set of moving nominal system equilibria is developed. Specifically, using equilibria-dependent Lyapunov functions, a hierarchical nonlinear robust control strategy is developed that robustly stabilizes a given nonlinear system over a prescribed range of system uncertainty by robustly stabilizing a collection of nonlinear controlled uncertain subsystems. The robust switching nonlinear controller architecture is designed based on a generalized (lower semicontinuo

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22

Le Corronc, Euriell, Bertrand Cottenceau, and Laurent Hardouin. "Control of uncertain (max,+)-linear systems in order to decrease uncertainty." IFAC Proceedings Volumes 43, no. 12 (2010): 400–405. http://dx.doi.org/10.3182/20100830-3-de-4013.00066.

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23

Roh, Young-Hoon, and Jun-Ho Oh. "Sliding mode control with uncertainty adaptation for uncertain input-delay systems." International Journal of Control 73, no. 13 (2000): 1255–60. http://dx.doi.org/10.1080/002071700417894.

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24

Liang, Song, and Zaihua Wang. "Observer‐based position control of uncertain mechanical systems with measurement uncertainty." International Journal of Robust and Nonlinear Control 29, no. 11 (2019): 3606–26. http://dx.doi.org/10.1002/rnc.4571.

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25

Doh, Tae-Yong, Jung-Ho Moon, and Myung Jin Chung. "An Iterative Learning Control for Uncertain Systems Using Structured Singular Value." Journal of Dynamic Systems, Measurement, and Control 121, no. 4 (1999): 660–67. http://dx.doi.org/10.1115/1.2802532.

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To deal with an iterative learning control (ILC) system with plant uncertainty, a set of new terms related with robust convergence is first defined. This paper proposes a sufficient condition for not only robust convergence but also robust stability of ILC for uncertain linear systems, including plant uncertainty. Thus, to find a new condition unrelated to the uncertainty, we first separate it into a known part and uncertainty one using linear fractional transformations (LFTs). Then, robust convergence and robust stability of an ILC system is determined by structured singular value (μ) of only

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26

YOU, CUILIAN. "UNCERTAINTY EXTENSION THEOREM AND PRODUCT UNCERTAIN MEASURE." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 18, no. 02 (2010): 197–208. http://dx.doi.org/10.1142/s0218488510006489.

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The additivity axiom of classical measure theory has been challenged by many mathematicians. Different replacements of the additivity correspond with different theory. In uncertainty theory, the additivity is replaced with self-duality and countable subadditivity. Similar to classical measure theory, there are some properties studied in uncertainty theory. Given the measure of each singleton set, the measure can be fully and uniquely determined in the sense of the maximum uncertainty principle. Generally speaking, a product uncertain measure may be defined in many ways, in this paper, a kind o

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27

Chen, Y. H. "Adaptive Robust Model-Following Control and Application to Robot Manipulators." Journal of Dynamic Systems, Measurement, and Control 109, no. 3 (1987): 209–15. http://dx.doi.org/10.1115/1.3143846.

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The model-following control problem for nonlinear uncertain dynamical systems is considered. Based only on the knowledge of functional properties relating to the bound of the time-varying uncertainty, a class of adaptive feedback controls is developed which, under some realistic assumptions, guarantees the error between the uncertain dynamical system and the model tends to be zero. Application to robotic manipulators is made.

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28

Egilmez, K., and S. H. Kim. "Control Under Uncertainty Through Zone Logic." Journal of Dynamic Systems, Measurement, and Control 114, no. 3 (1992): 375–89. http://dx.doi.org/10.1115/1.2897358.

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The manufacturing plant represents a complex environment, rife with uncertainty. The complexity arises from the multitude of interactions that must be considered when attempting to model most manufacturing processes. Important process variables can remain unidentified; or even if they are identified, their interactions may remain uncertain. This complexity and the uncertainties that are often its derivatives cause various inefficiencies when conventional control methods are employed. In an attempt to remedy this situation, an intelligent control methodology termed zone logic has been advanced.

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29

Benedetti, Lorenzo, Damien J. Batstone, Bernard De Baets, Ingmar Nopens, and Peter A. Vanrolleghem. "Uncertainty analysis of WWTP control strategies made feasible." Water Quality Research Journal 47, no. 1 (2012): 14–29. http://dx.doi.org/10.2166/wqrjc.2012.038.

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The control of wastewater treatment plants can help to achieve good effluent quality, in a complex, highly non-linear environment. A key but time-demanding component of such modelling studies is uncertainty analysis (UA). The general aims of this paper are (a) to evaluate methods for reduction of the time necessary to conduct an UA, and (b) to evaluate the sensitivity of parameters and model subsystems. Two UA studies on the Benchmark Simulation Model no. 2 (BSM2) are used to illustrate how the above mentioned aims can be achieved: (1) robustness of performance evaluations against changing ope

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30

Gerencsér, L., J. Rissanen, J. H. van Schuppen, and Zs Vágó. "Parametric Uncertainty and Control Performance in Stochastic Adaptive Control." IFAC Proceedings Volumes 28, no. 13 (1995): 79–82. http://dx.doi.org/10.1016/s1474-6670(17)45329-8.

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31

Deen, William, Alfons Weersink, Calum G. Turvey, and Susan Weaver. "Weed Control Decision Rules under Uncertainty." Review of Agricultural Economics 15, no. 1 (1993): 39. http://dx.doi.org/10.2307/1349710.

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32

Beneish, Messod Daniel, Mary Brooke Billings, and Leslie D. Hodder. "Internal Control Weaknesses and Information Uncertainty." Accounting Review 83, no. 3 (2008): 665–703. http://dx.doi.org/10.2308/accr.2008.83.3.665.

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We analyze a sample of 330 firms making unaudited disclosures required by Section 302 and 383 firms making audited disclosures required by Section 404 of the Sarbanes-Oxley Act. We find that Section 302 disclosures are associated with negative announcement abnormal returns of −1.8 percent, and that firms experience an abnormal increase in equity cost of capital of 68 basis points. We conclude that Section 302 disclosures are informative and point to lower credibility of disclosing firms' financial reporting. In contrast, we find that Section 404 disclosures have no noticeable impact on stock p

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33

Eisentraudt, Markus, and Sigrid Leyendecker. "Epistemic uncertainty in optimal control simulation." Mechanical Systems and Signal Processing 121 (April 2019): 876–89. http://dx.doi.org/10.1016/j.ymssp.2018.12.001.

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34

Yan, Dong Mei, and Li Liu. "Research on Elevator Group Control Uncertainty." Applied Mechanics and Materials 602-605 (August 2014): 874–77. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.874.

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Uncertainties elevator group control systems are analyzed first in this paper according to the fuzzy control theory and characteristics of the elevator group control system. Then, simulation model of elevator group control system is built using fuzzy inference system with Matlab. Operation of elevator group control system based on fuzzy control system is shown by simulation.

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35

Tøffner-Clausen, Steen. "Identification for Control: Quantification of Uncertainty." IFAC Proceedings Volumes 28, no. 16 (1995): 169–73. http://dx.doi.org/10.1016/s1474-6670(17)45173-1.

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36

Agram, Nacira, and Bernt Øksendal. "Model uncertainty stochastic mean-field control." Stochastic Analysis and Applications 37, no. 1 (2019): 36–56. http://dx.doi.org/10.1080/07362994.2018.1499036.

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37

Jun-yan, Liu. "Schedule Uncertainty Control: A Literature Review." Physics Procedia 33 (2012): 1842–48. http://dx.doi.org/10.1016/j.phpro.2012.05.293.

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38

Matyukhin, V. I. "Control of wheel system under uncertainty." Automation and Remote Control 70, no. 5 (2009): 811–28. http://dx.doi.org/10.1134/s0005117909050099.

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39

Tzes, Anthony, and Ke Le. "Adaptive weighted minimum prediction uncertainty control." International Journal of Control 68, no. 3 (1997): 657–72. http://dx.doi.org/10.1080/002071797223569.

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40

Forster, Martin, Davide La Torre, and Peter J. Lambert. "Optimal control of inequality under uncertainty." Mathematical Social Sciences 68 (March 2014): 53–59. http://dx.doi.org/10.1016/j.mathsocsci.2013.11.003.

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41

Dunn, Lewis A. "Arms Control Verification: Living with Uncertainty." International Security 14, no. 4 (1990): 165. http://dx.doi.org/10.2307/2538757.

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42

Herzallah, Randa. "UNCERTAINTY IN CONTROL PROBLEMS: A SURVEY." IFAC Proceedings Volumes 38, no. 1 (2005): 82–90. http://dx.doi.org/10.3182/20050703-6-cz-1902.01092.

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43

Grigorenko, N. L. "A control problem with dominating uncertainty." Proceedings of the Steklov Institute of Mathematics 287, S1 (2014): 68–76. http://dx.doi.org/10.1134/s0081543814090077.

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44

da Silva de Aguiar, Raquel Stella, Pierre Apkarian, and Dominikus Noll. "Structured Robust Control Against Mixed Uncertainty." IEEE Transactions on Control Systems Technology 26, no. 5 (2018): 1771–81. http://dx.doi.org/10.1109/tcst.2017.2723864.

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45

Bielecki, Tomasz R., Tao Chen, Igor Cialenco, Areski Cousin, and Monique Jeanblanc. "Adaptive Robust Control under Model Uncertainty." SIAM Journal on Control and Optimization 57, no. 2 (2019): 925–46. http://dx.doi.org/10.1137/17m1137917.

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46

van Beers, Robert J., Pierre Baraduc, and Daniel M. Wolpert. "Role of uncertainty in sensorimotor control." Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 357, no. 1424 (2002): 1137–45. http://dx.doi.org/10.1098/rstb.2002.1101.

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Neural signals are corrupted by noise and this places limits on information processing. We review the processes involved in goal–directed movements and how neural noise and uncertainty determine aspects of our behaviour. First, noise in sensory signals limits perception. We show that, when localizing our hand, the central nervous system (CNS) integrates visual and proprioceptive information, each with different noise properties, in a way that minimizes the uncertainty in the overall estimate. Second, noise in motor commands leads to inaccurate movements. We review an optimal–control framework,

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47

Cheong, Taesu, and Chelsea C. White. "Inventory replenishment control under supply uncertainty." Annals of Operations Research 208, no. 1 (2011): 581–92. http://dx.doi.org/10.1007/s10479-011-0929-9.

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48

Il’in, A. V., V. V. Fomichev, and A. S. Fursov. "Control Problems for Systems with Uncertainty." Computational Mathematics and Modeling 30, no. 4 (2019): 390–402. http://dx.doi.org/10.1007/s10598-019-09465-8.

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49

Jordan, Thomas J. "Monetary control uncertainty and inflation bias." Journal of Economics 73, no. 2 (2001): 125–47. http://dx.doi.org/10.1007/bf02340172.

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50

Orbán, Gergő, and Daniel M. Wolpert. "Representations of uncertainty in sensorimotor control." Current Opinion in Neurobiology 21, no. 4 (2011): 629–35. http://dx.doi.org/10.1016/j.conb.2011.05.026.

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