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We research the finite-time parameter identification of fractional-order time-varying delay neural networks (FTVDNNs) based on synchronization. First, based on the fractional-order Lyapunov stability theorem and feedback control idea, we construct a synchronous controller and some parameter update rules, which accomplish the synchronization of the drive-response FTVDNNs and complete the identification of uncertain parameters. Second, the theoretical analysis of the synchronization method is carried out, and the stable time is calculated. Finally, we give two examples for simulation verification. Our method can complete the synchronization of the FTVDNNs in finite time and identify uncertain parameters while synchronizing.
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Chen, Y. H. "Decentralized Adaptive Robust Control Design: The Uncertainty is Time Varying". Journal of Dynamic Systems, Measurement, and Control 113, n. 3 (1 settembre 1991): 515–18. http://dx.doi.org/10.1115/1.2896441.
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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HUANG, HE, HAN-XIONG LI e JUE ZHONG. "MASTER-SLAVE SYNCHRONIZATION OF GENERAL LUR'E SYSTEMS WITH TIME-VARYING DELAY AND PARAMETER UNCERTAINTY". International Journal of Bifurcation and Chaos 16, n. 02 (febbraio 2006): 281–94. http://dx.doi.org/10.1142/s0218127406014800.
This paper deals with the problem of master-slave synchronization for uncertain Lur'e systems via time-varying delay feedback control. The parametric uncertainty is assumed to be norm bounded. Several new and sufficient conditions are presented such that the uncertain Lur'e master and slave systems are synchronous for all admissible uncertainties. These synchronization criteria are dependent on the size of time delay, which can be expressed by means of matrix inequalities. The adopted method is based on defining a new Lyapunov–Krasovskii function and using some inequalities techniques. Our results obtained here extend and improve some previously related results. Finally, two numerical examples are provided to demonstrate the applications of our proposed results.
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Mukdasai, Kanit. "Robust Exponential Stability for LPD Discrete-Time System with Interval Time-Varying Delay". Journal of Applied Mathematics 2012 (2012): 1–13. http://dx.doi.org/10.1155/2012/237430.
This paper investigates the problem of robust exponential stability for uncertain linear-parameter dependent (LPD) discrete-time system with delay. The delay is of an interval type, which means that both lower and upper bounds for the time-varying delay are available. The uncertainty under consideration is norm-bounded uncertainty. Based on combination of the linear matrix inequality (LMI) technique and the use of suitable Lyapunov-Krasovskii functional, new sufficient conditions for the robust exponential stability are obtained in terms of LMI. Numerical examples are given to demonstrate the effectiveness and less conservativeness of the proposed methods.
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Iqbal, Muhammad Naveed, Jian Xiao e Weiming Xiang. "Parameter-dependent finite-time observer design for time-varying polytopic uncertain switched systems". Journal of the Franklin Institute 351, n. 3 (marzo 2014): 1657–72. http://dx.doi.org/10.1016/j.jfranklin.2013.12.007.
Hu, Jingting, Guixia Sui, Shengli Du e Xiaodi Li. "Finite-Time Stability of Uncertain Nonlinear Systems with Time-Varying Delay". Mathematical Problems in Engineering 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/2538904.
The problem of finite-time stability for a class of uncertain nonlinear systems with time-varying delay and external disturbances is investigated. By using the Lyapunov stability theory, sufficient conditions for the existence of finite-time state feedback controller for this class of systems are derived. The results can be applied to finite-time stability problems of linear time-delay systems with parameter uncertainties and external disturbances. Finally, two numerical examples are given to demonstrate the effectiveness of the obtained theoretical results.
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Shao, Xueying, Qing Lu, Hamid Reza Karimi e Jin Zhu. "New Results on Passivity Analysis for Uncertain Neural Networks with Time-Varying Delay". Abstract and Applied Analysis 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/303575.
The paper investigates the stability and passivity analysis problems for a class of uncertain neural networks with time-delay via delta operator approach. Both the parameter uncertainty and the generalized activation functions are considered in this paper. By constructing an appropriate Lyapunov-Krasovskii functional, some new stability and passivity conditions are obtained in terms of linear matrix inequalities (LMIs). The main characteristic of this paper is to obtain novel stability and passivity analysis criteria for uncertain neural networks with time-delay in the delta operator system framework. A numerical example is presented to demonstrate the effectiveness of the proposed results.
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Chen, Fu, Shugui Kang e Fangyuan Li. "Stability and Stabilization for Polytopic LPV Systems with Parameter-Varying Time Delays". Mathematical Problems in Engineering 2019 (5 maggio 2019): 1–12. http://dx.doi.org/10.1155/2019/4924963.
In this paper, we deal with the problem of stability and stabilization for linear parameter-varying (LPV) systems with time-varying time delays. The uncertain parameters are assumed to reside in a polytope with bounded variation rates. Being main difference from the existing achievements, the representation of the time derivative of the time-varying parameter is under a polytopic structure. Based on the new representation, delay-dependent sufficient conditions of stability and stabilization are, respectively, formulated in terms of linear matrix inequalities (LMI). Simulation examples are then provided to confirm the effectiveness of the given approach.
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Lou, Xuyang, Qian Ye e Baotong Cui. "Parameter-dependent robust stability of uncertain neural networks with time-varying delay". Journal of the Franklin Institute 349, n. 5 (giugno 2012): 1891–903. http://dx.doi.org/10.1016/j.jfranklin.2012.02.015.
This paper investigates the output feedback stabilization problem of linear time-varying uncertain delay systems with limited measurable state variables. Each uncertain parameter and each delay under consideration may take arbitrarily large values. In such a situation, the locations of uncertain entries in the system matrices play an important role. It has been shown that if a system has a particular configuration called a triangular configuration, then the system is stabilizable irrespective of the given bounds of uncertain variations. In the results so far obtained, the stabilization problem has been reduced to finding the proper variable transformation such that anM-matrix stability criterion is satisfied. However, it still has not been shown whether the constructed variable transformation enables the system to satisfy theM-matrix stability condition. The objective of this paper is to show a method that enables verification of whether the transformed system satisfies theM-matrix stability condition.
Hego, Axelle. "Analyse de sensibilité sur un modèle hydrologique de toiture végétalisée". Electronic Thesis or Diss., Université de Lorraine, 2022. http://www.theses.fr/2022LORR0161.
Ces dernières décennies, les surfaces imperméables des zones urbaines favorisent la saturation des systèmes d'évacuation. Dans le but de réduire l'impact de cette imperméabilité, l'installation de toitures végétalisées est une solution intéressante pour retenir et ralentir les eaux de pluie avant leur arrivée dans les réseaux. Une étude approfondie du comportement hydrique de ces structures est nécessaire pour la mise en place de toitures végétalisées performantes dans les éco-quartiers par exemple. Les performances hydriques sont caractérisées par la teneur en eau à l'intérieur des différentes couches de la toiture. Cette teneur en eau peut être simulée avec des modèles complexes qui dépendent de plusieurs paramètres difficiles à déterminer avec précision tels que des paramètres de sol, de végétation et de météorologie. Les incertitudes de ces paramètres vont se propager dans le modèle et affecter le calcul de la teneur en eau. Pour analyser et quantifier l'impact des incertitudes des paramètres sur la sortie du modèle, la teneur en eau, des méthodes d'analyse de sensibilité globale sont appliquées. Dans un premier temps, un modèle simplifié est considéré. Sa particularité est sa sortie variant au cours du temps. L'analyse de sensibilité est menée en utilisant une approche séquentielle mais aussi multivariée. Ensuite, une calibration du modèle complet est réalisée en utilisant l'analyse de sensibilité. Enfin, un paramètre météo incertain et variant dans le temps est ajouté à l'étude. Une approche originale est proposée pour générer des échantillons cohérents et corrélés dans le temps pour ce paramètre. Les résultats de ces travaux de thèse ont mis en évidence les paramètres ayant le plus d'impact sur le teneur en eau au cours du temps parmi les paramètres de sols, de végétation et de météo. Ces résultats ont permis d'améliorer la compréhension de la dynamique de la toiture végétalisée In the last decades, soil imperviousness in urban areas have been one of the main issue because it can lead to the saturation of drainage systems and flood. In order to reduce the impact of this imperviousness, the installation of green roofs is an interesting solution to retain and slow down rainwater before it reaches the sewer system. An in-depth study of the hydric behavior of these structures is necessary for the installation of efficient green roofs in eco-districts for example. The hydric performances are characterized by the water content inside the different layers of the roof. This water content can be simulated with complex models depending on several parameters difficult to determine with precision such as soil, vegetation and meteorological parameters. The uncertainties of these parameters will propagate in the model and affect the simulation of the water content. To analyse and quantify the impact of parameter uncertainties on the model output, the water content, global sensitivity analysis methods are applied. First, a simplified model is considered. Its particularity is its time-varying output. The sensitivity analysis is conducted using a sequential but also multivariate approach.Then, a calibration of the complete model is performed using the sensitivity analysis. Finally, an uncertain and time-varying weather parameter is added to the study. An original approach is proposed to generate consistent and time-correlated samples for this parameter. The results of this thesis work have highlighted the parameters with the highest impact on water content over time among the soil, vegetation and weather parameters. These results have improved the understanding of the green roof dynamics
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Doehr, Rachel M. "Adventures at the Zero Lower Bound: A Bayesian Time-Varying Parameter Vector Autoregressive Analysis of Monetary Policy Uncertainty Shocks". Scholarship @ Claremont, 2016. http://scholarship.claremont.edu/cmc_theses/1318.
Using survey-based measures of future interest rate expectations from the Blue Chip Economic Indicators and the Survey of Professional Forecasters, we examine the relationship between monetary policy uncertainty, captured as the dispersion of interest rate forecasts, and fluctuations in real economic activity and core inflation. We use a flexible time-varying parameter vector autoregression (TVP-VAR) model to clearly isolate the dynamic effects of shocks to monetary policy uncertainty. To further study possible a possible nonlinear relationship between monetary policy uncertainty and the macroeconomic aggregates, we extract the impulse-response functions (IRF’s) estimated at each quarter in the time series, and use a multi-variate regression with various measures of the shape of the IRF’s and the level of monetary policy uncertainty at that quarter in the TVP-VAR model to gauge the relationship between the effectiveness of traditional monetary policy (shocks to the Federal Funds rate), forward guidance (shocks to expected interest rates) and uncertainty. The results show that monetary policy uncertainty can have a quantitatively significant impact on output, with a one standard deviation shock to uncertainty associated with a 0.6% rise in unemployment. The indirect effects are more substantial, with a one standard deviation increase in monetary policy uncertainty associated with a 23% decrease in the maximum response of unemployment to a forward guidance episode (interest rate expectations shock). This evidence points to the importance of managing monetary policy uncertainty (clear and direct forward guidance) as a key policy tool in both stimulating economic activity as well as propagating other monetary policy through the macroeconomy.
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Juutilainen, I. (Ilmari). "Modelling of conditional variance and uncertainty using industrial process data". Doctoral thesis, University of Oulu, 2006. http://urn.fi/urn:isbn:9514282620.
Abstract
This thesis presents methods for modelling conditional variance and uncertainty of prediction at a query point on the basis of industrial process data. The introductory part of the thesis provides an extensive background of the examined methods and a summary of the results. The results are presented in detail in the original papers.
The application presented in the thesis is modelling of the mean and variance of the mechanical properties of steel plates. Both the mean and variance of the mechanical properties depend on many process variables. A method for predicting the probability of rejection in a quali?cation test is presented and implemented in a tool developed for the planning of strength margins. The developed tool has been successfully utilised in the planning of mechanical properties in a steel plate mill.
The methods for modelling the dependence of conditional variance on input variables are reviewed and their suitability for large industrial data sets are examined. In a comparative study, neural network modelling of the mean and dispersion narrowly performed the best.
A method is presented for evaluating the uncertainty of regression-type prediction at a query point on the basis of predicted conditional variance, model variance and the effect of uncertainty about explanatory variables at early process stages. A method for measuring the uncertainty of prediction on the basis of the density of the data around the query point is proposed. The proposed distance measure is utilised in comparing the generalisation ability of models. The generalisation properties of the most important regression learning methods are studied and the results indicate that local methods and quadratic regression have a poor interpolation capability compared with multi-layer perceptron and Gaussian kernel support vector regression.
The possibility of adaptively modelling a time-varying conditional variance function is disclosed. Two methods for adaptive modelling of the variance function are proposed. The background of the developed adaptive variance modelling methods is presented.
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Ganguly, Shreyan. "Modeling Nonstationarity Using Locally Stationary Basis Processes". The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1563408374215259.
碩士 大同大學 電機工程學系(所) 94 In this thesis, we propose an indirect adaptive fuzzy control scheme combined
with state estimation for continuous-time multiple-input-multiple-output (MIMO)
nonlinear systems with time-varying parametric uncertainties where the state information is not available. The proposed observer-based indirect adaptive fuzzy control employs a fuzzy system to approximate the time-varying parametric uncertainties, which are periodic and the only prior knowledge is the periodicity, to obtain fast parameter adaptation and fast convergence of the tracking error. Based on the Lyapunov stability theorem, the proposed adaptive fuzzy control system can guarantee the stability of the whole closed-loop system and obtain good tracking performance as well. Finally, the proposed method is applied to a 2-dof parallel robot control problems. The simulation can demonstrate the validity of the proposed scheme and achieve satisfy simulation results.
Libri sul tema "Uncertain time-Varying parameter":
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Chang-Jin, Kim. Sources of monetary growth uncertainty and economic activity: The time-varying-parameter model with heteroskedasticity in the disturbance terms. [Toronto, Ont: York University, Dept. of Economics, 1990.
Robust Control of Linear Systems Subject to Uncertain Time-Varying Parameters. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/3-540-33276-6.
Capitoli di libri sul tema "Uncertain time-Varying parameter":
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Fu, Minyue, e Huaizhong Li. "Model Following Robust Control of Linear Time-Varying Uncertain Systems". In Robustness of Dynamic Systems with Parameter Uncertainties, 261–70. Basel: Birkhäuser Basel, 1992. http://dx.doi.org/10.1007/978-3-0348-7268-3_26.
Schaeffner, Maximilian, e Roland Platz. "Linear Parameter-Varying (LPV) Buckling Control of an Imperfect Beam-Column Subject to Time-Varying Axial Loads". In Model Validation and Uncertainty Quantification, Volume 3, 103–12. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54858-6_11.
Yoo, Seog-Hwan, e Byung-Jae Choi. "A Balanced Model Reduction for T-S Fuzzy Systems with Uncertain Time Varying Parameters". In Computational and Information Science, 148–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30497-5_24.
Fu, Minyue, Lihua Xie e Carlos E. de Souza. "H ∞ Control for Linear Systems with Time-Varying Parameter Uncertainty *". In CONTROL of UNCERTAIN DYNAMIC SYSTEMS, 63–75. CRC Press, 2020. http://dx.doi.org/10.1201/9781003067702-6.
Cerone, V., D. Piga e D. Regruto. "Set-membership identification of input-output LPV models with uncertain time-varying parameters". In Linear Parameter-Varying System Identification, 41–64. WORLD SCIENTIFIC, 2011. http://dx.doi.org/10.1142/9789814355452_0003.
Rogers, Alexander W., Ziqi Song, Fernando Vega Ramon, Keju Jing e Dongda Zhang. "Hybrid Modelling Under Uncertainty: Effects of Model Greyness, Data Quality and Data Quantity". In Machine Learning and Hybrid Modelling for Reaction Engineering, 208–28. Royal Society of Chemistry, 2023. http://dx.doi.org/10.1039/bk9781837670178-00208.
Previous chapters have demonstrated how, by combining data-driven and mechanistic methods, hybrid modelling provides a cost-effective solution to modelling complex (bio)chemical reaction kinetics when the underlying mechanisms are not fully understood. However, the question of how much kinetic information to incorporate into a hybrid model (i.e., the ‘greyness’ of a hybrid model) remains to be systematically addressed. Therefore, to illustrate the effect of model greyness on accuracy and reliability, in this chapter, we built three hybrid models for predicting the kinetics of a complex biochemical system: γ-linolenic acid production via fermentation of the fungus Cunninghamella echinulata. Each hybrid model incorporated different amounts of kinetic information, representing three levels of hybrid model ‘greyness’, then embedded a Gaussian process (GP) to simulate the unknown kinetics inferred from experimental observation. Hybrid model parameter estimation is also revisited to introduce time-varying parameter regularisation to mitigate the risk of overfitting real process data with missing or uncertain measurements. Although all three hybrid models could fit well, we demonstrate how incorporating more specific kinetic information increases the risk of incorrect inductive bias, while too little renders the hybrid model prone to overfitting. By balancing the regularisation penalty weight and the amount of kinetic information provided, it is possible to build a high-fidelity hybrid model for predicting reaction system performance for new operating conditions and reactor scales. This extends the hybrid modelling theory presented in earlier chapters to meet the challenges encountered in practice.
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Mansouri, Majdi, Benjamin Dumont e Marie-France Destain. "Modeling and Prediction of Time-Varying Environmental Data Using Advanced Bayesian Methods". In Exploring Innovative and Successful Applications of Soft Computing, 112–37. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4785-5.ch007.
The problem of state/parameter estimation represents a key issue in crop models, which are nonlinear, non-Gaussian, and include a large number of parameters. The prediction errors are often important due to uncertainties in the equations, the input variables, and the parameters. The measurements needed to run the model and to perform calibration and validation are sometimes not numerous or known with some uncertainty. In these cases, estimating the state variables and/or parameters from easily obtained measurements can be extremely useful. In this chapter, the authors address the problem of modeling and prediction of time-varying Leaf area index and Soil Moisture (LSM) to better handle nonlinear and non-Gaussian processes without a priori state information. The performances of various conventional and state-of-the-art estimation techniques are compared when they are utilized to achieve this objective. These techniques include the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), Particle Filter (PF), and the more recently developed technique Variational Bayesian Filter (VF). The original data was issued from experiments carried out on silty soil in Belgium with a wheat crop during two consecutive years, the seasons 2008-09 and 2009-10.
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Mowbray, Max, Ehecatl Antonio Del Rio Chanona e Dongda Zhang. "Constructing Time-varying and History-dependent Kinetic Models Via Reinforcement Learning". In Machine Learning and Hybrid Modelling for Reaction Engineering, 247–73. Royal Society of Chemistry, 2023. http://dx.doi.org/10.1039/bk9781837670178-00247.
In Chapter 8, we demonstrated how to identify an accurate lumped kinetic model structure through reaction network reduction. However, this problem can become challenging if the kinetic parameters are time-varying due to continuous changes of catalyst and enzyme reactivity. Using machine learning methods, in Chapters 3 and 9, we have demonstrated that hybrid modelling provides an effective solution to account for the time-varying nature of kinetic parameters, reducing the model uncertainty. However, another longstanding challenge for predictive modelling of complex chemical and biochemical reactions is their history-dependent behaviour. For example, bistable reactions have ‘memory’ and their trajectories are dependent on past process conditions. Simultaneously resolving history-dependent kinetic model structure identification and time-varying parameter estimation has rarely been studied due to the complexity of the underlying mechanisms and lack of efficient mathematical optimisation algorithms. Therefore, in this chapter, we adopt reinforcement learning (RL) to resolve this challenge by integrating it with hybrid modelling. This chapter introduces a novel three-step modelling framework: (i) speculate and combine possible kinetic model structures sourced from process and phenomenological knowledge, (ii) identify the most likely kinetic model structure and its parameter values using RL and (iii) validate the hybrid model identified. To demonstrate the applicability of the framework, in silico experiments explore three different biochemical scenarios. The results show that the proposed framework efficiently constructs hybrid models to quantify both time-varying and history-dependent kinetic behaviours while minimising the risks of over-parametrisation and over-fitting, highlighting the potential of this framework for general chemical and biochemical reaction modelling.
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Yayla, Metehan, e Ali Turker Kutay. "Information Recovery in Composite Model Reference Adaptive Control". In Latest Adaptive Control Systems [Working Title]. IntechOpen, 2024. http://dx.doi.org/10.5772/intechopen.1005440.
This study introduces a new adaptive control approach incorporating frequency-limited estimation of matched uncertainty. While many existing adaptive parameter adjustment laws aim to mitigate uncertainty effects solely through tracking error, it is well-documented that integrating uncertainty estimation error into the adaptation process significantly improves transient performance. Our method incorporates low-frequency uncertainty estimation with a time-varying learning rate structure. Unlike conventional filter-based approaches, our approach also compensates for information loss during signal filtering to suppress high-frequency content. Additionally, we include a regulation term in the standard adaptive weight update law, acting as stability enhancement in the adaptive system. We demonstrate the closed-loop stability of the proposed method using Lyapunov’s stability theorem and highlight its efficacy through numerical examples and software-in-the-loop simulations with the X-plane flight simulator.
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Shahriari, Kyarash. "Identification and Response Prediction of Switching Dynamic Systems Using Interval Analysis". In Cybernetics and Systems Theory in Management, 274–302. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-61520-668-1.ch015.
A novel method based on interval analysis is proposed in this work for modeling and response prediction of SISO uncertain switching dynamic systems. To describe the system’s dynamic in any operating mode, a local linear model is used. The validity domain of any local model is determined in system’s input-output space. To take into account the modeling error, adjustable parameters of local models are considered time-varying and characterized by intervals of real numbers. A model whose parameters are characterized by intervals is called an interval model. A procedure is also developed to perform nstep prediction of system’s response using the multi-mode interval model. Since the model parameters are intervals, the predicted response at any instant is not a real number anymore but an interval of real numbers. The set of predicted intervals at different instances generates a tube through time called wrapping envelope. However, the identification/characterization procedure proposed in the early stage of this work guarantees that the wrapping envelope includes the system’s response taking into account possible modeling error and perturbations. This envelope can be used in diagnosis to supervise healthy operation of the system as well as in process safety analysis to guarantee that the physical variables of the system never enter in forbidden operating zones and the system remains in safe operating conditions.
Atti di convegni sul tema "Uncertain time-Varying parameter":
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Xing, Hailong, WenJie Zhao e D. H. Li. "Variable Structure Control for Parameter Uncertain Stochastic Systems with Time-Varying Delay". In 2008 IEEE Conference on Robotics, Automation and Mechatronics (RAM). IEEE, 2008. http://dx.doi.org/10.1109/ramech.2008.4681341.
Mizouri, Hanin, Rihab Lamouchi e Messaoud Amairi. "Functional Interval Estimation for Uncertain Continuous-time Linear Parameter-Varying Multivariable Systems". In 2023 IEEE International Workshop on Mechatronic Systems Supervision (IW_MSS). IEEE, 2023. http://dx.doi.org/10.1109/iw_mss59200.2023.10369308.
Southward, Steve C. "Real-Time Parameter ID Using Polynomial Chaos Expansions". In ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-43745.
A novel real-time parameter identification algorithm has been developed that exploits polynomial chaos expansion (PCE) representations of uncertain parameters. Dynamic system models inevitably contain parameters whose values are rarely known with absolute certainty. In many cases, such parameters are either not measurable, or they are slowly time varying. In some cases, the dynamic system model is inadequate and parameter values are simply chosen to provide a “best fit” representation. For the method proposed here, we assume apriori knowledge of the probability distributions associated with the uncertain parameters. Within the PCE framework, the uncertain parameter distribution is explicitly propagated through the dynamic system equations using a Galerkin projection onto an orthogonal polynomial basis. The probabilistic PCE model is then collapsed to a deterministic model where an adaptive algorithm is designed to effectively reduce the uncertainty. For illustration, this algorithm is numerically demonstrated using a simple first order dynamic system with only a single uncertain parameter.
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Cerone, V., e D. Regruto. "Set-membership identification of LPV models with uncertain measurements of the time-varying parameter". In 2008 47th IEEE Conference on Decision and Control. IEEE, 2008. http://dx.doi.org/10.1109/cdc.2008.4739267.
Li, Shanbin, Dominique Sauter e Bugong Xu. "Uniformly ultimate boundedness event-triggered control for discrete-time uncertain linear parameter-varying systems". In 2014 33rd Chinese Control Conference (CCC). IEEE, 2014. http://dx.doi.org/10.1109/chicc.2014.6895920.
Jun Li, Gang Wu e Zhiquan Wang. "Robust H∞Fault Tolerant Control Design for Linear Uncertain Systems with Time-varying Norm-bounded Parameter Uncertainty". In 2006 6th World Congress on Intelligent Control and Automation. IEEE, 2006. http://dx.doi.org/10.1109/wcica.2006.1712802.
Zhang, Zhen, Zongxuan Sun e Peiqing Ye. "An Internal Model-Based Tracking Control for a Class of Uncertain Linear Time-Varying Systems". In ASME 2010 Dynamic Systems and Control Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/dscc2010-4271.
In this paper, we extend previous results for a novel internal model-based tracking control with a class of known LTV plant models driven by LTI exosystems to uncertain LTI plant models driven by LTV exosystems. The augmented time-varying system to be stabilized becomes uncertain. Moreover, the time-varying fashion under consideration renders the augmented uncertain system linear parameter-varying (LPV). By means of an output-feedback gain-scheduling design, the augmented uncertain LTV system is stabilized. Simulation results illustrate the proposed design method.
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Jong Hae Kim, Eun Tae Jeung e Hong Bae Park. "Robust H/sup /spl infin// control for parameter uncertain system with time-varying delayed states: output feedback case". In Proceedings of 16th American CONTROL Conference. IEEE, 1997. http://dx.doi.org/10.1109/acc.1997.609513.
Lee, Lawton H., e Kameshwar Poolla. "Identifiability Issues for Parameter-Varying and Multidimensional Linear Systems". In ASME 1997 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/detc97/vib-4240.
Abstract This paper considers the identifiability of state space models for a system that is expressed as a linear fractional transformation (LFT): a constant matrix (containing identified parameters) in feedback with a finite-dimensional, block-diagonal (“structured”) linear operator. This model structure can represent linear time-invariant, linear parameter-varying, uncertain, and multidimensional systems. Families of input-output equivalent realizations are characterized as manifolds in the parameter space whose tangent spaces — and orthogonal complements — can be obtained via singular value decomposition. As illustrated by a numerical example, restricting iterative parameter estimation algorithms (e.g., maximum-likelihood with nonlinear programming) to the orthogonal directions offers significant computational advantages.
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Hyun, Dong Jin, Jungsu Choi e Kyoungchul Kong. "A High Performance Tracking Control Method Based on a Disturbance Observer With Parameter Adaptation". In ASME 2013 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/dscc2013-3814.
A disturbance observer (DOB) is a useful control algorithm for systems with uncertain dynamics, such as nonlinearity and time-varying dynamics. The DOB, however, is designed based on a nominal model, and its stability is sensitive to the magnitude of discrepancy between a controlled system and its nominal model. Therefore, to increase the stability margin of the DOB, it requires an accurate model identification, which is often difficult for nonlinear or uncertain systems. In this paper, the parameters of the nominal model are continuously updated by a parameter adaptation algorithm (PAA) to keep the model discrepancy small, such that the DOB is able to show its desired performance without losing stability robustness even in the presence of nonlinearity and/or time-varying dynamics. In the integration of the DOB and the PAA, however, there exists a complicated signal interaction. In this paper, such interaction problem is solved from a practical point of view; signal filtering. The proposed method shows improved performance for an electric motor system, and is verified by experimental results in this paper.
Rapporti di organizzazioni sul tema "Uncertain time-Varying parameter":
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Hauzenberger, Niko, Florian Huber, Gary Koop e James Mitchell. Bayesian modeling of time-varying parameters using regression trees. Federal Reserve Bank of Cleveland, gennaio 2023. http://dx.doi.org/10.26509/frbc-wp-202305.
In light of widespread evidence of parameter instability in macroeconomic models, many time-varying parameter (TVP) models have been proposed. This paper proposes a nonparametric TVP-VAR model using Bayesian additive regression trees (BART). The novelty of this model stems from the fact that the law of motion driving the parameters is treated nonparametrically. This leads to great flexibility in the nature and extent of parameter change, both in the conditional mean and in the conditional variance. In contrast to other nonparametric and machine learning methods that are black box, inference using our model is straightforward because, in treating the parameters rather than the variables nonparametrically, the model remains conditionally linear in the mean. Parsimony is achieved through adopting nonparametric factor structures and use of shrinkage priors. In an application to US macroeconomic data, we illustrate the use of our model in tracking both the evolving nature of the Phillips curve and how the effects of business cycle shocks on inflationary measures vary nonlinearly with movements in uncertainty.
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Liu e Nixon. L52305 Probabilistic Analysis of Pipeline Uplift Resistance. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), giugno 2010. http://dx.doi.org/10.55274/r0000002.
To investigate the feasibility of probabilistic analyses of the peak uplift resistance in frozen soils by varying parameters that are known to be important for the development of the uplift resistance under the upward movement of a pipe. A buried pipeline will be subjected to a variety of forces, both internal and external, including the interaction of the pipe with the surrounding soil. The soil-pipe interaction in permafrost regions have to account for the behavior of frozen and unfrozen soil, and transitions between the two as the pipeline traverses in a discontinuous permafrost zone. The variations in the properties and behavior of frozen soils are expected to be substantial in three dimensions of the Right-of-Way (ROW) and with time (seasonal fluctuations and changes with the history of pipeline operation). Given the uncertainties with frozen soil properties and the changes in behavior with time and location, a large variation in soil-pipe interaction characteristics can exist. The uplift resistance of a pipeline is one of these soil-pipe interactions that can be impacted by a variation in soil condition and state. A need was identified to outline the use of a probabilistic analysis of pipe uplift resistance in an attempt to capture the magnitude of these variations and uncertainties of frozen soil and the impact on the soil-pipe interaction. The probabilistic analysis allows the designer of a pipeline to consider a range of uplift resistance to a certain confidence level that would represent the likely values that a pipe may be subjected to. The work presented in this report is more focused on the methodology of the probabilistic approach, rather than the analysis itself for a specific design case, even though an example is provided for illustration purposes. A series of numerical simulations using Fast Lagrangian Analysis of Continua (FLAC) were completed varying one parameter with each run to develop a library of peak uplift resistances for a variety of different temperatures, soil properties and pipe parameters. The FLAC model was previously developed for PRC, a summary of this report is provided here to outline important parameters that were used to complete this analysis. The simulations were used to develop a correlation of peak uplift resistance as a function of soil tensile strain limit, modulus of deformation, and creep of frozen soils. Each of these parameters is dependent of the pipeline conditions such as temperature, displacement rate, and effective frozen cover depth. It is noted that the scope of the work was to develop a probabilistic method of estimating peak uplift resistance in frozen soils. Even though some sensitivity analysis were carried out, as outlined later in this report, to assess the impacts of the variable, detailed uncertainty analysis or risk assessment were not performed.
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RESIDUAL LIFE PREDICTION AND DESIGN CORRECTION METHOD OF CORRODED CIRCULAR STEEL TUBES BASED ON TIME-VARYING RELIABILITY. The Hong Kong Institute of Steel Construction, marzo 2024. http://dx.doi.org/10.18057/ijasc.2024.20.1.2.
Current research on the effects of corrosion on the safety of steel members is primarily focused on the degradation of the ultimate bearing capacity, and there is a lack of research on the reliability-based service life assessment. In this paper, a modified reliability function and an uncertainty model for each parameter considering the effects of corrosion are established based on the reliability analysis method of the GB50068-2018 design specification. The effects of the corrosive environment category, wall thickness, and slenderness ratio on the time-varying reliability of axially compressed round tubes are analyzed. The results indicate that an increase in the corrosion duration and the environmental category can cause a decrease in the reliability index of the component, and the smaller the wall thickness, the faster the corrosion-related degradation of the reliability index. However, with the increase of the slenderness ratio, the corrosion-related degradation of the reliability index gradually decreases. In the end, a residual life prediction method and a design correction method for corroded components based on the reliability index are proposed. Moreover, the values of the corrosion life and corrosion coefficient of circular steel tubes for different environmental categories, slenderness ratios, and thicknesses are determined. This paper provides a technical reference for the residual life prediction and full life design of round steel tubes considering local corrosion.
