Probabilistic Dynamics for Integrated Analysis of Accident Sequences considering Uncertain Events

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Robertas Alzbutas

2015-01-01

Full Text Available The analytical/deterministic modelling and simulation/probabilistic methods are used separately as a rule in order to analyse the physical processes and random or uncertain events. However, in the currently used probabilistic safety assessment this is an issue. The lack of treatment of dynamic interactions between the physical processes on one hand and random events on the other hand causes the limited assessment. In general, there are a lot of mathematical modelling theories, which can be used separately or integrated in order to extend possibilities of modelling and analysis. The Theory of Probabilistic Dynamics (TPD and its augmented version based on the concept of stimulus and delay are introduced for the dynamic reliability modelling and the simulation of accidents in hybrid (continuous-discrete systems considering uncertain events. An approach of non-Markovian simulation and uncertainty analysis is discussed in order to adapt the Stimulus-Driven TPD for practical applications. The developed approach and related methods are used as a basis for a test case simulation in view of various methods applications for severe accident scenario simulation and uncertainty analysis. For this and for wider analysis of accident sequences the initial test case specification is then extended and discussed. Finally, it is concluded that enhancing the modelling of stimulated dynamics with uncertainty and sensitivity analysis allows the detailed simulation of complex system characteristics and representation of their uncertainty. The developed approach of accident modelling and analysis can be efficiently used to estimate the reliability of hybrid systems and at the same time to analyze and possibly decrease the uncertainty of this estimate.

Symplectic Synchronization of Lorenz-Stenflo System with Uncertain Chaotic Parameters via Adaptive Control

Directory of Open Access Journals (Sweden)

Cheng-Hsiung Yang

2013-01-01

Full Text Available A new symplectic chaos synchronization of chaotic systems with uncertain chaotic parameters is studied. The traditional chaos synchronizations are special cases of the symplectic chaos synchronization. A sufficient condition is given for the asymptotical stability of the null solution of error dynamics and a parameter difference. The symplectic chaos synchronization with uncertain chaotic parameters may be applied to the design of secure communication systems. Finally, numerical results are studied for symplectic chaos synchronized from two identical Lorenz-Stenflo systems in three different cases.

A modified hybrid uncertain analysis method for dynamic response field of the LSOAAC with random and interval parameters

Science.gov (United States)

Zi, Bin; Zhou, Bin

2016-07-01

For the prediction of dynamic response field of the luffing system of an automobile crane (LSOAAC) with random and interval parameters, a hybrid uncertain model is introduced. In the hybrid uncertain model, the parameters with certain probability distribution are modeled as random variables, whereas, the parameters with lower and upper bounds are modeled as interval variables instead of given precise values. Based on the hybrid uncertain model, the hybrid uncertain dynamic response equilibrium equation, in which different random and interval parameters are simultaneously included in input and output terms, is constructed. Then a modified hybrid uncertain analysis method (MHUAM) is proposed. In the MHUAM, based on random interval perturbation method, the first-order Taylor series expansion and the first-order Neumann series, the dynamic response expression of the LSOAAC is developed. Moreover, the mathematical characteristics of extrema of bounds of dynamic response are determined by random interval moment method and monotonic analysis technique. Compared with the hybrid Monte Carlo method (HMCM) and interval perturbation method (IPM), numerical results show the feasibility and efficiency of the MHUAM for solving the hybrid LSOAAC problems. The effects of different uncertain models and parameters on the LSOAAC response field are also investigated deeply, and numerical results indicate that the impact made by the randomness in the thrust of the luffing cylinder F is larger than that made by the gravity of the weight in suspension Q . In addition, the impact made by the uncertainty in the displacement between the lower end of the lifting arm and the luffing cylinder a is larger than that made by the length of the lifting arm L .

Secure estimation, control and optimization of uncertain cyber-physical systems with applications to power networks

Science.gov (United States)

Taha, Ahmad Fayez

Transportation networks, wearable devices, energy systems, and the book you are reading now are all ubiquitous cyber-physical systems (CPS). These inherently uncertain systems combine physical phenomena with communication, data processing, control and optimization. Many CPSs are controlled and monitored by real-time control systems that use communication networks to transmit and receive data from systems modeled by physical processes. Existing studies have addressed a breadth of challenges related to the design of CPSs. However, there is a lack of studies on uncertain CPSs subject to dynamic unknown inputs and cyber-attacks---an artifact of the insertion of communication networks and the growing complexity of CPSs. The objective of this dissertation is to create secure, computational foundations for uncertain CPSs by establishing a framework to control, estimate and optimize the operation of these systems. With major emphasis on power networks, the dissertation deals with the design of secure computational methods for uncertain CPSs, focusing on three crucial issues---(1) cyber-security and risk-mitigation, (2) network-induced time-delays and perturbations and (3) the encompassed extreme time-scales. The dissertation consists of four parts. In the first part, we investigate dynamic state estimation (DSE) methods and rigorously examine the strengths and weaknesses of the proposed routines under dynamic attack-vectors and unknown inputs. In the second part, and utilizing high-frequency measurements in smart grids and the developed DSE methods in the first part, we present a risk mitigation strategy that minimizes the encountered threat levels, while ensuring the continual observability of the system through available, safe measurements. The developed methods in the first two parts rely on the assumption that the uncertain CPS is not experiencing time-delays, an assumption that might fail under certain conditions. To overcome this challenge, networked unknown input

Uncertain dynamical systems: A differential game approach

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Gutman, S.

1976-01-01

A class of dynamical systems in a conflict situation is formulated and discussed, and the formulation is applied to the study of an important class of systems in the presence of uncertainty. The uncertainty is deterministic and the only assumption is that its value belongs to a known compact set. Asymptotic stability is fully discussed with application to variable structure and model reference control systems.

Robust master-slave synchronization for general uncertain delayed dynamical model based on adaptive control scheme.

Science.gov (United States)

Wang, Tianbo; Zhou, Wuneng; Zhao, Shouwei; Yu, Weiqin

2014-03-01

In this paper, the robust exponential synchronization problem for a class of uncertain delayed master-slave dynamical system is investigated by using the adaptive control method. Different from some existing master-slave models, the considered master-slave system includes bounded unmodeled dynamics. In order to compensate the effect of unmodeled dynamics and effectively achieve synchronization, a novel adaptive controller with simple updated laws is proposed. Moreover, the results are given in terms of LMIs, which can be easily solved by LMI Toolbox in Matlab. A numerical example is given to illustrate the effectiveness of the method. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Dynamics Model Applied to Pricing Options with Uncertain Volatility

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Lorella Fatone

2012-01-01

model is proposed. The data used to test the calibration problem included observations of asset prices over a finite set of (known equispaced discrete time values. Statistical tests were used to estimate the statistical significance of the two parameters of the Black-Scholes model: the volatility and the drift. The effects of these estimates on the option pricing problem were investigated. In particular, the pricing of an option with uncertain volatility in the Black-Scholes framework was revisited, and a statistical significance was associated with the price intervals determined using the Black-Scholes-Barenblatt equations. Numerical experiments involving synthetic and real data were presented. The real data considered were the daily closing values of the S&P500 index and the associated European call and put option prices in the year 2005. The method proposed here for calibrating the Black-Scholes dynamics model could be extended to other science and engineering models that may be expressed in terms of stochastic dynamical systems.

Controlling Uncertain Dynamical Systems

Indian Academy of Sciences (India)

Author Affiliations. N Ananthkrishnan1 Rashi Bansal2. Head, CAE Analysis & Design Zeus Numerix Pvt Ltd. M-03, SINE, IIT Bombay Powai Mumbai 400076, India. MTech (Aerospace Engineering) with specialization in Dynamics & Control from IIT Bombay.

Adaptive Control for Linear Uncertain Systems with Unmodeled Dynamics Revisited via Optimal Control Modification

Science.gov (United States)

Nguyen, Nhan

2013-01-01

This paper presents the optimal control modification for linear uncertain plants. The Lyapunov analysis shows that the modification parameter has a limiting value depending on the nature of the uncertainty. The optimal control modification exhibits a linear asymptotic property that enables it to be analyzed in a linear time invariant framework for linear uncertain plants. The linear asymptotic property shows that the closed-loop plants in the limit possess a scaled input-output mapping. Using this property, we can derive an analytical closed-loop transfer function in the limit as the adaptive gain tends to infinity. The paper revisits the Rohrs counterexample problem that illustrates the nature of non-robustness of model-reference adaptive control in the presence of unmodeled dynamics. An analytical approach is developed to compute exactly the modification parameter for the optimal control modification that stabilizes the plant in the Rohrs counterexample. The linear asymptotic property is also used to address output feedback adaptive control for non-minimum phase plants with a relative degree 1.

Robust lyapunov controller for uncertain systems

KAUST Repository

Laleg-Kirati, Taous-Meriem; Elmetennani, Shahrazed

2017-01-01

Various examples of systems and methods are provided for Lyapunov control for uncertain systems. In one example, a system includes a process plant and a robust Lyapunov controller configured to control an input of the process plant. The robust

Exponentially asymptotical synchronization in uncertain complex dynamical networks with time delay

Energy Technology Data Exchange (ETDEWEB)

Luo Qun; Yang Han; Li Lixiang; Yang Yixian [Information Security Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876 (China); Han Jiangxue, E-mail: luoqun@bupt.edu.c [National Engineering Laboratory for Disaster Backup and Recovery, Beijing University of Posts and Telecommunications, Beijing 100876 (China)

2010-12-10

Over the past decade, complex dynamical network synchronization has attracted more and more attention and important developments have been made. In this paper, we explore the scheme of globally exponentially asymptotical synchronization in complex dynamical networks with time delay. Based on Lyapunov stability theory and through defining the error function between adjacent nodes, four novel adaptive controllers are designed under four situations where the Lipschitz constants of the state function in nodes are known or unknown and the network structure is certain or uncertain, respectively. These controllers could not only globally asymptotically synchronize all nodes in networks, but also ensure that the error functions do not exceed the pre-scheduled exponential function. Finally, simulations of the synchronization among the chaotic system in the small-world and scale-free network structures are presented, which prove the effectiveness and feasibility of our controllers.

Non-fragile guaranteed cost control for uncertain neutral dynamic systems with time-varying delays in state and control input

International Nuclear Information System (INIS)

Lien, C.-H.

2007-01-01

This article considers non-fragile guaranteed cost control problem for a class of uncertain neutral system with time-varying delays in both state and control input. Delay-dependent criteria are proposed to guarantee the robust stabilization of systems. Linear matrix inequality (LMI) optimization approach is used to solve the non-fragile guaranteed cost control problem. Non-fragile guaranteed cost control for unperturbed neutral system is considered in the first step. Robust non-fragile guaranteed cost control for uncertain neutral system is designed directly from the unperturbed condition. An efficient approach is proposed to design the non-fragile guaranteed cost control for uncertain neutral systems. LMI toolbox of Matlab is used to implement the proposed results. Finally, a numerical example is illustrated to show the usefulness of the proposed results

Control uncertain Genesio-Tesi chaotic system: Adaptive sliding mode approach

International Nuclear Information System (INIS)

Dadras, Sara; Momeni, Hamid Reza

2009-01-01

An adaptive sliding mode control (ASMC) technique is introduced in this paper for a chaotic dynamical system (Genesio-Tesi system). Using the sliding mode control technique, a sliding surface is determined and the control law is established. An adaptive sliding mode control law is derived to make the states of the Genesio-Tesi system asymptotically track and regulate the desired state. The designed control scheme can control the uncertain chaotic behaviors to a desired state without oscillating very fast and guarantee the property of asymptotical stability. An illustrative simulation result is given to demonstrate the effectiveness of the proposed adaptive sliding mode control design.

Dynamical Scheduling and Robust Control in Uncertain Environments with Petri Nets for DESs

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Dimitri Lefebvre

2017-10-01

Full Text Available This paper is about the incremental computation of control sequences for discrete event systems in uncertain environments where uncontrollable events may occur. Timed Petri nets are used for this purpose. The aim is to drive the marking of the net from an initial value to a reference one, in minimal or near-minimal time, by avoiding forbidden markings, deadlocks, and dead branches. The approach is similar to model predictive control with a finite set of control actions. At each step only a small area of the reachability graph is explored: this leads to a reasonable computational complexity. The robustness of the resulting trajectory is also evaluated according to a risk probability. A sufficient condition is provided to compute robust trajectories. The proposed results are applicable to a large class of discrete event systems, in particular in the domains of flexible manufacturing. However, they are also applicable to other domains as communication, computer science, transportation, and traffic as long as the considered systems admit Petri Nets (PNs models. They are suitable for dynamical deadlock-free scheduling and reconfiguration problems in uncertain environments.

Disturbance attenuation for uncertain control systems with contributions by Alberto Isidori and Dietrich Flockerzi

CERN Document Server

Knobloch, Hans Wilhelm

2014-01-01

  This book presents a survey on recent attempts to treat classical regulator design problems in case of an uncertain dynamics. It is shown that source of the uncertainty can be twofold: (i) The system is under the influence of an exogenous disturbance about which one has only incomplete - or none - information. (ii) A portion of the dynamical law is unspecified - due to imperfect modeling. Both cases are described by the state space model in a unified way “Disturbance Attenuation for Uncertain Control Systems” presents a variety of approaches to the design problem in the presence of a (partly) unknown disturbance signal. There is a clear philosophy underlying each approach which can be characterized by either one of the following terms: Adaptive Control, Worst Case Design, Dissipation Inequalities.  .

Boundary Control of Linear Uncertain 1-D Parabolic PDE Using Approximate Dynamic Programming.

Science.gov (United States)

Talaei, Behzad; Jagannathan, Sarangapani; Singler, John

2018-04-01

This paper develops a near optimal boundary control method for distributed parameter systems governed by uncertain linear 1-D parabolic partial differential equations (PDE) by using approximate dynamic programming. A quadratic surface integral is proposed to express the optimal cost functional for the infinite-dimensional state space. Accordingly, the Hamilton-Jacobi-Bellman (HJB) equation is formulated in the infinite-dimensional domain without using any model reduction. Subsequently, a neural network identifier is developed to estimate the unknown spatially varying coefficient in PDE dynamics. Novel tuning law is proposed to guarantee the boundedness of identifier approximation error in the PDE domain. A radial basis network (RBN) is subsequently proposed to generate an approximate solution for the optimal surface kernel function online. The tuning law for near optimal RBN weights is created, such that the HJB equation error is minimized while the dynamics are identified and closed-loop system remains stable. Ultimate boundedness (UB) of the closed-loop system is verified by using the Lyapunov theory. The performance of the proposed controller is successfully confirmed by simulation on an unstable diffusion-reaction process.

Active fault diagnosis in closed-loop uncertain systems

DEFF Research Database (Denmark)

Niemann, Hans Henrik

2006-01-01

Fault diagnosis of parametric faults in closed-loop uncertain systems by using an auxiliary input vector is considered in this paper, i.e. active fault diagnosis (AFD). The active fault diagnosis is based directly on the socalled fault signature matrix, related to the YJBK (Youla, Jabr, Bongiorno...... and Kucera) parameterization. Conditions are given for exact detection and isolation of parametric faults in closed-loop uncertain systems....

Pinning adaptive synchronization of a class of uncertain complex dynamical networks with multi-link against network deterioration

International Nuclear Information System (INIS)

Li, Lixiang; Li, Weiwei; Kurths, Jürgen; Luo, Qun; Yang, Yixian; Li, Shudong

2015-01-01

For the reason that the uncertain complex dynamic network with multi-link is quite close to various practical networks, there is superiority in the fields of research and application. In this paper, we focus upon pinning adaptive synchronization for uncertain complex dynamic networks with multi-link against network deterioration. The pinning approach can be applied to adapt uncertain coupling factors of deteriorated networks which can compensate effects of uncertainty. Several new synchronization criterions for networks with multi-link are derived, which ensure the synchronized states to be local or global stable with uncertainty and deterioration. Results of simulation are shown to demonstrate the feasibility and usefulness of our method

CMAC-based adaptive backstepping synchronization of uncertain chaotic systems

International Nuclear Information System (INIS)

Lin, C.-M.; Peng, Y.-F.; Lin, M.-H.

2009-01-01

This study proposes an adaptive backstepping control system for synchronizing uncertain chaotic system by using cerebellar model articulation controller (CMAC). CMAC is a nonlinear network with simple computation, good generalization capability and fast learning property. The proposed CMAC-based adaptive backstepping control (CABC) system uses backstepping method and adaptive cerebellar model articulation controller (ACMAC) for synchronizing uncertain chaotic system. Finally, simulation results for the Genesio system are presented to illustrate the effectiveness of the proposed control system.

Implementation on Electronic Circuits and RTR Pragmatical Adaptive Synchronization: Time-Reversed Uncertain Dynamical Systems' Analysis and Applications

Directory of Open Access Journals (Sweden)

Shih-Yu Li

2013-01-01

Full Text Available We expose the chaotic attractors of time-reversed nonlinear system, further implement its behavior on electronic circuit, and apply the pragmatical asymptotically stability theory to strictly prove that the adaptive synchronization of given master and slave systems with uncertain parameters can be achieved. In this paper, the variety chaotic motions of time-reversed Lorentz system are investigated through Lyapunov exponents, phase portraits, and bifurcation diagrams. For further applying the complex signal in secure communication and file encryption, we construct the circuit to show the similar chaotic signal of time-reversed Lorentz system. In addition, pragmatical asymptotically stability theorem and an assumption of equal probability for ergodic initial conditions (Ge et al., 1999, Ge and Yu, 2000, and Matsushima, 1972 are proposed to strictly prove that adaptive control can be accomplished successfully. The current scheme of adaptive control—by traditional Lyapunov stability theorem and Barbalat lemma, which are used to prove the error vector—approaches zero, as time approaches infinity. However, the core question—why the estimated or given parameters also approach to the uncertain parameters—remains without answer. By the new stability theory, those estimated parameters can be proved approaching the uncertain values strictly, and the simulation results are shown in this paper.

Passivity analysis and synthesis for uncertain time-delay systems

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Magdi S. Mahmoud

2001-01-01

Full Text Available In this paper, we investigate the robust passivity analysis and synthesis problems for a class of uncertain time-delay systems. This class of systems arises in the modelling effort of studying water quality constituents in fresh stream. For the analysis problem, we derive a sufficient condition for which the uncertain time-delay system is robustly stable and strictly passive for all admissible uncertainties. The condition is given in terms of a linear matrix inequality. Both the delay-independent and delay-dependent cases are considered. For the synthesis problem, we propose an observer-based design method which guarantees that the closed-loop uncertain time-delay system is stable and strictly passive for all admissible uncertainties. Several examples are worked out to illustrate the developed theory.

Intelligent diagnosis of jaundice with dynamic uncertain causality graph model*

Science.gov (United States)

Hao, Shao-rui; Geng, Shi-chao; Fan, Lin-xiao; Chen, Jia-jia; Zhang, Qin; Li, Lan-juan

2017-01-01

Jaundice is a common and complex clinical symptom potentially occurring in hepatology, general surgery, pediatrics, infectious diseases, gynecology, and obstetrics, and it is fairly difficult to distinguish the cause of jaundice in clinical practice, especially for general practitioners in less developed regions. With collaboration between physicians and artificial intelligence engineers, a comprehensive knowledge base relevant to jaundice was created based on demographic information, symptoms, physical signs, laboratory tests, imaging diagnosis, medical histories, and risk factors. Then a diagnostic modeling and reasoning system using the dynamic uncertain causality graph was proposed. A modularized modeling scheme was presented to reduce the complexity of model construction, providing multiple perspectives and arbitrary granularity for disease causality representations. A “chaining” inference algorithm and weighted logic operation mechanism were employed to guarantee the exactness and efficiency of diagnostic reasoning under situations of incomplete and uncertain information. Moreover, the causal interactions among diseases and symptoms intuitively demonstrated the reasoning process in a graphical manner. Verification was performed using 203 randomly pooled clinical cases, and the accuracy was 99.01% and 84.73%, respectively, with or without laboratory tests in the model. The solutions were more explicable and convincing than common methods such as Bayesian Networks, further increasing the objectivity of clinical decision-making. The promising results indicated that our model could be potentially used in intelligent diagnosis and help decrease public health expenditure. PMID:28471111

Intelligent diagnosis of jaundice with dynamic uncertain causality graph model.

Science.gov (United States)

Hao, Shao-Rui; Geng, Shi-Chao; Fan, Lin-Xiao; Chen, Jia-Jia; Zhang, Qin; Li, Lan-Juan

2017-05-01

Jaundice is a common and complex clinical symptom potentially occurring in hepatology, general surgery, pediatrics, infectious diseases, gynecology, and obstetrics, and it is fairly difficult to distinguish the cause of jaundice in clinical practice, especially for general practitioners in less developed regions. With collaboration between physicians and artificial intelligence engineers, a comprehensive knowledge base relevant to jaundice was created based on demographic information, symptoms, physical signs, laboratory tests, imaging diagnosis, medical histories, and risk factors. Then a diagnostic modeling and reasoning system using the dynamic uncertain causality graph was proposed. A modularized modeling scheme was presented to reduce the complexity of model construction, providing multiple perspectives and arbitrary granularity for disease causality representations. A "chaining" inference algorithm and weighted logic operation mechanism were employed to guarantee the exactness and efficiency of diagnostic reasoning under situations of incomplete and uncertain information. Moreover, the causal interactions among diseases and symptoms intuitively demonstrated the reasoning process in a graphical manner. Verification was performed using 203 randomly pooled clinical cases, and the accuracy was 99.01% and 84.73%, respectively, with or without laboratory tests in the model. The solutions were more explicable and convincing than common methods such as Bayesian Networks, further increasing the objectivity of clinical decision-making. The promising results indicated that our model could be potentially used in intelligent diagnosis and help decrease public health expenditure.

Assimilating uncertain, dynamic and intermittent streamflow observations in hydrological models

Science.gov (United States)

Mazzoleni, Maurizio; Alfonso, Leonardo; Chacon-Hurtado, Juan; Solomatine, Dimitri

2015-09-01

Catastrophic floods cause significant socio-economical losses. Non-structural measures, such as real-time flood forecasting, can potentially reduce flood risk. To this end, data assimilation methods have been used to improve flood forecasts by integrating static ground observations, and in some cases also remote sensing observations, within water models. Current hydrologic and hydraulic research works consider assimilation of observations coming from traditional, static sensors. At the same time, low-cost, mobile sensors and mobile communication devices are becoming also increasingly available. The main goal and innovation of this study is to demonstrate the usefulness of assimilating uncertain streamflow observations that are dynamic in space and intermittent in time in the context of two different semi-distributed hydrological model structures. The developed method is applied to the Brue basin, where the dynamic observations are imitated by the synthetic observations of discharge. The results of this study show how model structures and sensors locations affect in different ways the assimilation of streamflow observations. In addition, it proves how assimilation of such uncertain observations from dynamic sensors can provide model improvements similar to those of streamflow observations coming from a non-optimal network of static physical sensors. This can be a potential application of recent efforts to build citizen observatories of water, which can make the citizens an active part in information capturing, evaluation and communication, helping simultaneously to improvement of model-based flood forecasting.

Transient Stability Assessment of Power Systems With Uncertain Renewable Generation: Preprint

Energy Technology Data Exchange (ETDEWEB)

Villegas Pico, Hugo Nestor [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Aliprantis, Dionysios C. [Purdue University; Lin, Xiaojun [Purdue University

2017-08-09

The transient stability of a power system depends heavily on its operational state at the moment of a fault. In systems where the penetration of renewable generation is significant, the dispatch of the conventional fleet of synchronous generators is uncertain at the time of dynamic security analysis. Hence, the assessment of transient stability requires the solution of a system of nonlinear ordinary differential equations with unknown initial conditions and inputs. To this end, we set forth a computational framework that relies on Taylor polynomials, where variables are associated with the level of renewable generation. This paper describes the details of the method and illustrates its application on a nine-bus test system.

Adaptive observer based synchronization of a class of uncertain chaotic systems

International Nuclear Information System (INIS)

Bowong, S.; Yamapi, R.

2005-05-01

This study addresses the adaptive synchronization of a class of uncertain chaotic systems in the drive-response framework. For a class of uncertain chaotic systems with unknown parameters and external disturbances, a robust adaptive observer based response system is constructed to synchronize the uncertain chaotic system. Lyapunov stability theory and Barbalat lemma ensure the global synchronization between the drive and response systems even if Lipschitz constants on function matrices and bounds on uncertainties are unknown. Numerical simulation of the Genesio-Tesi system verifies the effectiveness of the proposed method. (author)

Robust Economic Control Decision Method of Uncertain System on Urban Domestic Water Supply.

Science.gov (United States)

Li, Kebai; Ma, Tianyi; Wei, Guo

2018-03-31

As China quickly urbanizes, urban domestic water generally presents the circumstances of both rising tendency and seasonal cycle fluctuation. A robust economic control decision method for dynamic uncertain systems is proposed in this paper. It is developed based on the internal model principle and pole allocation method, and it is applied to an urban domestic water supply system with rising tendency and seasonal cycle fluctuation. To achieve this goal, first a multiplicative model is used to describe the urban domestic water demand. Then, a capital stock and a labor stock are selected as the state vector, and the investment and labor are designed as the control vector. Next, the compensator subsystem is devised in light of the internal model principle. Finally, by using the state feedback control strategy and pole allocation method, the multivariable robust economic control decision method is implemented. The implementation with this model can accomplish the urban domestic water supply control goal, with the robustness for the variation of parameters. The methodology presented in this study may be applied to the water management system in other parts of the world, provided all data used in this study are available. The robust control decision method in this paper is also applicable to deal with tracking control problems as well as stabilization control problems of other general dynamic uncertain systems.

A design method of bilateral control system with uncertain dynamics of environments

International Nuclear Information System (INIS)

Yamada, Kou; Iida, Noriyuki; Kudou, Naoki

2002-01-01

In the present paper, we examine a design method for master-slave systems of bilateral control systems. In master-slave systems, human operator works to achieve tasks via the master and the salve system. The salve system contacts the environment and works the tasks. According to past studies, when the dynamics of environment is treated as uncertainties, the number of unstable poles of the slave system is required to be equivalent to that of the slave system with the dynamics of the environment. In some cases, the number of unstable poles of the slave system with the dynamics of environment is different from that of the slave system. We propose a simple design method of bilateral control systems such that the number of unstable poles of the slave system with the dynamics of environmental is different from that of the slave system without the dynamics of the environment. (author)

Output Feedback Adaptive Dynamic Surface Control of Permanent Magnet Synchronous Motor with Uncertain Time Delays via RBFNN

Directory of Open Access Journals (Sweden)

Shaohua Luo

2014-01-01

Full Text Available This paper focuses on an adaptive dynamic surface control based on the Radial Basis Function Neural Network for a fourth-order permanent magnet synchronous motor system wherein the unknown parameters, disturbances, chaos, and uncertain time delays are presented. Neural Network systems are used to approximate the nonlinearities and an adaptive law is employed to estimate accurate parameters. Then, a simple and effective controller has been obtained by introducing dynamic surface control technique on the basis of first-order filters. Asymptotically tracking stability in the sense of uniformly ultimate boundedness is achieved in a short time. Finally, the performance of the proposed control has been illustrated through simulation results.

Uncertain Dynamics, Correlation Effects, and Robust Investment Decisions

DEFF Research Database (Denmark)

Flor, Christian Riis; Hesel, Søren

2015-01-01

We analyze a firm's investment problem when the dynamics of project value and investment cost are uncertain. We provide an explicit solution using a robust method for an ambiguity averse firm taking this into account. Ambiguity aversion regarding a common risk factor impacts differently than...... ambiguity aversion regarding investment cost residual risk. Correlation between project value and investment cost matters; ambiguity aversion regarding common risk can decrease the investment probability only if correlation is positive. Ambiguity aversion regarding residual risk always increases...... the investment probability. When only project value is risky, volatility can monotonically decrease the investment threshold; this does not hold with the multiple prior method....

Adaptive estimation for control of uncertain nonlinear systems with applications to target tracking

Science.gov (United States)

Madyastha, Venkatesh Kattigari

2005-08-01

Design of nonlinear observers has received considerable attention since the early development of methods for linear state estimation. The most popular approach is the extended Kalman filter (EKF), that goes through significant degradation in the presence of nonlinearities, particularly if unmodeled dynamics are coupled to the process and the measurement. For uncertain nonlinear systems, adaptive observers have been introduced to estimate the unknown state variables where no priori information about the unknown parameters is available. While establishing global results, these approaches are applicable only to systems transformable to output feedback form. Over the recent years, neural network (NN) based identification and estimation schemes have been proposed that relax the assumptions on the system at the price of sacrificing on the global nature of the results. However, most of the NN based adaptive observer approaches in the literature require knowledge of the full dimension of the system, therefore may not be suitable for systems with unmodeled dynamics. We first propose a novel approach to nonlinear state estimation from the perspective of augmenting a linear time invariant observer with an adaptive element. The class of nonlinear systems treated here are finite but of otherwise unknown dimension. The objective is to improve the performance of the linear observer when applied to a nonlinear system. The approach relies on the ability of the NNs to approximate the unknown dynamics from finite time histories of available measurements. Next we investigate nonlinear state estimation from the perspective of adaptively augmenting an existing time varying observer, such as an EKF. EKFs find their applications mostly in target tracking problems. The proposed approaches are robust to unmodeled dynamics, including unmodeled disturbances. Lastly, we consider the problem of adaptive estimation in the presence of feedback control for a class of uncertain nonlinear systems

Dynamic Learning from Adaptive Neural Control of Uncertain Robots with Guaranteed Full-State Tracking Precision

Directory of Open Access Journals (Sweden)

Min Wang

2017-01-01

Full Text Available A dynamic learning method is developed for an uncertain n-link robot with unknown system dynamics, achieving predefined performance attributes on the link angular position and velocity tracking errors. For a known nonsingular initial robotic condition, performance functions and unconstrained transformation errors are employed to prevent the violation of the full-state tracking error constraints. By combining two independent Lyapunov functions and radial basis function (RBF neural network (NN approximator, a novel and simple adaptive neural control scheme is proposed for the dynamics of the unconstrained transformation errors, which guarantees uniformly ultimate boundedness of all the signals in the closed-loop system. In the steady-state control process, RBF NNs are verified to satisfy the partial persistent excitation (PE condition. Subsequently, an appropriate state transformation is adopted to achieve the accurate convergence of neural weight estimates. The corresponding experienced knowledge on unknown robotic dynamics is stored in NNs with constant neural weight values. Using the stored knowledge, a static neural learning controller is developed to improve the full-state tracking performance. A comparative simulation study on a 2-link robot illustrates the effectiveness of the proposed scheme.

Synchronization and parameter estimations of an uncertain Rikitake system

International Nuclear Information System (INIS)

Aguilar-Ibanez, Carlos; Martinez-Guerra, Rafael; Aguilar-Lopez, Ricardo; Mata-Machuca, Juan L.

2010-01-01

In this Letter we address the synchronization and parameter estimation of the uncertain Rikitake system, under the assumption the state is partially known. To this end we use the master/slave scheme in conjunction with the adaptive control technique. Our control approach consists of proposing a slave system which has to follow asymptotically the uncertain Rikitake system, refereed as the master system. The gains of the slave system are adjusted continually according to a convenient adaptation control law, until the measurable output errors converge to zero. The convergence analysis is carried out by using the Barbalat's Lemma. Under this context, uncertainty means that although the system structure is known, only a partial knowledge of the corresponding parameter values is available.

Navigation of autonomous vehicles for oil spill cleaning in dynamic and uncertain environments

Science.gov (United States)

Jin, Xin; Ray, Asok

2014-04-01

In the context of oil spill cleaning by autonomous vehicles in dynamic and uncertain environments, this paper presents a multi-resolution algorithm that seamlessly integrates the concepts of local navigation and global navigation based on the sensory information; the objective here is to enable adaptive decision making and online replanning of vehicle paths. The proposed algorithm provides a complete coverage of the search area for clean-up of the oil spills and does not suffer from the problem of having local minima, which is commonly encountered in potential-field-based methods. The efficacy of the algorithm is tested on a high-fidelity player/stage simulator for oil spill cleaning in a harbour, where the underlying oil weathering process is modelled as 2D random-walk particle tracking. A preliminary version of this paper was presented by X. Jin and A. Ray as 'Coverage Control of Autonomous Vehicles for Oil Spill Cleaning in Dynamic and Uncertain Environments', Proceedings of the American Control Conference, Washington, DC, June 2013, pp. 2600-2605.

Synchronizing a class of uncertain chaotic systems

International Nuclear Information System (INIS)

Chen Maoyin; Zhou Donghua; Shang Yun

2005-01-01

This Letter deals with the synchronization of a class of uncertain chaotic systems in the drive-response framework. A robust adaptive observer based response system is designed to synchronize a given chaotic system with unknown parameters and external disturbances. Lyapunov stability ensures the global synchronization between the drive and response systems even if Lipschitz constants on function matrices and bounds on uncertainties are unknown. Numerical simulation of Genesio-Tesi system verifies the effectiveness of this scheme

Adaptive generalized function projective lag synchronization of different chaotic systems with fully uncertain parameters

International Nuclear Information System (INIS)

Wu Xiangjun; Lu Hongtao

2011-01-01

Highlights: → Adaptive generalized function projective lag synchronization (AGFPLS) is proposed. → Two uncertain chaos systems are lag synchronized up to a scaling function matrix. → The synchronization speed is sensitively influenced by the control gains. → The AGFPLS scheme is robust against noise perturbation. - Abstract: In this paper, a novel projective synchronization scheme called adaptive generalized function projective lag synchronization (AGFPLS) is proposed. In the AGFPLS method, the states of two different chaotic systems with fully uncertain parameters are asymptotically lag synchronized up to a desired scaling function matrix. By means of the Lyapunov stability theory, an adaptive controller with corresponding parameter update rule is designed for achieving AGFPLS between two diverse chaotic systems and estimating the unknown parameters. This technique is employed to realize AGFPLS between uncertain Lue chaotic system and uncertain Liu chaotic system, and between Chen hyperchaotic system and Lorenz hyperchaotic system with fully uncertain parameters, respectively. Furthermore, AGFPLS between two different uncertain chaotic systems can still be achieved effectively with the existence of noise perturbation. The corresponding numerical simulations are performed to demonstrate the validity and robustness of the presented synchronization method.

Robust output feedback H-infinity control and filtering for uncertain linear systems

CERN Document Server

Chang, Xiao-Heng

2014-01-01

"Robust Output Feedback H-infinity Control and Filtering for Uncertain Linear Systems" discusses new and meaningful findings on robust output feedback H-infinity control and filtering for uncertain linear systems, presenting a number of useful and less conservative design results based on the linear matrix inequality (LMI) technique. Though primarily intended for graduate students in control and filtering, the book can also serve as a valuable reference work for researchers wishing to explore the area of robust H-infinity control and filtering of uncertain systems. Dr. Xiao-Heng Chang is a Professor at the College of Engineering, Bohai University, China.

Singularity-Free Neural Control for the Exponential Trajectory Tracking in Multiple-Input Uncertain Systems with Unknown Deadzone Nonlinearities

Directory of Open Access Journals (Sweden)

J. Humberto Pérez-Cruz

2014-01-01

Full Text Available The trajectory tracking for a class of uncertain nonlinear systems in which the number of possible states is equal to the number of inputs and each input is preceded by an unknown symmetric deadzone is considered. The unknown dynamics is identified by means of a continuous time recurrent neural network in which the control singularity is conveniently avoided by guaranteeing the invertibility of the coupling matrix. Given this neural network-based mathematical model of the uncertain system, a singularity-free feedback linearization control law is developed in order to compel the system state to follow a reference trajectory. By means of Lyapunov-like analysis, the exponential convergence of the tracking error to a bounded zone can be proven. Likewise, the boundedness of all closed-loop signals can be guaranteed.

Synchronization of uncertain chaotic systems using a single transmission channel

International Nuclear Information System (INIS)

Feng Yong; Yu Xinghuo; Sun Lixia

2008-01-01

This paper proposes a robust sliding mode observer for synchronization of uncertain chaotic systems with multi-nonlinearities. A new control strategy is proposed for the construction of the robust sliding mode observer, which can avoid the strict conditions in the design process of Walcott-Zak observer. A new method of multi-dimensional signal transmission via single transmission channel is proposed and applied to chaos synchronization of uncertain chaotic systems with multi-nonlinearities. The simulation results are presented to validate the method

Approximate N-Player Nonzero-Sum Game Solution for an Uncertain Continuous Nonlinear System.

Science.gov (United States)

Johnson, Marcus; Kamalapurkar, Rushikesh; Bhasin, Shubhendu; Dixon, Warren E

2015-08-01

An approximate online equilibrium solution is developed for an N -player nonzero-sum game subject to continuous-time nonlinear unknown dynamics and an infinite horizon quadratic cost. A novel actor-critic-identifier structure is used, wherein a robust dynamic neural network is used to asymptotically identify the uncertain system with additive disturbances, and a set of critic and actor NNs are used to approximate the value functions and equilibrium policies, respectively. The weight update laws for the actor neural networks (NNs) are generated using a gradient-descent method, and the critic NNs are generated by least square regression, which are both based on the modified Bellman error that is independent of the system dynamics. A Lyapunov-based stability analysis shows that uniformly ultimately bounded tracking is achieved, and a convergence analysis demonstrates that the approximate control policies converge to a neighborhood of the optimal solutions. The actor, critic, and identifier structures are implemented in real time continuously and simultaneously. Simulations on two and three player games illustrate the performance of the developed method.

Robust output synchronization of heterogeneous nonlinear agents in uncertain networks.

Science.gov (United States)

Yang, Xi; Wan, Fuhua; Tu, Mengchuan; Shen, Guojiang

2017-11-01

This paper investigates the global robust output synchronization problem for a class of nonlinear multi-agent systems. In the considered setup, the controlled agents are heterogeneous and with both dynamic and parametric uncertainties, the controllers are incapable of exchanging their internal states with the neighbors, and the communication network among agents is defined by an uncertain simple digraph. The problem is pursued via nonlinear output regulation theory and internal model based design. For each agent, the input-driven filter and the internal model compose the controller, and the decentralized dynamic output feedback control law is derived by using backstepping method and the modified dynamic high-gain technique. The theoretical result is applied to output synchronization problem for uncertain network of Lorenz-type agents. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Adaptive synchronization of Rossler system with uncertain parameters

International Nuclear Information System (INIS)

Park, Ju H.

2005-01-01

This article addresses control for the chaos synchronization of Rossler systems with three uncertain parameters. Based on the Lyapunov stability theory, an adaptive control law is derived to make the states of two identical Rossler systems asymptotically synchronized. A numerical simulations is presented to show the effectiveness of the proposed chaos synchronization scheme

Stability analysis of fuzzy parametric uncertain systems.

Science.gov (United States)

Bhiwani, R J; Patre, B M

2011-10-01

In this paper, the determination of stability margin, gain and phase margin aspects of fuzzy parametric uncertain systems are dealt. The stability analysis of uncertain linear systems with coefficients described by fuzzy functions is studied. A complexity reduced technique for determining the stability margin for FPUS is proposed. The method suggested is dependent on the order of the characteristic polynomial. In order to find the stability margin of interval polynomials of order less than 5, it is not always necessary to determine and check all four Kharitonov's polynomials. It has been shown that, for determining stability margin of FPUS of order five, four, and three we require only 3, 2, and 1 Kharitonov's polynomials respectively. Only for sixth and higher order polynomials, a complete set of Kharitonov's polynomials are needed to determine the stability margin. Thus for lower order systems, the calculations are reduced to a large extent. This idea has been extended to determine the stability margin of fuzzy interval polynomials. It is also shown that the gain and phase margin of FPUS can be determined analytically without using graphical techniques. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

Dynamic analysis of large structures with uncertain parameters based on coupling component mode synthesis and perturbation method

Directory of Open Access Journals (Sweden)

D. Sarsri

2016-03-01

Full Text Available This paper presents a methodological approach to compute the stochastic eigenmodes of large FE models with parameter uncertainties based on coupling of second order perturbation method and component mode synthesis methods. Various component mode synthesis methods are used to optimally reduce the size of the model. The statistical first two moments of dynamic response of the reduced system are obtained by the second order perturbation method. Numerical results illustrating the accuracy and efficiency of the proposed coupled methodological procedures for large FE models with uncertain parameters are presented.

Response and reliability analysis of nonlinear uncertain dynamical structures by the probability density evolution method

DEFF Research Database (Denmark)

Nielsen, Søren R. K.; Peng, Yongbo; Sichani, Mahdi Teimouri

2016-01-01

The paper deals with the response and reliability analysis of hysteretic or geometric nonlinear uncertain dynamical systems of arbitrary dimensionality driven by stochastic processes. The approach is based on the probability density evolution method proposed by Li and Chen (Stochastic dynamics...... of structures, 1st edn. Wiley, London, 2009; Probab Eng Mech 20(1):33–44, 2005), which circumvents the dimensional curse of traditional methods for the determination of non-stationary probability densities based on Markov process assumptions and the numerical solution of the related Fokker–Planck and Kolmogorov......–Feller equations. The main obstacle of the method is that a multi-dimensional convolution integral needs to be carried out over the sample space of a set of basic random variables, for which reason the number of these need to be relatively low. In order to handle this problem an approach is suggested, which...

Design of Robust AMB Controllers for Rotors Subjected to Varying and Uncertain Seal Forces

DEFF Research Database (Denmark)

Lauridsen, Jonas Skjødt; Santos, Ilmar

2017-01-01

This paper demonstrates the design and simulation results of model based controllers for AMB systems, subjectedto uncertain and changing dynamic seal forces. Specifically, a turbocharger with a hole-pattern seal mounted acrossthe balance piston is considered. The dynamic forces of the seal, which...... are dependent on the operational conditions,have a significant effect on the overall system dynamics. Furthermore, these forces are considered uncertain.The nominal and the uncertainty representation of the seal model are established using results from conventionalmodelling approaches, i.e. CFD and Bulkflow......, and experimental results. Three controllers are synthesized: I) AnH∞ controller based on nominal plant representation, II) A µ controller, designed to be robust against uncertaintiesin the dynamic seal model and III) a Linear Parameter Varying (LPV) controller, designed to provide a unifiedperformance over a large...

Chaos synchronization of uncertain Genesio-Tesi chaotic systems with deadzone nonlinearity

International Nuclear Information System (INIS)

Sun, Y.-J.

2009-01-01

In this Letter, the concept of practical synchronization is introduced and the chaos synchronization of uncertain Genesio-Tesi chaotic systems with deadzone nonlinearity is investigated. Based on the time-domain approach, a tracking control is proposed to realize chaos synchronization for the uncertain Genesio-Tesi chaotic systems with deadzone nonlinearity. Moreover, the guaranteed exponential convergence rate and convergence radius can be pre-specified. Finally, a numerical example is provided to illustrate the feasibility and effectiveness of the obtained result.

Function Projective Synchronization in Discrete-Time Chaotic System with Uncertain Parameters

International Nuclear Information System (INIS)

Chen Yong; Li Xin

2009-01-01

The function projective synchronization of discrete-time chaotic systems is presented. Based on backstepping design with three controllers, a systematic, concrete and automatic scheme is developed to investigate function projective synchronization (FPS) of discrete-time chaotic systems with uncertain parameters. With the aid of symbolic-numeric computation, we use the proposed scheme to illustrate FPS between two identical 3D Henon-like maps with uncertain parameters. Numeric simulations are used to verify the effectiveness of our scheme. (general)

Robust Stabilization of Nonlinear Systems with Uncertain Varying Control Coefficient

Directory of Open Access Journals (Sweden)

Zaiyue Yang

2014-01-01

Full Text Available This paper investigates the stabilization problem for a class of nonlinear systems, whose control coefficient is uncertain and varies continuously in value and sign. The study emphasizes the development of a robust control that consists of a modified Nussbaum function to tackle the uncertain varying control coefficient. By such a method, the finite-time escape phenomenon has been prevented when the control coefficient is crossing zero and varying its sign. The proposed control guarantees the asymptotic stabilization of the system and boundedness of all closed-loop signals. The control performance is illustrated by a numerical simulation.

Robust lyapunov controller for uncertain systems

KAUST Repository

Laleg-Kirati, Taous-Meriem

2017-02-23

Various examples of systems and methods are provided for Lyapunov control for uncertain systems. In one example, a system includes a process plant and a robust Lyapunov controller configured to control an input of the process plant. The robust Lyapunov controller includes an inner closed loop Lyapunov controller and an outer closed loop error stabilizer. In another example, a method includes monitoring a system output of a process plant; generating an estimated system control input based upon a defined output reference; generating a system control input using the estimated system control input and a compensation term; and adjusting the process plant based upon the system control input to force the system output to track the defined output reference. An inner closed loop Lyapunov controller can generate the estimated system control input and an outer closed loop error stabilizer can generate the system control input.

Robust output observer-based control of neutral uncertain systems with discrete and distributed time delays: LMI optimization approach

International Nuclear Information System (INIS)

Chen, J.-D.

2007-01-01

In this paper, the robust control problem of output dynamic observer-based control for a class of uncertain neutral systems with discrete and distributed time delays is considered. Linear matrix inequality (LMI) optimization approach is used to design the new output dynamic observer-based controls. Three classes of observer-based controls are proposed and the maximal perturbed bound is given. Based on the results of this paper, the constraint of matrix equality is not necessary for designing the observer-based controls. Finally, a numerical example is given to illustrate the usefulness of the proposed method

Fractional-order sliding mode control for a class of uncertain nonlinear systems based on LQR

Directory of Open Access Journals (Sweden)

Dong Zhang

2017-03-01

Full Text Available This article presents a new fractional-order sliding mode control (FOSMC strategy based on a linear-quadratic regulator (LQR for a class of uncertain nonlinear systems. First, input/output feedback linearization is used to linearize the nonlinear system and decouple tracking error dynamics. Second, LQR is designed to ensure that the tracking error dynamics converges to the equilibrium point as soon as possible. Based on LQR, a novel fractional-order sliding surface is introduced. Subsequently, the FOSMC is designed to reject system uncertainties and reduce the magnitude of control chattering. Then, the global stability of the closed-loop control system is analytically proved using Lyapunov stability theory. Finally, a typical single-input single-output system and a typical multi-input multi-output system are simulated to illustrate the effectiveness and advantages of the proposed control strategy. The results of the simulation indicate that the proposed control strategy exhibits excellent performance and robustness with system uncertainties. Compared to conventional integer-order sliding mode control, the high-frequency chattering of the control input is drastically depressed.

Parameter identification technique for uncertain chaotic systems using state feedback and steady-state analysis.

Science.gov (United States)

Zaher, Ashraf A

2008-03-01

A technique is introduced for identifying uncertain and/or unknown parameters of chaotic dynamical systems via using simple state feedback. The proposed technique is based on bringing the system into a stable steady state and then solving for the unknown parameters using a simple algebraic method that requires access to the complete or partial states of the system depending on the dynamical model of the chaotic system. The choice of the state feedback is optimized in terms of practicality and causality via employing a single feedback signal and tuning the feedback gain to ensure both stability and identifiability. The case when only a single scalar time series of one of the states is available is also considered and it is demonstrated that a synchronization-based state observer can be augmented to the state feedback to address this problem. A detailed case study using the Lorenz system is used to exemplify the suggested technique. In addition, both the Rössler and Chua systems are examined as possible candidates for utilizing the proposed methodology when partial identification of the unknown parameters is considered. Finally, the dependence of the proposed technique on the structure of the chaotic dynamical model and the operating conditions is discussed and its advantages and limitations are highlighted via comparing it with other methods reported in the literature.

Adaptive fuzzy observer-based stabilization of a class of uncertain time-delayed chaotic systems with actuator nonlinearities

International Nuclear Information System (INIS)

Shahnazi, Reza; Haghani, Adel; Jeinsch, Torsten

2015-01-01

An observer-based output feedback adaptive fuzzy controller is proposed to stabilize a class of uncertain chaotic systems with unknown time-varying time delays, unknown actuator nonlinearities and unknown external disturbances. The actuator nonlinearity can be backlash-like hysteresis or dead-zone. Based on universal approximation property of fuzzy systems the unknown nonlinear functions are approximated by fuzzy systems, where the consequent parts of fuzzy rules are tuned with adaptive schemes. The proposed method does not need the availability of the states and an observer based output feedback approach is proposed to estimate the states. To have more robustness and at the same time to alleviate chattering an adaptive discontinuous structure is suggested. Semi-global asymptotic stability of the overall system is ensured by proposing a suitable Lyapunov–Krasovskii functional candidate. The approach is applied to stabilize the time-delayed Lorenz chaotic system with uncertain dynamics amid significant disturbances. Analysis of simulations reveals the effectiveness of the proposed method in terms of coping well with the modeling uncertainties, nonlinearities in actuators, unknown time-varying time-delays and unknown external disturbances while maintaining asymptotic convergence

Adaptive synchronization of hyperchaotic Chen system with uncertain parameters

International Nuclear Information System (INIS)

Park, Ju H.

2005-01-01

This article addresses control for the chaos synchronization of hyperchaotic Chen system with five uncertain parameters. Based on the Lyapunov stability theory, an adaptive control law is derived to make the states of two identical hyperchaotic Chen systems asymptotically synchronized. Finally, a numerical simulations is presented to show the effectiveness of the proposed chaos synchronization scheme

Parameter-dependent PWQ Lyapunov function stability criteria for uncertain piecewise linear systems

Directory of Open Access Journals (Sweden)

Morten Hovd

2018-01-01

Full Text Available The calculation of piecewise quadratic (PWQ Lyapunov functions is addressed in view of stability analysis of uncertain piecewise linear dynamics. As main contribution, the linear matrix inequality (LMI approach proposed in (Johansson and Rantzer, 1998 for the stability analysis of PWL and PWA dynamics is extended to account for parametric uncertainty based on a improved relaxation technique. The results are applied for the analysis of a Phase Locked Loop (PLL benchmark and the ability to guarantee a stability region in the parameter space well beyond the state of the art is demonstrated.

Robust digital controllers for uncertain chaotic systems: A digital redesign approach

Energy Technology Data Exchange (ETDEWEB)

Ababneh, Mohammad [Department of Controls, FMC Kongsberg Subsea, FMC Energy Systems, Houston, TX 77067 (United States); Barajas-Ramirez, Juan-Gonzalo [CICESE, Depto. De Electronica y Telecomunicaciones, Ensenada, BC, 22860 (Mexico); Chen Guanrong [Centre for Chaos Control and Synchronization, Department of Electronic Engineering, City University of Hong Kong (China); Shieh, Leang S. [Department of Electrical and Computer Engineering, University of Houston, Houston, TX 77204-4005 (United States)

2007-03-15

In this paper, a new and systematic method for designing robust digital controllers for uncertain nonlinear systems with structured uncertainties is presented. In the proposed method, a controller is designed in terms of the optimal linear model representation of the nominal system around each operating point of the trajectory, while the uncertainties are decomposed such that the uncertain nonlinear system can be rewritten as a set of local linear models with disturbed inputs. Applying conventional robust control techniques, continuous-time robust controllers are first designed to eliminate the effects of the uncertainties on the underlying system. Then, a robust digital controller is obtained as the result of a digital redesign of the designed continuous-time robust controller using the state-matching technique. The effectiveness of the proposed controller design method is illustrated through some numerical examples on complex nonlinear systems--chaotic systems.

Synchronization transmission of laser pattern signal within uncertain switched network

Science.gov (United States)

Lü, Ling; Li, Chengren; Li, Gang; Sun, Ao; Yan, Zhe; Rong, Tingting; Gao, Yan

2017-06-01

We propose a new technology for synchronization transmission of laser pattern signal within uncertain network with controllable topology. In synchronization process, the connection of dynamic network can vary at all time according to different demands. Especially, we construct the Lyapunov function of network through designing a special semi-positive definite function, and the synchronization transmission of laser pattern signal within uncertain network with controllable topology can be realized perfectly, which effectively avoids the complicated calculation for solving the second largest eignvalue of the coupling matrix of the dynamic network in order to obtain the network synchronization condition. At the same time, the uncertain parameters in dynamic equations belonging to network nodes can also be identified accurately via designing the identification laws of uncertain parameters. In addition, there are not any limitations for the synchronization target of network in the new technology, in other words, the target can either be a state variable signal of an arbitrary node within the network or an exterior signal.

Invariant set computation for constrained uncertain discrete-time systems

NARCIS (Netherlands)

Athanasopoulos, N.; Bitsoris, G.

2010-01-01

In this article a novel approach to the determination of polytopic invariant sets for constrained discrete-time linear uncertain systems is presented. First, the problem of stabilizing a prespecified initial condition set in the presence of input and state constraints is addressed. Second, the

Evaluating system behavior through Dynamic Master Logic Diagram (DMLD) modeling

International Nuclear Information System (INIS)

Hu, Y.-S.; Modarres, Mohammad

1999-01-01

In this paper, the Dynamic Master Logic Diagram (DMLD) is introduced for representing full-scale time-dependent behavior and uncertain behavior of complex physical systems. Conceptually, the DMLD allows one to decompose a complex system hierarchically to model and to represent: (1) partial success/failure of the system, (2) full-scale logical, physical and fuzzy connectivity relations, (3) probabilistic, resolutional or linguistic uncertainty, (4) multiple-state system dynamics, and (5) floating threshold and transition effects. To demonstrate the technique, examples of using DMLD to model, to diagnose and to control dynamic behavior of a system are presented. A DMLD-based expert system building tool, called Dynamic Reliability Expert System (DREXs), is introduced to automate the DMLD modeling process

Analysing the uncertain future of copper with three exploratory system dynamics models

OpenAIRE

Auping, W.; Pruyt, E.; Kwakkel, J.H.

2012-01-01

High copper prices, the prospect of a transition to a more sustainable energy mix and increasing copper demands from emerging economies have not led to an in-creased attention to the base metal copper in mineral scarcity discussions. The copper system is well documented, but especially regarding the demand of copper many uncertainties exist. In order to create insight in this systems behaviour in the coming 40 years, an Exploratory System Dynamics Modelling and Analysis study was performed. T...

Robust intelligent sliding model control using recurrent cerebellar model articulation controller for uncertain nonlinear chaotic systems

International Nuclear Information System (INIS)

Peng Yafu

2009-01-01

In this paper, a robust intelligent sliding model control (RISMC) scheme using an adaptive recurrent cerebellar model articulation controller (RCMAC) is developed for a class of uncertain nonlinear chaotic systems. This RISMC system offers a design approach to drive the state trajectory to track a desired trajectory, and it is comprised of an adaptive RCMAC and a robust controller. The adaptive RCMAC is used to mimic an ideal sliding mode control (SMC) due to unknown system dynamics, and a robust controller is designed to recover the residual approximation error for guaranteeing the stable characteristic. Moreover, the Taylor linearization technique is employed to derive the linearized model of the RCMAC. The all adaptation laws of the RISMC system are derived based on the Lyapunov stability analysis and projection algorithm, so that the stability of the system can be guaranteed. Finally, the proposed RISMC system is applied to control a Van der Pol oscillator, a Genesio chaotic system and a Chua's chaotic circuit. The effectiveness of the proposed control scheme is verified by some simulation results with unknown system dynamics and existence of external disturbance. In addition, the advantages of the proposed RISMC are indicated in comparison with a SMC system

Sub-optimal control of fuzzy linear dynamical systems under granular differentiability concept.

Science.gov (United States)

Mazandarani, Mehran; Pariz, Naser

2018-05-01

This paper deals with sub-optimal control of a fuzzy linear dynamical system. The aim is to keep the state variables of the fuzzy linear dynamical system close to zero in an optimal manner. In the fuzzy dynamical system, the fuzzy derivative is considered as the granular derivative; and all the coefficients and initial conditions can be uncertain. The criterion for assessing the optimality is regarded as a granular integral whose integrand is a quadratic function of the state variables and control inputs. Using the relative-distance-measure (RDM) fuzzy interval arithmetic and calculus of variations, the optimal control law is presented as the fuzzy state variables feedback. Since the optimal feedback gains are obtained as fuzzy functions, they need to be defuzzified. This will result in the sub-optimal control law. This paper also sheds light on the restrictions imposed by the approaches which are based on fuzzy standard interval arithmetic (FSIA), and use strongly generalized Hukuhara and generalized Hukuhara differentiability concepts for obtaining the optimal control law. The granular eigenvalues notion is also defined. Using an RLC circuit mathematical model, it is shown that, due to their unnatural behavior in the modeling phenomenon, the FSIA-based approaches may obtain some eigenvalues sets that might be different from the inherent eigenvalues set of the fuzzy dynamical system. This is, however, not the case with the approach proposed in this study. The notions of granular controllability and granular stabilizability of the fuzzy linear dynamical system are also presented in this paper. Moreover, a sub-optimal control for regulating a Boeing 747 in longitudinal direction with uncertain initial conditions and parameters is gained. In addition, an uncertain suspension system of one of the four wheels of a bus is regulated using the sub-optimal control introduced in this paper. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Lyapunov-based control of limit cycle oscillations in uncertain aircraft systems

Science.gov (United States)

Bialy, Brendan

Store-induced limit cycle oscillations (LCO) affect several fighter aircraft and is expected to remain an issue for next generation fighters. LCO arises from the interaction of aerodynamic and structural forces, however the primary contributor to the phenomenon is still unclear. The practical concerns regarding this phenomenon include whether or not ordnance can be safely released and the ability of the aircrew to perform mission-related tasks while in an LCO condition. The focus of this dissertation is the development of control strategies to suppress LCO in aircraft systems. The first contribution of this work (Chapter 2) is the development of a controller consisting of a continuous Robust Integral of the Sign of the Error (RISE) feedback term with a neural network (NN) feedforward term to suppress LCO behavior in an uncertain airfoil system. The second contribution of this work (Chapter 3) is the extension of the development in Chapter 2 to include actuator saturation. Suppression of LCO behavior is achieved through the implementation of an auxiliary error system that features hyperbolic functions and a saturated RISE feedback control structure. Due to the lack of clarity regarding the driving mechanism behind LCO, common practice in literature and in Chapters 2 and 3 is to replicate the symptoms of LCO by including nonlinearities in the wing structure, typically a nonlinear torsional stiffness. To improve the accuracy of the system model a partial differential equation (PDE) model of a flexible wing is derived (see Appendix F) using Hamilton's principle. Chapters 4 and 5 are focused on developing boundary control strategies for regulating the bending and twisting deformations of the derived model. The contribution of Chapter 4 is the construction of a backstepping-based boundary control strategy for a linear PDE model of an aircraft wing. The backstepping-based strategy transforms the original system to a exponentially stable system. A Lyapunov-based stability

Robust Fuzzy Control for Fractional-Order Uncertain Hydroturbine Regulating System with Random Disturbances

Directory of Open Access Journals (Sweden)

Fengjiao Wu

2016-01-01

Full Text Available The robust fuzzy control for fractional-order hydroturbine regulating system is studied in this paper. First, the more practical fractional-order hydroturbine regulating system with uncertain parameters and random disturbances is presented. Then, on the basis of interval matrix theory and fractional-order stability theorem, a fuzzy control method is proposed for fractional-order hydroturbine regulating system, and the stability condition is expressed as a group of linear matrix inequalities. Furthermore, the proposed method has good robustness which can process external random disturbances and uncertain parameters. Finally, the validity and superiority are proved by the numerical simulations.

Distributed Robustness Analysis of Interconnected Uncertain Systems Using Chordal Decomposition

DEFF Research Database (Denmark)

Pakazad, Sina Khoshfetrat; Hansson, Anders; Andersen, Martin Skovgaard

2014-01-01

Large-scale interconnected uncertain systems commonly have large state and uncertainty dimensions. Aside from the heavy computational cost of performing robust stability analysis in a centralized manner, privacy requirements in the network can also introduce further issues. In this paper, we util...

Adaptive variable structure control for uncertain chaotic systems containing dead-zone nonlinearity

International Nuclear Information System (INIS)

Yan, J.-J.; Shyu, K.-K.; Lin, J.-S.

2005-01-01

This paper addresses a practical tracking problem for a class of uncertain chaotic systems with dead-zone nonlinearity in the input function. Based on the Lyapunov stability theorem and Barbalat lemma, an adaptive variable structure controller (AVSC) is proposed to ensure the occurrence of the sliding mode even though the control input contains a dead-zone. Also it is worthy of note that the proposed AVSC involves no information of the upper bound of uncertainty. Thus, the limitation of knowing the bound of uncertainty in advance is certainly released. Furthermore, in the sliding mode, the investigated uncertain chaotic system remains insensitive to the uncertainty, and behaves like a linear system. Finally, a well-known Duffing-Holmes chaotic system is used to demonstrate the feasibility of the proposed AVSC

Chattering-free fuzzy sliding-mode control strategy for uncertain chaotic systems

International Nuclear Information System (INIS)

Yau, H.-T.; Chen, C.-L.

2006-01-01

This paper proposes a chattering-free fuzzy sliding-mode control (FSMC) strategy for uncertain chaotic systems. A fuzzy logic control is used to replace the discontinuous sign function of the reaching law in traditional sliding-mode control (SMC), and hence a control input without chattering is obtained in the chaotic systems with uncertainties. Base on the Lyapunov stability theory, we address the design schemes of integration fuzzy sliding-mode control, where the reaching law is proposed by a set of linguistic rules and the control input is chattering free. The Genesio chaotic system is used to test the proposed control strategy and the simulation results show the FSMC not only can control the uncertain chaotic behaviors to a desired state without oscillator very fast, but also the switching function is smooth without chattering. This result implies that this strategy is feasible and effective for chaos control

Global sensitivity analysis in stochastic simulators of uncertain reaction networks.

Science.gov (United States)

Navarro Jimenez, M; Le Maître, O P; Knio, O M

2016-12-28

Stochastic models of chemical systems are often subjected to uncertainties in kinetic parameters in addition to the inherent random nature of their dynamics. Uncertainty quantification in such systems is generally achieved by means of sensitivity analyses in which one characterizes the variability with the uncertain kinetic parameters of the first statistical moments of model predictions. In this work, we propose an original global sensitivity analysis method where the parametric and inherent variability sources are both treated through Sobol's decomposition of the variance into contributions from arbitrary subset of uncertain parameters and stochastic reaction channels. The conceptual development only assumes that the inherent and parametric sources are independent, and considers the Poisson processes in the random-time-change representation of the state dynamics as the fundamental objects governing the inherent stochasticity. A sampling algorithm is proposed to perform the global sensitivity analysis, and to estimate the partial variances and sensitivity indices characterizing the importance of the various sources of variability and their interactions. The birth-death and Schlögl models are used to illustrate both the implementation of the algorithm and the richness of the proposed analysis method. The output of the proposed sensitivity analysis is also contrasted with a local derivative-based sensitivity analysis method classically used for this type of systems.

Global sensitivity analysis in stochastic simulators of uncertain reaction networks

KAUST Repository

Navarro, María

2016-12-26

Stochastic models of chemical systems are often subjected to uncertainties in kinetic parameters in addition to the inherent random nature of their dynamics. Uncertainty quantification in such systems is generally achieved by means of sensitivity analyses in which one characterizes the variability with the uncertain kinetic parameters of the first statistical moments of model predictions. In this work, we propose an original global sensitivity analysis method where the parametric and inherent variability sources are both treated through Sobol’s decomposition of the variance into contributions from arbitrary subset of uncertain parameters and stochastic reaction channels. The conceptual development only assumes that the inherent and parametric sources are independent, and considers the Poisson processes in the random-time-change representation of the state dynamics as the fundamental objects governing the inherent stochasticity. A sampling algorithm is proposed to perform the global sensitivity analysis, and to estimate the partial variances and sensitivity indices characterizing the importance of the various sources of variability and their interactions. The birth-death and Schlögl models are used to illustrate both the implementation of the algorithm and the richness of the proposed analysis method. The output of the proposed sensitivity analysis is also contrasted with a local derivative-based sensitivity analysis method classically used for this type of systems.

Augmented nonlinear differentiator design and application to nonlinear uncertain systems.

Science.gov (United States)

Shao, Xingling; Liu, Jun; Li, Jie; Cao, Huiliang; Shen, Chong; Zhang, Xiaoming

2017-03-01

In this paper, an augmented nonlinear differentiator (AND) based on sigmoid function is developed to calculate the noise-less time derivative under noisy measurement condition. The essential philosophy of proposed AND in achieving high attenuation of noise effect is established by expanding the signal dynamics with extra state variable representing the integrated noisy measurement, then with the integral of measurement as input, the augmented differentiator is formulated to improve the estimation quality. The prominent advantages of the present differentiation technique are: (i) better noise suppression ability can be achieved without appreciable delay; (ii) the improved methodology can be readily extended to construct augmented high-order differentiator to obtain multiple derivatives. In addition, the convergence property and robustness performance against noises are investigated via singular perturbation theory and describing function method, respectively. Also, comparison with several classical differentiators is given to illustrate the superiority of AND in noise suppression. Finally, the robust control problems of nonlinear uncertain systems, including a numerical example and a mass spring system, are addressed to demonstrate the effectiveness of AND in precisely estimating the disturbance and providing the unavailable differential estimate to implement output feedback based controller. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Fault tolerant control for uncertain systems with parametric faults

DEFF Research Database (Denmark)

Niemann, Hans Henrik; Poulsen, Niels Kjølstad

2006-01-01

A fault tolerant control (FTC) architecture based on active fault diagnosis (AFD) and the YJBK (Youla, Jarb, Bongiorno and Kucera)parameterization is applied in this paper. Based on the FTC architecture, fault tolerant control of uncertain systems with slowly varying parametric faults...... is investigated. Conditions are given for closed-loop stability in case of false alarms or missing fault detection/isolation....

ON A NUMERICAL ALGORITHM FOR UNCERTAIN SYSTEM ∫ Φ ...

African Journals Online (AJOL)

Administrator

Science World Journal Vol 7 (No 1) 2012 www.scienceworldjournal.org. ISSN 1597-6343. On a Numerical Algorithm for Uncertain System. Newton's Algorithm. Step 1 Calculate. )(),().(k k k. xAxgxF. Step 2. Check if ε. <. )(k xg for a predetermined ,ε if so stop, else. Step3. Set k k. PxA. )( = )(k xg. -. Step4. Set k k k. Px x. +. = +1.

On robust control of uncertain chaotic systems: a sliding-mode synthesis via chaotic optimization

International Nuclear Information System (INIS)

Lu Zhao; Shieh Leangsan; Chen GuanRong

2003-01-01

This paper presents a novel Lyapunov-based control approach which utilizes a Lyapunov function of the nominal plant for robust tracking control of general multi-input uncertain nonlinear systems. The difficulty of constructing a control Lyapunov function is alleviated by means of predefining an optimal sliding mode. The conventional schemes for constructing sliding modes of nonlinear systems stipulate that the system of interest is canonical-transformable or feedback-linearizable. An innovative approach that exploits a chaotic optimizing algorithm is developed thereby obtaining the optimal sliding manifold for the control purpose. Simulations on the uncertain chaotic Chen's system illustrate the effectiveness of the proposed approach

Robust adaptive fault-tolerant control for leader-follower flocking of uncertain multi-agent systems with actuator failure.

Science.gov (United States)

Yazdani, Sahar; Haeri, Mohammad

2017-11-01

In this work, we study the flocking problem of multi-agent systems with uncertain dynamics subject to actuator failure and external disturbances. By considering some standard assumptions, we propose a robust adaptive fault tolerant protocol for compensating of the actuator bias fault, the partial loss of actuator effectiveness fault, the model uncertainties, and external disturbances. Under the designed protocol, velocity convergence of agents to that of virtual leader is guaranteed while the connectivity preservation of network and collision avoidance among agents are ensured as well. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Using reference trajectories to predicted uncertain systems: exemplified on a power plant

DEFF Research Database (Denmark)

Odgaard, Peter Fogh; Stoustrup, Jakob; Mataji, B.

2007-01-01

This paper presents a method for prediction of uncertain closed loop systems, where the uncertainties are depending on operating points. Such model uncertainties are often present when complicated non-linear systems are predicted. The method uses precomputed mean and variances of the prediction e...

Parameter Identification and Synchronization of Uncertain Chaotic Systems Based on Sliding Mode Observer

Directory of Open Access Journals (Sweden)

Li-lian Huang

2013-01-01

Full Text Available The synchronization of nonlinear uncertain chaotic systems is investigated. We propose a sliding mode state observer scheme which combines the sliding mode control with observer theory and apply it into the uncertain chaotic system with unknown parameters and bounded interference. Based on Lyapunov stability theory, the constraints of synchronization and proof are given. This method not only can realize the synchronization of chaotic systems, but also identify the unknown parameters and obtain the correct parameter estimation. Otherwise, the synchronization of chaotic systems with unknown parameters and bounded external disturbances is robust by the design of the sliding surface. Finally, numerical simulations on Liu chaotic system with unknown parameters and disturbances are carried out. Simulation results show that this synchronization and parameter identification has been totally achieved and the effectiveness is verified very well.

Adaptive control for a class of nonlinear complex dynamical systems with uncertain complex parameters and perturbations.

Directory of Open Access Journals (Sweden)

Jian Liu

Full Text Available In this paper, adaptive control is extended from real space to complex space, resulting in a new control scheme for a class of n-dimensional time-dependent strict-feedback complex-variable chaotic (hyperchaotic systems (CVCSs in the presence of uncertain complex parameters and perturbations, which has not been previously reported in the literature. In detail, we have developed a unified framework for designing the adaptive complex scalar controller to ensure this type of CVCSs asymptotically stable and for selecting complex update laws to estimate unknown complex parameters. In particular, combining Lyapunov functions dependent on complex-valued vectors and back-stepping technique, sufficient criteria on stabilization of CVCSs are derived in the sense of Wirtinger calculus in complex space. Finally, numerical simulation is presented to validate our theoretical results.

Adaptive control for a class of nonlinear complex dynamical systems with uncertain complex parameters and perturbations.

Science.gov (United States)

Liu, Jian; Liu, Kexin; Liu, Shutang

2017-01-01

In this paper, adaptive control is extended from real space to complex space, resulting in a new control scheme for a class of n-dimensional time-dependent strict-feedback complex-variable chaotic (hyperchaotic) systems (CVCSs) in the presence of uncertain complex parameters and perturbations, which has not been previously reported in the literature. In detail, we have developed a unified framework for designing the adaptive complex scalar controller to ensure this type of CVCSs asymptotically stable and for selecting complex update laws to estimate unknown complex parameters. In particular, combining Lyapunov functions dependent on complex-valued vectors and back-stepping technique, sufficient criteria on stabilization of CVCSs are derived in the sense of Wirtinger calculus in complex space. Finally, numerical simulation is presented to validate our theoretical results.

Adaptive synchronization of a new hyperchaotic system with uncertain parameters

International Nuclear Information System (INIS)

Gao Tiegang; Chen Zengqiang; Yuan Zhuzhi; Yu Dongchuan

2007-01-01

This paper discusses control for the master-slave synchronization of a new hyperchaos with five uncertain parameters. An adaptive control law is derived to make the states of two identical hyperchaotic systems asymptotically synchronized based on the Lyapunov stability theory. Finally, a numerical simulation is presented to verify the effectiveness of the proposed synchronization scheme

Minimum Time Search in Uncertain Dynamic Domains with Complex Sensorial Platforms

Science.gov (United States)

Lanillos, Pablo; Besada-Portas, Eva; Lopez-Orozco, Jose Antonio; de la Cruz, Jesus Manuel

2014-01-01

The minimum time search in uncertain domains is a searching task, which appears in real world problems such as natural disasters and sea rescue operations, where a target has to be found, as soon as possible, by a set of sensor-equipped searchers. The automation of this task, where the time to detect the target is critical, can be achieved by new probabilistic techniques that directly minimize the Expected Time (ET) to detect a dynamic target using the observation probability models and actual observations collected by the sensors on board the searchers. The selected technique, described in algorithmic form in this paper for completeness, has only been previously partially tested with an ideal binary detection model, in spite of being designed to deal with complex non-linear/non-differential sensorial models. This paper covers the gap, testing its performance and applicability over different searching tasks with searchers equipped with different complex sensors. The sensorial models under test vary from stepped detection probabilities to continuous/discontinuous differentiable/non-differentiable detection probabilities dependent on distance, orientation, and structured maps. The analysis of the simulated results of several static and dynamic scenarios performed in this paper validates the applicability of the technique with different types of sensor models. PMID:25093345

Adapting environmental management to uncertain but inevitable change.

Science.gov (United States)

Nicol, Sam; Fuller, Richard A; Iwamura, Takuya; Chadès, Iadine

2015-06-07

Implementation of adaptation actions to protect biodiversity is limited by uncertainty about the future. One reason for this is the fear of making the wrong decisions caused by the myriad future scenarios presented to decision-makers. We propose an adaptive management (AM) method for optimally managing a population under uncertain and changing habitat conditions. Our approach incorporates multiple future scenarios and continually learns the best management strategy from observations, even as conditions change. We demonstrate the performance of our AM approach by applying it to the spatial management of migratory shorebird habitats on the East Asian-Australasian flyway, predicted to be severely impacted by future sea-level rise. By accounting for non-stationary dynamics, our solution protects 25,000 more birds per year than the current best stationary approach. Our approach can be applied to many ecological systems that require efficient adaptation strategies for an uncertain future. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Numerical solution of uncertain neutron diffusion equation for ...

Indian Academy of Sciences (India)

The concept of fuzziness is hybridised with ... impreciseness, vagueness, experimental error and different operating conditions affected by the system. ... But the presence of uncertain parameters makes the system uncertain and we get uncer-.

Robust adaptive controller design for a class of uncertain nonlinear systems using online T-S fuzzy-neural modeling approach.

Science.gov (United States)

Chien, Yi-Hsing; Wang, Wei-Yen; Leu, Yih-Guang; Lee, Tsu-Tian

2011-04-01

This paper proposes a novel method of online modeling and control via the Takagi-Sugeno (T-S) fuzzy-neural model for a class of uncertain nonlinear systems with some kinds of outputs. Although studies about adaptive T-S fuzzy-neural controllers have been made on some nonaffine nonlinear systems, little is known about the more complicated uncertain nonlinear systems. Because the nonlinear functions of the systems are uncertain, traditional T-S fuzzy control methods can model and control them only with great difficulty, if at all. Instead of modeling these uncertain functions directly, we propose that a T-S fuzzy-neural model approximates a so-called virtual linearized system (VLS) of the system, which includes modeling errors and external disturbances. We also propose an online identification algorithm for the VLS and put significant emphasis on robust tracking controller design using an adaptive scheme for the uncertain systems. Moreover, the stability of the closed-loop systems is proven by using strictly positive real Lyapunov theory. The proposed overall scheme guarantees that the outputs of the closed-loop systems asymptotically track the desired output trajectories. To illustrate the effectiveness and applicability of the proposed method, simulation results are given in this paper.

A Multi-Pathfinder for Developing Adaptive Robust Policies in System Dynamics

NARCIS (Netherlands)

Hamarat, C.; Pruyt, E.; Loonen, E.T.

2013-01-01

Adaptivity is essential for dynamically complex and uncertain systems. Adaptive policymaking is an approach to design policies that can be adapted over time to how the future unfolds. It is crucial for adaptive policymaking to specify under what conditions and how to adapt the policy. The

On the C(R) stability of uncertain singularly perturbed systems

International Nuclear Information System (INIS)

Sun, Y.-J.

2009-01-01

In this paper, a simple criterion for the C(R) stability of uncertain singularly perturbed systems is proposed. Such a criterion can be easily checked by some algebraic inequality. The upper bound of the singular perturbation parameter ε is also given by estimating the unique positive zero of specific function. Finally, a numerical example is provided to illustrate the main result

Dynamic Power Management for Portable Hybrid Power-Supply Systems Utilizing Approximate Dynamic Programming

Directory of Open Access Journals (Sweden)

Jooyoung Park

2015-05-01

Full Text Available Recently, the optimization of power flows in portable hybrid power-supply systems (HPSSs has become an important issue with the advent of a variety of mobile systems and hybrid energy technologies. In this paper, a control strategy is considered for dynamically managing power flows in portable HPSSs employing batteries and supercapacitors. Our dynamic power management strategy utilizes the concept of approximate dynamic programming (ADP. ADP methods are important tools in the fields of stochastic control and machine learning, and the utilization of these tools for practical engineering problems is now an active and promising research field. We propose an ADP-based procedure based on optimization under constraints including the iterated Bellman inequalities, which can be solved by convex optimization carried out offline, to find the optimal power management rules for portable HPSSs. The effectiveness of the proposed procedure is tested through dynamic simulations for smartphone workload scenarios, and simulation results show that the proposed strategy can successfully cope with uncertain workload demands.

Design of Distributed Engine Control Systems with Uncertain Delay.

Directory of Open Access Journals (Sweden)

Xiaofeng Liu

Full Text Available Future gas turbine engine control systems will be based on distributed architecture, in which, the sensors and actuators will be connected to the controllers via a communication network. The performance of the distributed engine control (DEC is dependent on the network performance. This study introduces a distributed control system architecture based on a networked cascade control system (NCCS. Typical turboshaft engine-distributed controllers are designed based on the NCCS framework with a H∞ output feedback under network-induced time delays and uncertain disturbances. The sufficient conditions for robust stability are derived via the Lyapunov stability theory and linear matrix inequality approach. Both numerical and hardware-in-loop simulations illustrate the effectiveness of the presented method.

Design of Distributed Engine Control Systems with Uncertain Delay.

Science.gov (United States)

Liu, Xiaofeng; Li, Yanxi; Sun, Xu

Future gas turbine engine control systems will be based on distributed architecture, in which, the sensors and actuators will be connected to the controllers via a communication network. The performance of the distributed engine control (DEC) is dependent on the network performance. This study introduces a distributed control system architecture based on a networked cascade control system (NCCS). Typical turboshaft engine-distributed controllers are designed based on the NCCS framework with a H∞ output feedback under network-induced time delays and uncertain disturbances. The sufficient conditions for robust stability are derived via the Lyapunov stability theory and linear matrix inequality approach. Both numerical and hardware-in-loop simulations illustrate the effectiveness of the presented method.

Decomposition and Projection Methods for Distributed Robustness Analysis of Interconnected Uncertain Systems

DEFF Research Database (Denmark)

Pakazad, Sina Khoshfetrat; Hansson, Anders; Andersen, Martin Skovgaard

2013-01-01

We consider a class of convex feasibility problems where the constraints that describe the feasible set are loosely coupled. These problems arise in robust stability analysis of large, weakly interconnected uncertain systems. To facilitate distributed implementation of robust stability analysis o...

Towards electricity markets accommodating uncertain offers

DEFF Research Database (Denmark)

Papakonstantinou, Athanasios; Pinson, Pierre

2014-01-01

formulation of an electricity market, based on the Continuous Ranked Probability Score (CRPS) reduces the impact of poor estimates for both the stochastic producers and the system operator. We introduce a simulation setting which first demonstrates that impact and then proceed to illustrate the main features......The use of renewable energy sources of energy and in particular wind and solar has been on the rise over the last decades with plans to increase it even more. Such developments introduce significant challenges in existing power systems and can result in high electricity prices and costly...... infrastructure investments. In this paper we propose a new electricity market mechanism whereby the uncertain and dynamic nature of wind power and other stochastic sources is embedded in the market mechanism itself, by modelling producers’ bids as probabilistic estimates. An extension on the bilevel programming...

Path Integration Applied to Structural Systems with Uncertain Properties

DEFF Research Database (Denmark)

Nielsen, Søren R.K.; Köylüoglu, H. Ugur

Path integration (cell-to-cell mapping) method is applied to evaluate the joint probability density function (jpdf) of the response of the structural systems, with uncertain properties, subject to white noise excitation. A general methodology to deal with uncertainties is outlined and applied...... to the friction controlled slip of a structure on a foundation where the friction coefficient is modelled as a random variable. Exact results derived using the total probability theorem are compared to the ones obtained via path integration....

IP Controller Design for Uncertain Two-Mass Torsional System Using Time-Frequency Analysis

Directory of Open Access Journals (Sweden)

Jing Cui

2018-01-01

Full Text Available With the development of industrial production, drive systems are demanded for larger inertias of motors and load machines, whereas shafts should be lightweight. In this situation, it will excite mechanical vibrations in load side, which is harmful for industrial production when the motor works. Because of the complexity of the flexible shaft, it is often difficult to calculate stiffness coefficient of the flexible shaft. Furthermore, only the velocity of driving side could be measured, whereas the driving torque, the load torque, and the velocity of load side are immeasurable. Therefore, it is inconvenient to design the controller for the uncertain system. In this paper, a low-order IP controller is designed for an uncertain two-mass torsional system based on polynomial method and time-frequency analysis (TFA. IP controller parameters are calculated by inertias of driving side and load side as well as the resonant frequency based on polynomial method. Therein, the resonant frequency is identified using the time-frequency analysis (TFA of the velocity step response of the driving side under the open-loop system state, which can not only avoid harmful persistent start-stop excitation signal of the traditional method, but also obtain high recognition accuracy under the condition of weak vibration signal submerged in noise. The effectiveness of the designed IP controller is verified by groups of experiments. Experimental results show that good performance for vibration suppression is obtained for uncertain two-mass torsional system in a medium-low shaft stiffness condition.

Fuzzy Backstepping Sliding Mode Control for Mismatched Uncertain System

Directory of Open Access Journals (Sweden)

H. Q. Hou

2014-06-01

Full Text Available Sliding mode controllers have succeeded in many control problems that the conventional control theories have difficulties to deal with; however it is practically impossible to achieve high-speed switching control. Therefore, in this paper an adaptive fuzzy backstepping sliding mode control scheme is derived for mismatched uncertain systems. Firstly fuzzy sliding mode controller is designed using backstepping method based on the Lyapunov function approach, which is capable of handling mismatched problem. Then fuzzy sliding mode controller is designed using T-S fuzzy model method, it can improve the performance of the control systems and their robustness. Finally this method of control is applied to nonlinear system as a case study; simulation results are also provided the performance of the proposed controller.

Using Small Models for Big Issues : Exploratory System Dynamics Modelling and Analysis for Insightful Crisis Management

NARCIS (Netherlands)

Pruyt, E.

2010-01-01

The main goal of this paper is to explain and illustrate different exploratory uses of small System Dynamics models for analysis and decision support in case of dynamically complex issues that are deeply uncertain. The applied focuss of the paper is the field of inter/national safety and security.

Control of uncertain systems by feedback linearization with neural networks augmentation. Part II. Controller validation by numerical simulation

Directory of Open Access Journals (Sweden)

Adrian TOADER

2010-09-01

Full Text Available The paper was conceived in two parts. Part I, previously published in this journal, highlighted the main steps of adaptive output feedback control for non-affine uncertain systems, having a known relative degree. The main paradigm of this approach was the feedback linearization (dynamic inversion with neural network augmentation. Meanwhile, based on new contributions of the authors, a new paradigm, that of robust servomechanism problem solution, has been added to the controller architecture. The current Part II of the paper presents the validation of the controller hereby obtained by using the longitudinal channel of a hovering VTOL-type aircraft as mathematical model.

Sliding mode control for uncertain unified chaotic systems with input nonlinearity

International Nuclear Information System (INIS)

Chiang, T.-Y.; Hung, M.-L.; Yan, J.-J.; Yang, Y.-S.; Chang, J.-F.

2007-01-01

This paper investigates the stabilization problem for a class of unified chaotic systems subject to uncertainties and input nonlinearity. Using the sliding mode control technique, a robust control law is established which stabilizes the uncertain unified chaotic systems even when the nonlinearity in the actuators is present. A novel adaptive switching surface is introduced to simplify the task of assigning the stability of the closed-loop system in the sliding mode motion. An illustrative example is given to demonstrate the effectiveness of the proposed sliding mode control design

Reinforcement-Learning-Based Robust Controller Design for Continuous-Time Uncertain Nonlinear Systems Subject to Input Constraints.

Science.gov (United States)

Liu, Derong; Yang, Xiong; Wang, Ding; Wei, Qinglai

2015-07-01

The design of stabilizing controller for uncertain nonlinear systems with control constraints is a challenging problem. The constrained-input coupled with the inability to identify accurately the uncertainties motivates the design of stabilizing controller based on reinforcement-learning (RL) methods. In this paper, a novel RL-based robust adaptive control algorithm is developed for a class of continuous-time uncertain nonlinear systems subject to input constraints. The robust control problem is converted to the constrained optimal control problem with appropriately selecting value functions for the nominal system. Distinct from typical action-critic dual networks employed in RL, only one critic neural network (NN) is constructed to derive the approximate optimal control. Meanwhile, unlike initial stabilizing control often indispensable in RL, there is no special requirement imposed on the initial control. By utilizing Lyapunov's direct method, the closed-loop optimal control system and the estimated weights of the critic NN are proved to be uniformly ultimately bounded. In addition, the derived approximate optimal control is verified to guarantee the uncertain nonlinear system to be stable in the sense of uniform ultimate boundedness. Two simulation examples are provided to illustrate the effectiveness and applicability of the present approach.

Robust Stability and H∞ Control of Uncertain Piecewise Linear Switched Systems with Filippov Solutions

DEFF Research Database (Denmark)

Ahmadi, Mohamadreza; Mojallali, Hamed; Wisniewski, Rafal

2012-01-01

This paper addresses the robust stability and control problem of uncertain piecewise linear switched systems where, instead of the conventional Carathe ́odory solutions, we allow for Filippov solutions. In other words, in contrast to the previous studies, solutions with infinite switching in fini...... algorithm is proposed to surmount the aforementioned matrix inequality conditions....... time along the facets and on faces of arbitrary dimensions are also taken into account. Firstly, based on earlier results, the stability problem of piecewise linear systems with Filippov solutions is translated into a number of linear matrix inequality feasibility tests. Subsequently, a set of matrix...... inequalities are brought forward, which determines the asymptotic stability of the Filippov solutions of a given uncertain piecewise linear system. Afterwards, bilinear matrix inequality conditions for synthesizing a robust controller with a guaranteed H∞ per- formance are formulated. Finally, a V-K iteration...

Robust Hinf control of uncertain switched systems defined on polyhedral sets with Filippov solutions

DEFF Research Database (Denmark)

Ahmadi, Mohamadreza; Mojallali, Hamed; Wisniewski, Rafal

2012-01-01

This paper considers the control problem of a class of uncertain switched systems defined on polyhedral sets known as piecewise linear systems where, instead of the conventional Carathe ́odory solutions, Filippov solutions are studied. In other words, in contrast to the previous studies, solutions...

A Novel Adaptive Observer-Based Control Scheme for Synchronization and Suppression of a Class of Uncertain Chaotic Systems

International Nuclear Information System (INIS)

Jing, Wang; Zhen-Yu, Tan; Xi-Kui, Ma; Jin-Feng, Gao

2009-01-01

A novel adaptive observer-based control scheme is presented for synchronization and suppression of a class of uncertain chaotic system. First, an adaptive observer based on an orthogonal neural network is designed. Subsequently, the sliding mode controllers via the proposed adaptive observer are proposed for synchronization and suppression of the uncertain chaotic systems. Theoretical analysis and numerical simulation show the effectiveness of the proposed scheme. (general)

Dynamic analysis and reliability assessment of structures with uncertain-but-bounded parameters under stochastic process excitations

International Nuclear Information System (INIS)

Do, Duy Minh; Gao, Wei; Song, Chongmin; Tangaramvong, Sawekchai

2014-01-01

This paper presents the non-deterministic dynamic analysis and reliability assessment of structures with uncertain-but-bounded parameters under stochastic process excitations. Random ground acceleration from earthquake motion is adopted to illustrate the stochastic process force. The exact change ranges of natural frequencies, random vibration displacement and stress responses of structures are investigated under the interval analysis framework. Formulations for structural reliability are developed considering the safe boundary and structural random vibration responses as interval parameters. An improved particle swarm optimization algorithm, namely randomised lower sequence initialized high-order nonlinear particle swarm optimization algorithm, is employed to capture the better bounds of structural dynamic characteristics, random vibration responses and reliability. Three numerical examples are used to demonstrate the presented method for interval random vibration analysis and reliability assessment of structures. The accuracy of the results obtained by the presented method is verified by the randomised Quasi-Monte Carlo simulation method (QMCSM) and direct Monte Carlo simulation method (MCSM). - Highlights: • Interval uncertainty is introduced into structural random vibration responses. • Interval dynamic reliability assessments of structures are implemented. • Boundaries of structural dynamic response and reliability are achieved

Weather and Climate Manipulation as an Optimal Control for Adaptive Dynamical Systems

Directory of Open Access Journals (Sweden)

Sergei A. Soldatenko

2017-01-01

Full Text Available The weather and climate manipulation is examined as an optimal control problem for the earth climate system, which is considered as a complex adaptive dynamical system. Weather and climate manipulations are actually amorphous operations. Since their objectives are usually formulated vaguely, the expected results are fairly unpredictable and uncertain. However, weather and climate modification is a purposeful process and, therefore, we can formulate operations to manipulate weather and climate as the optimization problem within the framework of the optimal control theory. The complexity of the earth’s climate system is discussed and illustrated using the simplified low-order coupled chaotic dynamical system. The necessary conditions of optimality are derived for the large-scale atmospheric dynamics. This confirms that even a relatively simplified control problem for the atmospheric dynamics requires significant efforts to obtain the solution.

Generalized dislocated lag function projective synchronization of fractional order chaotic systems with fully uncertain parameters

International Nuclear Information System (INIS)

Wang, Cong; Zhang, Hong-li; Fan, Wen-hui

2017-01-01

In this paper, we propose a new method to improve the safety of secure communication. This method uses the generalized dislocated lag projective synchronization and function projective synchronization to form a new generalized dislocated lag function projective synchronization. Moreover, this paper takes the examples of fractional order Chen system and Lü system with uncertain parameters as illustration. As the parameters of the two systems are uncertain, the nonlinear controller and parameter update algorithms are designed based on the fractional stability theory and adaptive control method. Moreover, this synchronization form and method of control are applied to secure communication via chaotic masking modulation. Many information signals can be recovered and validated. Finally, simulations are used to show the validity and feasibility of the proposed scheme.

Adaptive and neuroadaptive control for nonnegative and compartmental dynamical systems

Science.gov (United States)

Volyanskyy, Kostyantyn Y.

Neural networks have been extensively used for adaptive system identification as well as adaptive and neuroadaptive control of highly uncertain systems. The goal of adaptive and neuroadaptive control is to achieve system performance without excessive reliance on system models. To improve robustness and the speed of adaptation of adaptive and neuroadaptive controllers several controller architectures have been proposed in the literature. In this dissertation, we develop a new neuroadaptive control architecture for nonlinear uncertain dynamical systems. The proposed framework involves a novel controller architecture with additional terms in the update laws that are constructed using a moving window of the integrated system uncertainty. These terms can be used to identify the ideal system weights of the neural network as well as effectively suppress system uncertainty. Linear and nonlinear parameterizations of the system uncertainty are considered and state and output feedback neuroadaptive controllers are developed. Furthermore, we extend the developed framework to discrete-time dynamical systems. To illustrate the efficacy of the proposed approach we apply our results to an aircraft model with wing rock dynamics, a spacecraft model with unknown moment of inertia, and an unmanned combat aerial vehicle undergoing actuator failures, and compare our results with standard neuroadaptive control methods. Nonnegative systems are essential in capturing the behavior of a wide range of dynamical systems involving dynamic states whose values are nonnegative. A sub-class of nonnegative dynamical systems are compartmental systems. These systems are derived from mass and energy balance considerations and are comprised of homogeneous interconnected microscopic subsystems or compartments which exchange variable quantities of material via intercompartmental flow laws. In this dissertation, we develop direct adaptive and neuroadaptive control framework for stabilization, disturbance

Developing Scenarios for Uncertain Complex Risks : Using SD to Explore Futures of Lyme Disease in the Netherlands

NARCIS (Netherlands)

Pruyt, E.; Coumou, J.

2012-01-01

Lyme disease due to infection with Lyme borreliosis poses an uncertain dynamic threat to the Dutch and their public health system. This risk was used to develop and illustrate two variants of a National Risk Assessment approaches for slumbering/latent risks. This paper explains and illustrates the

Chaos synchronization of uncertain chaotic systems using composite nonlinear feedback based integral sliding mode control.

Science.gov (United States)

Mobayen, Saleh

2018-06-01

This paper proposes a combination of composite nonlinear feedback and integral sliding mode techniques for fast and accurate chaos synchronization of uncertain chaotic systems with Lipschitz nonlinear functions, time-varying delays and disturbances. The composite nonlinear feedback method allows accurate following of the master chaotic system and the integral sliding mode control provides invariance property which rejects the perturbations and preserves the stability of the closed-loop system. Based on the Lyapunov- Krasovskii stability theory and linear matrix inequalities, a novel sufficient condition is offered for the chaos synchronization of uncertain chaotic systems. This method not only guarantees the robustness against perturbations and time-delays, but also eliminates reaching phase and avoids chattering problem. Simulation results demonstrate that the suggested procedure leads to a great control performance. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Robust Fuzzy Control for Fractional-Order Uncertain Hydroturbine Regulating System with Random Disturbances

OpenAIRE

Fengjiao Wu; Guitao Zhang; Zhengzhong Wang

2016-01-01

The robust fuzzy control for fractional-order hydroturbine regulating system is studied in this paper. First, the more practical fractional-order hydroturbine regulating system with uncertain parameters and random disturbances is presented. Then, on the basis of interval matrix theory and fractional-order stability theorem, a fuzzy control method is proposed for fractional-order hydroturbine regulating system, and the stability condition is expressed as a group of linear matrix inequalities. ...

Sustainable infrastructure system modeling under uncertainties and dynamics

Science.gov (United States)

Huang, Yongxi

Infrastructure systems support human activities in transportation, communication, water use, and energy supply. The dissertation research focuses on critical transportation infrastructure and renewable energy infrastructure systems. The goal of the research efforts is to improve the sustainability of the infrastructure systems, with an emphasis on economic viability, system reliability and robustness, and environmental impacts. The research efforts in critical transportation infrastructure concern the development of strategic robust resource allocation strategies in an uncertain decision-making environment, considering both uncertain service availability and accessibility. The study explores the performances of different modeling approaches (i.e., deterministic, stochastic programming, and robust optimization) to reflect various risk preferences. The models are evaluated in a case study of Singapore and results demonstrate that stochastic modeling methods in general offers more robust allocation strategies compared to deterministic approaches in achieving high coverage to critical infrastructures under risks. This general modeling framework can be applied to other emergency service applications, such as, locating medical emergency services. The development of renewable energy infrastructure system development aims to answer the following key research questions: (1) is the renewable energy an economically viable solution? (2) what are the energy distribution and infrastructure system requirements to support such energy supply systems in hedging against potential risks? (3) how does the energy system adapt the dynamics from evolving technology and societal needs in the transition into a renewable energy based society? The study of Renewable Energy System Planning with Risk Management incorporates risk management into its strategic planning of the supply chains. The physical design and operational management are integrated as a whole in seeking mitigations against the

H2 Control for the Continuous-Time Markovian Jump Linear Uncertain Systems with Partly Known Transition Rates and Input Quantization

Directory of Open Access Journals (Sweden)

Xin-Gang Zhao

2013-01-01

Full Text Available For a class of continuous-time Markovian jump linear uncertain systems with partly known transition rates and input quantization, the H2 state-feedback control design is considered. The elements in the transition rates matrix include completely known, boundary known, and completely unknown ones. First, an H2 cost index for Markovian jump linear uncertain systems is introduced; then by introducing a new matrix inequality condition, sufficient conditions are formulated in terms of linear matrix inequalities (LMIs for the H2 control of the Markovian jump linear uncertain systems. Less conservativeness is achieved than the result obtained with the existing technique. Finally, a numerical example is given to verify the validity of the theoretical results.

Adaptive Observer-Based Fault-Tolerant Control Design for Uncertain Systems

Directory of Open Access Journals (Sweden)

Huaming Qian

2015-01-01

Full Text Available This study focuses on the design of the robust fault-tolerant control (FTC system based on adaptive observer for uncertain linear time invariant (LTI systems. In order to improve robustness, rapidity, and accuracy of traditional fault estimation algorithm, an adaptive fault estimation algorithm (AFEA using an augmented observer is presented. By utilizing a new fault estimator model, an improved AFEA based on linear matrix inequality (LMI technique is proposed to increase the performance. Furthermore, an observer-based state feedback fault-tolerant control strategy is designed, which guarantees the stability and performance of the faulty system. Moreover, the adaptive observer and the fault-tolerant controller are designed separately, whose performance can be considered, respectively. Finally, simulation results of an aircraft application are presented to illustrate the effectiveness of the proposed design methods.

Enabling time-dependent uncertain eco-weights for road networks

DEFF Research Database (Denmark)

Hu, Jilin; Yang, Bin; Jensen, Christian S.

2017-01-01

travel costs. Based on the techniques above, different histogram aggregation methods are proposed to accurately estimate time-dependent GHG emissions for routes. Based on a 200-million GPS record data set collected from 150 vehicles in Denmark over two years, a comprehensive empirical study is conducted...... transportation. The foundation of eco-routing is a weighted-graph representation of a road network in which road segments, or edges, are associated with eco-weights that capture the GHG emissions caused by traversing the edges. Due to the dynamics of traffic, the eco-weights are best modeled as being time...... dependent and uncertain. We formalize the problem of assigning a time-dependent, uncertain eco-weight to each edge in a road network based on historical GPS records. In particular, a sequence of histograms is employed to describe the uncertain eco-weight of an edge at different time intervals. Compression...

Output Feedback-Based Boundary Control of Uncertain Coupled Semilinear Parabolic PDE Using Neurodynamic Programming.

Science.gov (United States)

Talaei, Behzad; Jagannathan, Sarangapani; Singler, John

2018-04-01

In this paper, neurodynamic programming-based output feedback boundary control of distributed parameter systems governed by uncertain coupled semilinear parabolic partial differential equations (PDEs) under Neumann or Dirichlet boundary control conditions is introduced. First, Hamilton-Jacobi-Bellman (HJB) equation is formulated in the original PDE domain and the optimal control policy is derived using the value functional as the solution of the HJB equation. Subsequently, a novel observer is developed to estimate the system states given the uncertain nonlinearity in PDE dynamics and measured outputs. Consequently, the suboptimal boundary control policy is obtained by forward-in-time estimation of the value functional using a neural network (NN)-based online approximator and estimated state vector obtained from the NN observer. Novel adaptive tuning laws in continuous time are proposed for learning the value functional online to satisfy the HJB equation along system trajectories while ensuring the closed-loop stability. Local uniformly ultimate boundedness of the closed-loop system is verified by using Lyapunov theory. The performance of the proposed controller is verified via simulation on an unstable coupled diffusion reaction process.

Uncertain multiobjective redundancy allocation problem of repairable systems based on artificial bee colony algorithm

Institute of Scientific and Technical Information of China (English)

Guo Jiansheng; Wang Zutong; Zheng Mingfa; Wang Ying

2014-01-01

Based on the uncertainty theory, this paper is devoted to the redundancy allocation problem in repairable parallel-series systems with uncertain factors, where the failure rate, repair rate and other relative coefficients involved are considered as uncertain variables. The availability of the system and the corresponding designing cost are considered as two optimization objectives. A crisp multiobjective optimization formulation is presented on the basis of uncertainty theory to solve this resultant problem. For solving this problem efficiently, a new multiobjective artificial bee colony algorithm is proposed to search the Pareto efficient set, which introduces rank value and crowding distance in the greedy selection strategy, applies fast non-dominated sort procedure in the exploitation search and inserts tournament selection in the onlooker bee phase. It shows that the proposed algorithm outperforms NSGA-II greatly and can solve multiobjective redundancy allocation problem efficiently. Finally, a numerical example is provided to illustrate this approach.

Uncertain differential equations

CERN Document Server

Yao, Kai

2016-01-01

This book introduces readers to the basic concepts of and latest findings in the area of differential equations with uncertain factors. It covers the analytic method and numerical method for solving uncertain differential equations, as well as their applications in the field of finance. Furthermore, the book provides a number of new potential research directions for uncertain differential equation. It will be of interest to researchers, engineers and students in the fields of mathematics, information science, operations research, industrial engineering, computer science, artificial intelligence, automation, economics, and management science.

Ranking Queries on Uncertain Data

CERN Document Server

Hua, Ming

2011-01-01

Uncertain data is inherent in many important applications, such as environmental surveillance, market analysis, and quantitative economics research. Due to the importance of those applications and rapidly increasing amounts of uncertain data collected and accumulated, analyzing large collections of uncertain data has become an important task. Ranking queries (also known as top-k queries) are often natural and useful in analyzing uncertain data. Ranking Queries on Uncertain Data discusses the motivations/applications, challenging problems, the fundamental principles, and the evaluation algorith

Event-triggered decentralized adaptive fault-tolerant control of uncertain interconnected nonlinear systems with actuator failures.

Science.gov (United States)

Choi, Yun Ho; Yoo, Sung Jin

2018-06-01

This paper investigates the event-triggered decentralized adaptive tracking problem of a class of uncertain interconnected nonlinear systems with unexpected actuator failures. It is assumed that local control signals are transmitted to local actuators with time-varying faults whenever predefined conditions for triggering events are satisfied. Compared with the existing control-input-based event-triggering strategy for adaptive control of uncertain nonlinear systems, the aim of this paper is to propose a tracking-error-based event-triggering strategy in the decentralized adaptive fault-tolerant tracking framework. The proposed approach can relax drastic changes in control inputs caused by actuator faults in the existing triggering strategy. The stability of the proposed event-triggering control system is analyzed in the Lyapunov sense. Finally, simulation comparisons of the proposed and existing approaches are provided to show the effectiveness of the proposed theoretical result in the presence of actuator faults. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Robust Control with Enlaeged Interval of Uncertain Parameters

Directory of Open Access Journals (Sweden)

Marek Keresturi

2002-01-01

Full Text Available Robust control is advantageous for systems with defined interval of uncertain parameters. This can be substantially enlarged dividing it into a few sub-intervals. Corresponding controllers for each of them may be set after approximate identification of some uncertain plant parameters. The paper deals with application of the pole region assignment method for position control of the crane crab. The same track form is required for uncertain burden mass and approximate value of rope length. Measurement of crab position and speed is supposed, burden deviation angle is observed. Simulation results have verified feasibility of this design procedure.

Nonlinear dynamics analysis of the spur gear system for railway locomotive

Science.gov (United States)

Wang, Junguo; He, Guangyue; Zhang, Jie; Zhao, Yongxiang; Yao, Yuan

2017-02-01

Considering the factors such as the nonlinearity backlash, static transmission error and time-varying meshing stiffness, a three-degree-of-freedom torsional vibration model of spur gear transmission system for a typical locomotive is developed, in which the wheel/rail adhesion torque is considered as uncertain but bounded parameter. Meantime, the Ishikawa method is used for analysis and calculation of the time-varying mesh stiffness of the gear pair in meshing process. With the help of bifurcation diagrams, phase plane diagrams, Poincaré maps, time domain response diagrams and amplitude-frequency spectrums, the effects of the pinion speed and stiffness on the dynamic behavior of gear transmission system for locomotive are investigated in detail by using the numerical integration method. Numerical examples reveal various types of nonlinear phenomena and dynamic evolution mechanism involving one-period responses, multi-periodic responses, bifurcation and chaotic responses. Some research results present useful information to dynamic design and vibration control of the gear transmission system for railway locomotive.

Hypercube algorithms suitable for image understanding in uncertain environments

International Nuclear Information System (INIS)

Huntsberger, T.L.; Sengupta, A.

1988-01-01

Computer vision in a dynamic environment needs to be fast and able to tolerate incomplete or uncertain intermediate results. An appropriately chose representation coupled with a parallel architecture addresses both concerns. The wide range of numerical and symbolic processing needed for robust computer vision can only be achieved through a blend of SIMD and MIMD processing techniques. The 1024 element hypercube architecture has these capabilities, and was chosen as the test-bed hardware for development of highly parallel computer vision algorithms. This paper presents and analyzes parallel algorithms for color image segmentation and edge detection. These algorithms are part of a recently developed computer vision system which uses multiple valued logic to represent uncertainty in the imaging process and in intermediate results. Algorithms for the extraction of three dimensional properties of objects using dynamic scene analysis techniques within the same framework are examined. Results from experimental studies using a 1024 element hypercube implementation of the algorithm as applied to a series of natural scenes are reported

A New Fast Nonsingular Terminal Sliding Mode Control for a Class of Second-Order Uncertain Systems

Directory of Open Access Journals (Sweden)

Linjie Xin

2016-01-01

Full Text Available This paper considers the robust and adaptive nonsingular terminal sliding mode (NTSM control for a class of second-order uncertain systems. First, a new fast NTSM was proposed which had global fast convergence rate in the sliding phase. Then, a new form of robust NTSM controller was designed to handle a wider class of second-order uncertain systems. Moreover, an exponential-decline switching gain was introduced for chattering suppression. After that, a double sliding surfaces control scheme was constructed to combine the NTSM control with the adaptive technique. The benefit is that a strict demonstration can be given for the stagnation problem in the stability analysis of NTSM. Finally, a case study for tracking control of a variable-length pendulum was performed to verify the proposed controllers.

Robust Control Design for Uncertain Nonlinear Dynamic Systems

Science.gov (United States)

Kenny, Sean P.; Crespo, Luis G.; Andrews, Lindsey; Giesy, Daniel P.

2012-01-01

Robustness to parametric uncertainty is fundamental to successful control system design and as such it has been at the core of many design methods developed over the decades. Despite its prominence, most of the work on robust control design has focused on linear models and uncertainties that are non-probabilistic in nature. Recently, researchers have acknowledged this disparity and have been developing theory to address a broader class of uncertainties. This paper presents an experimental application of robust control design for a hybrid class of probabilistic and non-probabilistic parametric uncertainties. The experimental apparatus is based upon the classic inverted pendulum on a cart. The physical uncertainty is realized by a known additional lumped mass at an unknown location on the pendulum. This unknown location has the effect of substantially altering the nominal frequency and controllability of the nonlinear system, and in the limit has the capability to make the system neutrally stable and uncontrollable. Another uncertainty to be considered is a direct current motor parameter. The control design objective is to design a controller that satisfies stability, tracking error, control power, and transient behavior requirements for the largest range of parametric uncertainties. This paper presents an overview of the theory behind the robust control design methodology and the experimental results.

Uncertain programming models for portfolio selection with uncertain returns

Science.gov (United States)

Zhang, Bo; Peng, Jin; Li, Shengguo

2015-10-01

In an indeterminacy economic environment, experts' knowledge about the returns of securities consists of much uncertainty instead of randomness. This paper discusses portfolio selection problem in uncertain environment in which security returns cannot be well reflected by historical data, but can be evaluated by the experts. In the paper, returns of securities are assumed to be given by uncertain variables. According to various decision criteria, the portfolio selection problem in uncertain environment is formulated as expected-variance-chance model and chance-expected-variance model by using the uncertainty programming. Within the framework of uncertainty theory, for the convenience of solving the models, some crisp equivalents are discussed under different conditions. In addition, a hybrid intelligent algorithm is designed in the paper to provide a general method for solving the new models in general cases. At last, two numerical examples are provided to show the performance and applications of the models and algorithm.

Dynamic Analysis and Adaptive Sliding Mode Controller for a Chaotic Fractional Incommensurate Order Financial System

Science.gov (United States)

Hajipour, Ahmad; Tavakoli, Hamidreza

2017-12-01

In this study, the dynamic behavior and chaos control of a chaotic fractional incommensurate-order financial system are investigated. Using well-known tools of nonlinear theory, i.e. Lyapunov exponents, phase diagrams and bifurcation diagrams, we observe some interesting phenomena, e.g. antimonotonicity, crisis phenomena and route to chaos through a period doubling sequence. Adopting largest Lyapunov exponent criteria, we find that the system yields chaos at the lowest order of 2.15. Next, in order to globally stabilize the chaotic fractional incommensurate order financial system with uncertain dynamics, an adaptive fractional sliding mode controller is designed. Numerical simulations are used to demonstrate the effectiveness of the proposed control method.

Optimisation-based worst-case analysis and anti-windup synthesis for uncertain nonlinear systems

Science.gov (United States)

Menon, Prathyush Purushothama

This thesis describes the development and application of optimisation-based methods for worst-case analysis and anti-windup synthesis for uncertain nonlinear systems. The worst-case analysis methods developed in the thesis are applied to the problem of nonlinear flight control law clearance for highly augmented aircraft. Local, global and hybrid optimisation algorithms are employed to evaluate worst-case violations of a nonlinear response clearance criterion, for a highly realistic aircraft simulation model and flight control law. The reliability and computational overheads associated with different opti misation algorithms are compared, and the capability of optimisation-based approaches to clear flight control laws over continuous regions of the flight envelope is demonstrated. An optimisation-based method for computing worst-case pilot inputs is also developed, and compared with current industrial approaches for this problem. The importance of explicitly considering uncertainty in aircraft parameters when computing worst-case pilot demands is clearly demonstrated. Preliminary results on extending the proposed framework to the problems of limit-cycle analysis and robustness analysis in the pres ence of time-varying uncertainties are also included. A new method for the design of anti-windup compensators for nonlinear constrained systems controlled using nonlinear dynamics inversion control schemes is presented and successfully applied to some simple examples. An algorithm based on the use of global optimisation is proposed to design the anti-windup compensator. Some conclusions are drawn from the results of the research presented in the thesis, and directions for future work are identified.

Multilateral Telecoordinated Control of Multiple Robots With Uncertain Kinematics.

Science.gov (United States)

Zhai, Di-Hua; Xia, Yuanqing

2017-06-06

This paper addresses the telecoordinated control of multiple robots in the simultaneous presence of asymmetric time-varying delays, nonpassive external forces, and uncertain kinematics/dynamics. To achieve the control objective, a neuroadaptive controller with utilizing prescribed performance control and switching control technique is developed, where the basic idea is to employ the concept of motion synchronization in each pair of master-slave robots and among all slave robots. By using the multiple Lyapunov-Krasovskii functionals method, the state-independent input-to-output practical stability of the closed-loop system is established. Compared with the previous approaches, the new design is straightforward and easier to implement and is applicable to a wider area. Simulation results on three pairs of three degrees-of-freedom robots confirm the theoretical findings.

Chaos control and duration time of a class of uncertain chaotic systems

International Nuclear Information System (INIS)

Bowong, Samuel; Moukam Kakmeni, F.M.

2003-01-01

This Letter presents a robust control scheme for a class of uncertain chaotic systems in the canonical form, with unknown nonlinearities. To cope with the uncertainties, we combine Lyapunov methodology with observer design. The proposed strategy comprises an exponential linearizing feedback and an uncertainty estimator. The developed control scheme allows chaos suppression. The advantage of this method over the existing results is that the control time is explicitly computed. Simulations studies are conducted to verify the effectiveness of the scheme

Analytic Model Predictive Control of Uncertain Nonlinear Systems: A Fuzzy Adaptive Approach

Directory of Open Access Journals (Sweden)

Xiuyan Peng

2015-01-01

Full Text Available A fuzzy adaptive analytic model predictive control method is proposed in this paper for a class of uncertain nonlinear systems. Specifically, invoking the standard results from the Moore-Penrose inverse of matrix, the unmatched problem which exists commonly in input and output dimensions of systems is firstly solved. Then, recurring to analytic model predictive control law, combined with fuzzy adaptive approach, the fuzzy adaptive predictive controller synthesis for the underlying systems is developed. To further reduce the impact of fuzzy approximation error on the system and improve the robustness of the system, the robust compensation term is introduced. It is shown that by applying the fuzzy adaptive analytic model predictive controller the rudder roll stabilization system is ultimately uniformly bounded stabilized in the H-infinity sense. Finally, simulation results demonstrate the effectiveness of the proposed method.

Optimization of a dynamic uncertain causality graph for fault diagnosis in nuclear power plant

Institute of Scientific and Technical Information of China (English)

Yue Zhao; Francesco Di Maio; Enrico Zio; Qin Zhang; Chun-Ling Dong; Jin-Ying Zhang

2017-01-01

Fault diagnostics is important for safe operation of nuclear power plants (NPPs).In recent years,data-driven approaches have been proposed and implemented to tackle the problem,e.g.,neural networks,fuzzy and neurofuzzy approaches,support vector machine,K-nearest neighbor classifiers and inference methodologies.Among these methods,dynamic uncertain causality graph (DUCG)has been proved effective in many practical cases.However,the causal graph construction behind the DUCG is complicate and,in many cases,results redundant on the symptoms needed to correctly classify the fault.In this paper,we propose a method to simplify causal graph construction in an automatic way.The method consists in transforming the expert knowledge-based DCUG into a fuzzy decision tree (FDT) by extracting from the DUCG a fuzzy rule base that resumes the used symptoms at the basis of the FDT.Genetic algorithm (GA) is,then,used for the optimization of the FDT,by performing a wrapper search around the FDT:the set of symptoms selected during the iterative search are taken as the best set of symptoms for the diagnosis of the faults that can occur in the system.The effectiveness of the approach is shown with respect to a DUCG model initially built to diagnose 23 faults originally using 262 symptoms of Unit-1 in the Ningde NPP of the China Guangdong Nuclear Power Corporation.The results show that the FDT,with GA-optimized symptoms and diagnosis strategy,can drive the construction of DUCG and lower the computational burden without loss of accuracy in diagnosis.

Optimization of a dynamic uncertain causality graph for fault diagnosis in nuclear power plant

Institute of Scientific and Technical Information of China (English)

Yue Zhao; Francesco Di Maio; Enrico Zio; Qin Zhang; Chun-Ling Dong; Jin-Ying Zhang

2017-01-01

Fault diagnostics is important for safe operation of nuclear power plants (NPPs).In recent years,data-driven approaches have been proposed and implemented to tackle the problem,e.g.,neural networks,fuzzy and neurofuzzy approaches,support vector machine,K-nearest neighbor classifiers and inference methodologies.Among these methods,dynamic uncertain causality graph (DUCG) has been proved effective in many practical cases.However,the causal graph construction behind the DUCG is complicate and,in many cases,results redundant on the symptoms needed to correctly classify the fault.In this paper,we propose a method to simplify causal graph construction in an automatic way.The method consists in transforming the expert knowledge-based DCUG into a fuzzy decision tree (FDT) by extracting from the DUCG a fuzzy rule base that resumes the used symptoms at the basis of the FDT.Genetic algorithm (GA) is,then,used for the optimization of the FDT,by performing a wrapper search around the FDT:the set of symptoms selected during the iterative search are taken as the best set of symptoms for the diagnosis of the faults that can occur in the system.The effectiveness of the approach is shown with respect to a DUCG model initially built to diagnose 23 faults originally using 262 symptoms of Unit-1 in the Ningde NPP of the China Guangdong Nuclear Power Corporation.The results show that the FDT,with GA-optimized symptoms and diagnosis strategy,can drive the construction of DUCG and lower the computational burden without loss of accuracy in diagnosis.

Randomized Algorithms for Analysis and Control of Uncertain Systems With Applications

CERN Document Server

Tempo, Roberto; Dabbene, Fabrizio

2013-01-01

The presence of uncertainty in a system description has always been a critical issue in control. The main objective of Randomized Algorithms for Analysis and Control of Uncertain Systems, with Applications (Second Edition) is to introduce the reader to the fundamentals of probabilistic methods in the analysis and design of systems subject to deterministic and stochastic uncertainty. The approach propounded by this text guarantees a reduction in the computational complexity of classical  control algorithms and in the conservativeness of standard robust control techniques. The second edition has been thoroughly updated to reflect recent research and new applications with chapters on statistical learning theory, sequential methods for control and the scenario approach being completely rewritten.   Features: ·         self-contained treatment explaining Monte Carlo and Las Vegas randomized algorithms from their genesis in the principles of probability theory to their use for system analysis; ·    ...

Nonsingular Terminal Sliding Mode Control of Uncertain Second-Order Nonlinear Systems

Directory of Open Access Journals (Sweden)

Minh-Duc Tran

2015-01-01

Full Text Available This paper presents a high-performance nonsingular terminal sliding mode control method for uncertain second-order nonlinear systems. First, a nonsingular terminal sliding mode surface is introduced to eliminate the singularity problem that exists in conventional terminal sliding mode control. By using this method, the system not only can guarantee that the tracking errors reach the reference value in a finite time with high-precision tracking performance but also can overcome the complex-value and the restrictions of the exponent (the exponent should be fractional number with an odd numerator and an odd denominator in traditional terminal sliding mode. Then, in order to eliminate the chattering phenomenon, a super-twisting higher-order nonsingular terminal sliding mode control method is proposed. The stability of the closed-loop system is established using the Lyapunov theory. Finally, simulation results are presented to illustrate the effectiveness of the proposed method.

Design Sliding Mode Controller of with Parallel Fuzzy Inference System Compensator to Control of Robot Manipulator

Directory of Open Access Journals (Sweden)

Farzin Piltan

2013-06-01

Full Text Available Sliding mode controller (SMC is a significant nonlinear controller under condition of partly uncertain dynamic parameters of system. This controller is used to control of highly nonlinear systems especially for robot manipulators, because this controller is a robust and stable. Conversely, pure sliding mode controller is used in many applications; it has two important drawbacks namely; chattering phenomenon, and nonlinear equivalent dynamic formulation in uncertain dynamic parameter. The nonlinear equivalent dynamic formulation problem and chattering phenomenon in uncertain system can be solved by using artificial intelligence theorem. However fuzzy logic controller is used to control complicated nonlinear dynamic systems, but it cannot guarantee stability and robustness.  In this research parallel fuzzy logic theory is used to compensate the system dynamic uncertainty.

Stabilization of constrained uncertain systems by an off-line approach using zonotopes

Directory of Open Access Journals (Sweden)

Walid Hamdi

2018-01-01

Full Text Available In this paper, stabilization of uncertain systems was established using zonotopic sets. The obtained state feedback control laws are computed by an off-line approach reducing computational burdens. The resolution of a robust model predictive control (MPC allows computing a sequence of state feedback control laws corresponding to a sequence of zonotopic invariant sets. The implemented control laws are then calculated by linear interpolation between the state feedback gains corresponding to the nested pre-computed zonotopic sets. The proposed interpolation with the use of zonotopic sets achieves better control performances.

Adaptive controller design for modified projective synchronization of Genesio-Tesi chaotic system with uncertain parameters

International Nuclear Information System (INIS)

Park, Ju H.

2007-01-01

The paper addresses control problem for the modified projective synchronization of the Genesio-Tesi chaotic systems with three uncertain parameters. An adaptive control law is derived to make the states of two identical Genesio-Tesi systems asymptotically synchronized up to specific ratios. The stability analysis in the paper is proved using a well-known Lyapunov stability theory. A numerical simulation is presented to show the effectiveness of the proposed chaos synchronization scheme

Processing Uncertain RFID Data in Traceability Supply Chains

Directory of Open Access Journals (Sweden)

Dong Xie

2014-01-01

Full Text Available Radio Frequency Identification (RFID is widely used to track and trace objects in traceability supply chains. However, massive uncertain data produced by RFID readers are not effective and efficient to be used in RFID application systems. Following the analysis of key features of RFID objects, this paper proposes a new framework for effectively and efficiently processing uncertain RFID data, and supporting a variety of queries for tracking and tracing RFID objects. We adjust different smoothing windows according to different rates of uncertain data, employ different strategies to process uncertain readings, and distinguish ghost, missing, and incomplete data according to their apparent positions. We propose a comprehensive data model which is suitable for different application scenarios. In addition, a path coding scheme is proposed to significantly compress massive data by aggregating the path sequence, the position, and the time intervals. The scheme is suitable for cyclic or long paths. Moreover, we further propose a processing algorithm for group and independent objects. Experimental evaluations show that our approach is effective and efficient in terms of the compression and traceability queries.

Processing uncertain RFID data in traceability supply chains.

Science.gov (United States)

Xie, Dong; Xiao, Jie; Guo, Guangjun; Jiang, Tong

2014-01-01

Radio Frequency Identification (RFID) is widely used to track and trace objects in traceability supply chains. However, massive uncertain data produced by RFID readers are not effective and efficient to be used in RFID application systems. Following the analysis of key features of RFID objects, this paper proposes a new framework for effectively and efficiently processing uncertain RFID data, and supporting a variety of queries for tracking and tracing RFID objects. We adjust different smoothing windows according to different rates of uncertain data, employ different strategies to process uncertain readings, and distinguish ghost, missing, and incomplete data according to their apparent positions. We propose a comprehensive data model which is suitable for different application scenarios. In addition, a path coding scheme is proposed to significantly compress massive data by aggregating the path sequence, the position, and the time intervals. The scheme is suitable for cyclic or long paths. Moreover, we further propose a processing algorithm for group and independent objects. Experimental evaluations show that our approach is effective and efficient in terms of the compression and traceability queries.

A framework for the analysis of vibrations of structures with uncertain attachments

International Nuclear Information System (INIS)

Li, Shuo; Mace, Brian R.; Halkyard, Roger; Ferguson, Neil S.

2016-01-01

Attachments affect the dynamic response of an assembled structure. When engineers are modelling structures, small attachments will often not be included in the “bare” model, especially in the initial design stages. The location of these attachments might be poorly known, yet they affect the response of the structure. This paper considers how attachments jointed to the structure at uncertain points, can be included in the dynamic model of a structure. Two approaches are proposed. In the time domain, a combination of component mode synthesis, characteristic constraint modes and modal analysis gives a computationally efficient basis for subsequent analysis using, for example, Monte Carlo simulation. The frequency domain approach is based on assembly of frequency response functions of bare structure and attachment. Numerical examples of a beam and a plate with a point mass added at an uncertain location are considered and predictions compared with experiment results. (paper)

Forest Conservation in Costa Rica: when nonuse benefits are uncertain but rising

NARCIS (Netherlands)

Bulte, E.H.; Soest, van D.P.; Kooten, van G.C.; Schipper, R.A.

2002-01-01

Stochastic dynamic programming is used to investigate optimal holding of primary tropical forest in humid Costa Rica when future nonuse benefits of forest conservation are uncertain and increasing. The quasi-option value of maintaining primary forests is included as a component of investment in

Flexible Procurement of Services with Uncertain Durations using Redundancy

OpenAIRE

Stein, S; Gerding, E; Rogers, A; Larson, K; Jennings, NR

2009-01-01

Emerging service-oriented technologies allow software agents to automatically procure distributed services to complete complex tasks. However, in many application scenarios, service providers demand financial remuneration, execution times are uncertain and consumers have deadlines for their tasks. In this paper, we address these issues by developing a novel approach that dynamically procures multiple, redundant services over time, in order to ensure success by the deadline. Specifically, we f...

Uncertain multi-attribute decision making methods and applications

CERN Document Server

Xu, Zeshui

2015-01-01

This book introduces methods for uncertain multi-attribute decision making including uncertain multi-attribute group decision making and their applications to supply chain management, investment decision making, personnel assessment, redesigning products, maintenance services, military system efficiency evaluation. Multi-attribute decision making, also known as multi-objective decision making with finite alternatives, is an important component of modern decision science. The theory and methods of multi-attribute decision making have been extensively applied in engineering, economics, management and military contexts, such as venture capital project evaluation, facility location, bidding, development ranking of industrial sectors and so on. Over the last few decades, great attention has been paid to research on multi-attribute decision making in uncertain settings, due to the increasing complexity and uncertainty of supposedly objective aspects and the fuzziness of human thought. This book can be used as a ref...

Adaptive robust PID controller design based on a sliding mode for uncertain chaotic systems

International Nuclear Information System (INIS)

Chang Weider; Yan Junjuh

2005-01-01

A robust adaptive PID controller design motivated from the sliding mode control is proposed for a class of uncertain chaotic systems in this paper. Three PID control gains, K p , K i , and K d , are adjustable parameters and will be updated online with an adequate adaptation mechanism to minimize a previously designed sliding condition. By introducing a supervisory controller, the stability of the closed-loop PID control system under with the plant uncertainty and external disturbance can be guaranteed. Finally, a well-known Duffing-Holmes chaotic system is used as an illustrative to show the effectiveness of the proposed robust adaptive PID controller

Sustained, Low?Intensity Exercise Achieved by a Dynamic Feeding System Decreases Body Fat in Ponies

OpenAIRE

de Laat, M.A.; Hampson, B.A.; Sillence, M.N.; Pollitt, C.C.

2016-01-01

Background Obesity in horses is increasing in prevalence and can be associated with insulin insensitivity and laminitis. Current treatment strategies for obesity include dietary restriction and exercise. However, whether exercise alone is effective for decreasing body fat is uncertain. Hypothesis Our hypothesis was that twice daily use of a dynamic feeding system for 3 months would induce sustained, low?intensity exercise thereby decreasing adiposity and improving insulin sensitivity (SI). An...

Representation and management of temporal and uncertain knowledge

International Nuclear Information System (INIS)

Chen, Ziqiang

1993-01-01

This thesis contributes to the investigation of uncertain temporal knowledge representation and management, especially for process verification and supervisor systems design. The evolution of process behaviour is time dependent and information describing this temporal evolution is uncertain/imprecise. In Artificial Intelligence, time and uncertainty have been, since long-time, considered as two of the most difficult research fields. Furthermore, these two fields, even different, may be present in an interactive way. We now try to deal with this special kind of uncertainty: temporal uncertainty. Integrating time and uncertainty brings out study issues of temporal information representation, events ordering and temporal reasoning under uncertainty. The investigation of these problems has been guided by preserving the intrinsic properties of time. The main contribution of this thesis can be summarised as follows: (1) unified representation of uncertainty and imprecision over temporal information; (2) formal structuring of time under uncertainty; (3) formalising fuzzy temporal reasoning system; (4) modelling temporal evolution of process, providing associated reasoning mechanism to verify the process evolution, modelling fuzzy temporal Petri nets; (5) design and implementation of SURTEL, a programming tool for dealing with uncertain temporal information and knowledge. (author) [fr

Multi-Period Mean-Variance Portfolio Selection with Uncertain Time Horizon When Returns Are Serially Correlated

Directory of Open Access Journals (Sweden)

Ling Zhang

2012-01-01

Full Text Available We study a multi-period mean-variance portfolio selection problem with an uncertain time horizon and serial correlations. Firstly, we embed the nonseparable multi-period optimization problem into a separable quadratic optimization problem with uncertain exit time by employing the embedding technique of Li and Ng (2000. Then we convert the later into an optimization problem with deterministic exit time. Finally, using the dynamic programming approach, we explicitly derive the optimal strategy and the efficient frontier for the dynamic mean-variance optimization problem. A numerical example with AR(1 return process is also presented, which shows that both the uncertainty of exit time and the serial correlations of returns have significant impacts on the optimal strategy and the efficient frontier.

Energy pricing under uncertain supply

International Nuclear Information System (INIS)

Serra, P.J.

1997-01-01

This paper introduces a new pricing system - based on the Chilean tariff regulations - to deal with an uncertain energy supply. It consists of a basic rate for each unit actually consumed and a compensation that the utilities pay their customers for each unit of energy that they voluntarily reduce below their normal consumption during an energy shortage. Within the framework of a model that portrays the stylized facts of the Chilean electric system, and assumes risk-neutral agents, this paper shows the equivalency of the new pricing system with both contingent pricing and priority pricing. (Author)

Project Delivery System Mode Decision Based on Uncertain AHP and Fuzzy Sets

Science.gov (United States)

Kaishan, Liu; Huimin, Li

2017-12-01

The project delivery system mode determines the contract pricing type, project management mode and the risk allocation among all participants. Different project delivery system modes have different characteristics and applicable scope. For the owners, the selection of the delivery mode is the key point to decide whether the project can achieve the expected benefits, it relates to the success or failure of project construction. Under the precondition of comprehensively considering the influence factors of the delivery mode, the model of project delivery system mode decision was set up on the basis of uncertain AHP and fuzzy sets, which can well consider the uncertainty and fuzziness when conducting the index evaluation and weight confirmation, so as to rapidly and effectively identify the most suitable delivery mode according to project characteristics. The effectiveness of the model has been verified via the actual case analysis in order to provide reference for the construction project delivery system mode.

Non-fragile observer-based output feedback control for polytopic uncertain system under distributed model predictive control approach

Science.gov (United States)

Zhu, Kaiqun; Song, Yan; Zhang, Sunjie; Zhong, Zhaozhun

2017-07-01

In this paper, a non-fragile observer-based output feedback control problem for the polytopic uncertain system under distributed model predictive control (MPC) approach is discussed. By decomposing the global system into some subsystems, the computation complexity is reduced, so it follows that the online designing time can be saved.Moreover, an observer-based output feedback control algorithm is proposed in the framework of distributed MPC to deal with the difficulties in obtaining the states measurements. In this way, the presented observer-based output-feedback MPC strategy is more flexible and applicable in practice than the traditional state-feedback one. What is more, the non-fragility of the controller has been taken into consideration in favour of increasing the robustness of the polytopic uncertain system. After that, a sufficient stability criterion is presented by using Lyapunov-like functional approach, meanwhile, the corresponding control law and the upper bound of the quadratic cost function are derived by solving an optimisation subject to convex constraints. Finally, some simulation examples are employed to show the effectiveness of the method.

Prediction-based control for LTI systems with uncertain time-varying delays and partial state knowledge

Science.gov (United States)

Léchappé, V.; Moulay, E.; Plestan, F.

2018-06-01

The stability of a prediction-based controller for linear time-invariant (LTI) systems is studied in the presence of time-varying input and output delays. The uncertain delay case is treated as well as the partial state knowledge case. The reduction method is used in order to prove the convergence of the closed-loop system including the state observer, the predictor and the plant. Explicit conditions that guarantee the closed-loop stability are given, thanks to a Lyapunov-Razumikhin analysis. Simulations illustrate the theoretical results.

Dynamic Energy Management System for a Smart Microgrid.

Science.gov (United States)

Venayagamoorthy, Ganesh Kumar; Sharma, Ratnesh K; Gautam, Prajwal K; Ahmadi, Afshin

2016-08-01

This paper presents the development of an intelligent dynamic energy management system (I-DEMS) for a smart microgrid. An evolutionary adaptive dynamic programming and reinforcement learning framework is introduced for evolving the I-DEMS online. The I-DEMS is an optimal or near-optimal DEMS capable of performing grid-connected and islanded microgrid operations. The primary sources of energy are sustainable, green, and environmentally friendly renewable energy systems (RESs), e.g., wind and solar; however, these forms of energy are uncertain and nondispatchable. Backup battery energy storage and thermal generation were used to overcome these challenges. Using the I-DEMS to schedule dispatches allowed the RESs and energy storage devices to be utilized to their maximum in order to supply the critical load at all times. Based on the microgrid's system states, the I-DEMS generates energy dispatch control signals, while a forward-looking network evaluates the dispatched control signals over time. Typical results are presented for varying generation and load profiles, and the performance of I-DEMS is compared with that of a decision tree approach-based DEMS (D-DEMS). The robust performance of the I-DEMS was illustrated by examining microgrid operations under different battery energy storage conditions.

Parametric optimal control of uncertain systems under an optimistic value criterion

Science.gov (United States)

Li, Bo; Zhu, Yuanguo

2018-01-01

It is well known that the optimal control of a linear quadratic model is characterized by the solution of a Riccati differential equation. In many cases, the corresponding Riccati differential equation cannot be solved exactly such that the optimal feedback control may be a complex time-oriented function. In this article, a parametric optimal control problem of an uncertain linear quadratic model under an optimistic value criterion is considered for simplifying the expression of optimal control. Based on the equation of optimality for the uncertain optimal control problem, an approximation method is presented to solve it. As an application, a two-spool turbofan engine optimal control problem is given to show the utility of the proposed model and the efficiency of the presented approximation method.

Robust Optimization-Based Generation Self-Scheduling under Uncertain Price

Directory of Open Access Journals (Sweden)

Xiao Luo

2011-01-01

Full Text Available This paper considers generation self-scheduling in electricity markets under uncertain price. Based on the robust optimization (denoted as RO methodology, a new self-scheduling model, which has a complicated max-min optimization structure, is set up. By using optimal dual theory, the proposed model is reformulated to an ordinary quadratic and quadratic cone programming problems in the cases of box and ellipsoidal uncertainty, respectively. IEEE 30-bus system is used to test the new model. Some comparisons with other methods are done, and the sensitivity with respect to the uncertain set is analyzed. Comparing with the existed uncertain self-scheduling approaches, the new method has twofold characteristics. First, it does not need a prediction of distribution of random variables and just requires an estimated value and the uncertain set of power price. Second, the counterpart of RO corresponding to the self-scheduling is a simple quadratic or quadratic cone programming. This indicates that the reformulated problem can be solved by many ordinary optimization algorithms.

Evaluating impact of market changes on increasing cell-load variation in dynamic cellular manufacturing systems using a hybrid Tabu search and simulated annealing algorithms

Directory of Open Access Journals (Sweden)

Aidin Delgoshaei

2016-06-01

Full Text Available In this paper, a new method is proposed for scheduling dynamic cellular manufacturing systems (D-CMS in the presence of uncertain product demands. The aim of this method is to control the process of trading off between in-house manufacturing and outsourcing while product demands are uncertain and can be varied from period to period. To solve the proposed problem, a hybrid Tabu Search and Simulated Annealing are developed to overcome hardness of the proposed model and then results are compared with a Branch and Bound and Simulated Annealing algorithms. A Taguchi method (L_27 orthogonal optimization is used to estimate parameters of the proposed method in order to solve experiments derived from literature. An in-depth analysis is conducted on the results in consideration of various factors. For evaluating the system imbalance in dynamic market demands, a new measuring index is developed. Our findings indicate that the uncertain condition of market demands affects the routing of product parts and may induce machine-load variations that yield to cell-load diversity. The results showed that the proposed hybrid method can provide solutions with better quality.

Management of Uncertain Data - towards unattended integration

NARCIS (Netherlands)

de Keijzer, Ander

2008-01-01

In recent years, the need to support uncertain data has increased. Sensor applications, for example, are dealing with the inherent uncertainty about the readings of the sensors. Current database management systems are not equipped to deal with this uncertainty, other than as a user defined

Stochastic modeling of the energy supply system with uncertain fuel price – A case of emerging technologies for distributed power generation

International Nuclear Information System (INIS)

Mirkhani, Sh.; Saboohi, Y.

2012-01-01

Highlights: ► An existing bottom-up deterministic energy system model (ESM) has limited capability in handling the uncertainties. ► Uncertainty has been modeled based on GBM. Probabilistic scenarios are generated based on Cox–Ross method. ► A multistage stochastic model has been developed where scenarios are integrated in the energy system model. ► A distributed generation system has been introduced as a case study where fuel price is considered as an uncertain parameter. - Abstract: A deterministic energy supply model with bottom-up structure has limited capability in handling the uncertainties. To enhance the applicability of such a model in an uncertain environment two main issues have been investigated in the present paper. First, a binomial lattice is generated based on the stochastic nature of the source of uncertainty. Second, an energy system model (ESM) has been reformulated as a multistage stochastic problem. The result of the application of the modified energy model encompasses all uncertain outcomes together and enables optimal timing of capacity expansion. The performance of the model has been demonstrated with the help of a case study. The case study has been formulated on the assumption that a gas fired engine competes with renewable energy technologies in an uncertain environment where the price of natural gas is volatile. The result of stochastic model has then been compared with those of a deterministic model by studying the expected value of perfect information (EVPI) and the value of stochastic solution (VSS). Finally the results of the sensitivity analysis have been discussed where the characteristics of uncertainty of the price of fuel are varied.

systems

Directory of Open Access Journals (Sweden)

Alexander Leonessa

2000-01-01

Full Text Available 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 semicontinuous Lyapunov function obtained by minimizing a potential function over a given switching set induced by the parameterized nominal system equilibria. The proposed framework robustly stabilizes a compact positively invariant set of a given nonlinear uncertain dynamical system with structured parametric uncertainty. Finally, the efficacy of the proposed approach is demonstrated on a jet engine propulsion control problem with uncertain pressure-flow map data.

Robust extended Kalman filter of discrete-time Markovian jump nonlinear system under uncertain noise

International Nuclear Information System (INIS)

Zhu, Jin; Park, Jun Hong; Lee, Kwan Soo; Spiryagin, Maksym

2008-01-01

This paper examines the problem of robust extended Kalman filter design for discrete -time Markovian jump nonlinear systems with noise uncertainty. Because of the existence of stochastic Markovian switching, the state and measurement equations of underlying system are subject to uncertain noise whose covariance matrices are time-varying or un-measurable instead of stationary. First, based on the expression of filtering performance deviation, admissible uncertainty of noise covariance matrix is given. Secondly, two forms of noise uncertainty are taken into account: Non- Structural and Structural. It is proved by applying game theory that this filter design is a robust mini-max filter. A numerical example shows the validity of the method

Simplex sliding mode control for nonlinear uncertain systems via chaos optimization

International Nuclear Information System (INIS)

Lu, Zhao; Shieh, Leang-San; Chen, Guanrong; Coleman, Norman P.

2005-01-01

As an emerging effective approach to nonlinear robust control, simplex sliding mode control demonstrates some attractive features not possessed by the conventional sliding mode control method, from both theoretical and practical points of view. However, no systematic approach is currently available for computing the simplex control vectors in nonlinear sliding mode control. In this paper, chaos-based optimization is exploited so as to develop a systematic approach to seeking the simplex control vectors; particularly, the flexibility of simplex control is enhanced by making the simplex control vectors dependent on the Euclidean norm of the sliding vector rather than being constant, which result in both reduction of the chattering and speedup of the convergence. Computer simulation on a nonlinear uncertain system is given to illustrate the effectiveness of the proposed control method

Approaches to Learning to Control Dynamic Uncertainty

Directory of Open Access Journals (Sweden)

Magda Osman

2015-10-01

Full Text Available In dynamic environments, when faced with a choice of which learning strategy to adopt, do people choose to mostly explore (maximizing their long term gains or exploit (maximizing their short term gains? More to the point, how does this choice of learning strategy influence one’s later ability to control the environment? In the present study, we explore whether people’s self-reported learning strategies and levels of arousal (i.e., surprise, stress correspond to performance measures of controlling a Highly Uncertain or Moderately Uncertain dynamic environment. Generally, self-reports suggest a preference for exploring the environment to begin with. After which, those in the Highly Uncertain environment generally indicated they exploited more than those in the Moderately Uncertain environment; this difference did not impact on performance on later tests of people’s ability to control the dynamic environment. Levels of arousal were also differentially associated with the uncertainty of the environment. Going beyond behavioral data, our model of dynamic decision-making revealed that, in actual fact, there was no difference in exploitation levels between those in the highly uncertain or moderately uncertain environments, but there were differences based on sensitivity to negative reinforcement. We consider the implications of our findings with respect to learning and strategic approaches to controlling dynamic uncertainty.

Model Predictive Control of Wind Turbines using Uncertain LIDAR Measurements

DEFF Research Database (Denmark)

Mirzaei, Mahmood; Soltani, Mohsen; Poulsen, Niels Kjølstad

2013-01-01

, we simplify state prediction for the MPC. Consequently, the control problem of the nonlinear system is simplified into a quadratic programming. We consider uncertainty in the wind propagation time, which is the traveling time of wind from the LIDAR measurement point to the rotor. An algorithm based......The problem of Model predictive control (MPC) of wind turbines using uncertain LIDAR (LIght Detection And Ranging) measurements is considered. A nonlinear dynamical model of the wind turbine is obtained. We linearize the obtained nonlinear model for different operating points, which are determined...... on wind speed estimation and measurements from the LIDAR is devised to find an estimate of the delay and compensate for it before it is used in the controller. Comparisons between the MPC with error compensation, the MPC without error compensation and an MPC with re-linearization at each sample point...

Generalization from uncertain and imprecise data

Energy Technology Data Exchange (ETDEWEB)

Bouchon-Meunier, B.; Marsala, C.; Rifqi, M. [Universite P. et M. Curie, Paris (France); Ramdani, M. [Universite P. et M. Curie, Paris (France)]|[Faculte des Sciences et Techniques, Mohammadia (Morocco)

1996-12-31

Most of the knowledge available about a given system is imperfect, which means imprecise, uncertain, qualitative, expressed in natural language with words which are generally vague. Some pieces of knowledge are numerical, obtained by means of measurements with more or less precise devices. They can also be incomplete, with unknown values for some elements of the system. Classification of objects, decision-making according to the description of the system, are well known problems which can be approached by various ways. Methods based on a generalization process appear very efficient when a list of already solved cases is available and sufficiently representative of all the possible cases. In this paper, we focus on the case where fuzzy sets are used to represent imperfect knowledge because of the capability of fuzzy sets to help managing imprecise data, possibly submitted to some non probabilistic uncertainty such as a subjective doubt. Fuzzy sets also present the interesting property to establish an interface between numerical and symbolic data and are interesting to use when both types of data are present. We suppose that the objects of the system are described by means of attributes, the value of which can be imprecise, uncertain or undetermined. Our purpose is to find rules enabling us to attach a class to any object of the system. We focus this study on two generalization methods based on the knowledge of a training set of objects associated with their descriptions and their classes.

Alignment Condition-Based Robust Adaptive Iterative Learning Control of Uncertain Robot System

Directory of Open Access Journals (Sweden)

Guofeng Tong

2014-04-01

Full Text Available This paper proposes an adaptive iterative learning control strategy integrated with saturation-based robust control for uncertain robot system in presence of modelling uncertainties, unknown parameter, and external disturbance under alignment condition. An important merit is that it achieves adaptive switching of gain matrix both in conventional PD-type feedforward control and robust adaptive control in the iteration domain simultaneously. The analysis of convergence of proposed control law is based on Lyapunov's direct method under alignment initial condition. Simulation results demonstrate the faster learning rate and better robust performance with proposed algorithm by comparing with other existing robust controllers. The actual experiment on three-DOF robot manipulator shows its better practical effectiveness.

Smooth Adaptive Internal Model Control Based on U Model for Nonlinear Systems with Dynamic Uncertainties

Directory of Open Access Journals (Sweden)

Li Zhao

2016-01-01

Full Text Available An improved smooth adaptive internal model control based on U model control method is presented to simplify modeling structure and parameter identification for a class of uncertain dynamic systems with unknown model parameters and bounded external disturbances. Differing from traditional adaptive methods, the proposed controller can simplify the identification of time-varying parameters in presence of bounded external disturbances. Combining the small gain theorem and the virtual equivalent system theory, learning rate of smooth adaptive internal model controller has been analyzed and the closed-loop virtual equivalent system based on discrete U model has been constructed as well. The convergence of this virtual equivalent system is proved, which further shows the convergence of the complex closed-loop discrete U model system. Finally, simulation and experimental results on a typical nonlinear dynamic system verified the feasibility of the proposed algorithm. The proposed method is shown to have lighter identification burden and higher control accuracy than the traditional adaptive controller.

Data-Driven Optimization of Incentive-based Demand Response System with Uncertain Responses of Customers

Directory of Open Access Journals (Sweden)

Jimyung Kang

2017-10-01

Full Text Available Demand response is nowadays considered as another type of generator, beyond just a simple peak reduction mechanism. A demand response service provider (DRSP can, through its subcontracts with many energy customers, virtually generate electricity with actual load reduction. However, in this type of virtual generator, the amount of load reduction includes inevitable uncertainty, because it consists of a very large number of independent energy customers. While they may reduce energy today, they might not tomorrow. In this circumstance, a DSRP must choose a proper set of these uncertain customers to achieve the exact preferred amount of load curtailment. In this paper, the customer selection problem for a service provider that consists of uncertain responses of customers is defined and solved. The uncertainty of energy reduction is fully considered in the formulation with data-driven probability distribution modeling and stochastic programming technique. The proposed optimization method that utilizes only the observed load data provides a realistic and applicable solution to a demand response system. The performance of the proposed optimization is verified with real demand response event data in Korea, and the results show increased and stabilized performance from the service provider’s perspective.

System dynamics

International Nuclear Information System (INIS)

Kim, Do Hun; Mun, Tae Hun; Kim, Dong Hwan

1999-02-01

This book introduces systems thinking and conceptual tool and modeling tool of dynamics system such as tragedy of single thinking, accessible way of system dynamics, feedback structure and causal loop diagram analysis, basic of system dynamics modeling, causal loop diagram and system dynamics modeling, information delay modeling, discovery and application for policy, modeling of crisis of agricultural and stock breeding products, dynamic model and lesson in ecosystem, development and decadence of cites and innovation of education forward system thinking.

Simultaneous Robust Fault and State Estimation for Linear Discrete-Time Uncertain Systems

Directory of Open Access Journals (Sweden)

Feten Gannouni

2017-01-01

Full Text Available We consider the problem of robust simultaneous fault and state estimation for linear uncertain discrete-time systems with unknown faults which affect both the state and the observation matrices. Using transformation of the original system, a new robust proportional integral filter (RPIF having an error variance with an optimized guaranteed upper bound for any allowed uncertainty is proposed to improve robust estimation of unknown time-varying faults and to improve robustness against uncertainties. In this study, the minimization problem of the upper bound of the estimation error variance is formulated as a convex optimization problem subject to linear matrix inequalities (LMI for all admissible uncertainties. The proportional and the integral gains are optimally chosen by solving the convex optimization problem. Simulation results are given in order to illustrate the performance of the proposed filter, in particular to solve the problem of joint fault and state estimation.

Time-optimal path planning in uncertain flow fields using ensemble method

KAUST Repository

Wang, Tong

2016-01-06

An ensemble-based approach is developed to conduct time-optimal path planning in unsteady ocean currents under uncertainty. We focus our attention on two-dimensional steady and unsteady uncertain flows, and adopt a sampling methodology that is well suited to operational forecasts, where a set deterministic predictions is used to model and quantify uncertainty in the predictions. In the operational setting, much about dynamics, topography and forcing of the ocean environment is uncertain, and as a result a single path produced by a model simulation has limited utility. To overcome this limitation, we rely on a finitesize ensemble of deterministic forecasts to quantify the impact of variability in the dynamics. The uncertainty of flow field is parametrized using a finite number of independent canonical random variables with known densities, and the ensemble is generated by sampling these variables. For each the resulting realizations of the uncertain current field, we predict the optimal path by solving a boundary value problem (BVP), based on the Pontryagin maximum principle. A family of backward-in-time trajectories starting at the end position is used to generate suitable initial values for the BVP solver. This allows us to examine and analyze the performance of sampling strategy, and develop insight into extensions dealing with regional or general circulation models. In particular, the ensemble method enables us to perform a statistical analysis of travel times, and consequently develop a path planning approach that accounts for these statistics. The proposed methodology is tested for a number of scenarios. We first validate our algorithms by reproducing simple canonical solutions, and then demonstrate our approach in more complex flow fields, including idealized, steady and unsteady double-gyre flows.

Hard and soft sub-time-optimal controllers for a mechanical system with uncertain mass

DEFF Research Database (Denmark)

Kulczycki, P.; Wisniewski, Rafal; Kowalski, P.

2004-01-01

An essential limitation in using the classical optimal control has been its limited robustness to modeling inadequacies and perturbations. This paper presents conceptions of two practical control structures based on the time-optimal approach: hard and soft ones. The hard structure is defined...... by parameters selected in accordance with the rules of the statistical decision theory; however, the soft structure allows additionally to eliminate rapid changes in control values. The object is a basic mechanical system, with uncertain (also non-stationary) mass treated as a stochastic process....... The methodology proposed here is of a universal nature and may easily be applied with respect to other elements of uncertainty of time-optimal controlled mechanical systems....

Hard and soft Sub-Time-Optimal Controllers for a Mechanical System with Uncertain Mass

DEFF Research Database (Denmark)

Kulczycki, P.; Wisniewski, Rafal; Kowalski, P.

2005-01-01

An essential limitation in using the classical optimal control has been its limited robustness to modeling inadequacies and perturbations. This paper presents conceptions of two practical control structures based on the time-optimal approach: hard and soft ones. The hard structure is defined...... by parameters selected in accordance with the rules of the statistical decision theory; however, the soft structure allows additionally to eliminate rapid changes in control values. The object is a basic mechanical system, with uncertain (also non-stationary) mass treated as a stochastic process....... The methodology proposed here is of a universal nature and may easily be applied with respect to other elements of uncertainty of time-optimal controlled mechanical systems....

Uncertain data envelopment analysis

CERN Document Server

Wen, Meilin

2014-01-01

This book is intended to present the milestones in the progression of uncertain Data envelopment analysis (DEA). Chapter 1 gives some basic introduction to uncertain theories, including probability theory, credibility theory, uncertainty theory and chance theory. Chapter 2 presents a comprehensive review and discussion of basic DEA models. The stochastic DEA is introduced in Chapter 3, in which the inputs and outputs are assumed to be random variables. To obtain the probability distribution of a random variable, a lot of samples are needed to apply the statistics inference approach. Chapter 4

Path planning in uncertain flow fields using ensemble method

KAUST Repository

Wang, Tong

2016-08-20

An ensemble-based approach is developed to conduct optimal path planning in unsteady ocean currents under uncertainty. We focus our attention on two-dimensional steady and unsteady uncertain flows, and adopt a sampling methodology that is well suited to operational forecasts, where an ensemble of deterministic predictions is used to model and quantify uncertainty. In an operational setting, much about dynamics, topography, and forcing of the ocean environment is uncertain. To address this uncertainty, the flow field is parametrized using a finite number of independent canonical random variables with known densities, and the ensemble is generated by sampling these variables. For each of the resulting realizations of the uncertain current field, we predict the path that minimizes the travel time by solving a boundary value problem (BVP), based on the Pontryagin maximum principle. A family of backward-in-time trajectories starting at the end position is used to generate suitable initial values for the BVP solver. This allows us to examine and analyze the performance of the sampling strategy and to develop insight into extensions dealing with general circulation ocean models. In particular, the ensemble method enables us to perform a statistical analysis of travel times and consequently develop a path planning approach that accounts for these statistics. The proposed methodology is tested for a number of scenarios. We first validate our algorithms by reproducing simple canonical solutions, and then demonstrate our approach in more complex flow fields, including idealized, steady and unsteady double-gyre flows.

Finite-Time H∞ Filtering for Linear Continuous Time-Varying Systems with Uncertain Observations

Directory of Open Access Journals (Sweden)

Huihong Zhao

2012-01-01

Full Text Available This paper is concerned with the finite-time H∞ filtering problem for linear continuous time-varying systems with uncertain observations and ℒ2-norm bounded noise. The design of finite-time H∞ filter is equivalent to the problem that a certain indefinite quadratic form has a minimum and the filter is such that the minimum is positive. The quadratic form is related to a Krein state-space model according to the Krein space linear estimation theory. By using the projection theory in Krein space, the finite-time H∞ filtering problem is solved. A numerical example is given to illustrate the performance of the H∞ filter.

Interval type-2 fuzzy PID controller for uncertain nonlinear inverted pendulum system.

Science.gov (United States)

El-Bardini, Mohammad; El-Nagar, Ahmad M

2014-05-01

In this paper, the interval type-2 fuzzy proportional-integral-derivative controller (IT2F-PID) is proposed for controlling an inverted pendulum on a cart system with an uncertain model. The proposed controller is designed using a new method of type-reduction that we have proposed, which is called the simplified type-reduction method. The proposed IT2F-PID controller is able to handle the effect of structure uncertainties due to the structure of the interval type-2 fuzzy logic system (IT2-FLS). The results of the proposed IT2F-PID controller using a new method of type-reduction are compared with the other proposed IT2F-PID controller using the uncertainty bound method and the type-1 fuzzy PID controller (T1F-PID). The simulation and practical results show that the performance of the proposed controller is significantly improved compared with the T1F-PID controller. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Visualization of Uncertain Contour Trees

DEFF Research Database (Denmark)

Kraus, Martin

2010-01-01

Contour trees can represent the topology of large volume data sets in a relatively compact, discrete data structure. However, the resulting trees often contain many thousands of nodes; thus, many graph drawing techniques fail to produce satisfactory results. Therefore, several visualization methods...... were proposed recently for the visualization of contour trees. Unfortunately, none of these techniques is able to handle uncertain contour trees although any uncertainty of the volume data inevitably results in partially uncertain contour trees. In this work, we visualize uncertain contour trees...... by combining the contour trees of two morphologically filtered versions of a volume data set, which represent the range of uncertainty. These two contour trees are combined and visualized within a single image such that a range of potential contour trees is represented by the resulting visualization. Thus...

Robust H∞ Filtering for Uncertain Neutral Stochastic Systems with Markovian Jumping Parameters and Time Delay

Directory of Open Access Journals (Sweden)

Yajun Li

2015-01-01

Full Text Available This paper deals with the robust H∞ filter design problem for a class of uncertain neutral stochastic systems with Markovian jumping parameters and time delay. Based on the Lyapunov-Krasovskii theory and generalized Finsler Lemma, a delay-dependent stability condition is proposed to ensure not only that the filter error system is robustly stochastically stable but also that a prescribed H∞ performance level is satisfied for all admissible uncertainties. All obtained results are expressed in terms of linear matrix inequalities which can be easily solved by MATLAB LMI toolbox. Numerical examples are given to show that the results obtained are both less conservative and less complicated in computation.

The Cramér-Rao Bounds and Sensor Selection for Nonlinear Systems with Uncertain Observations.

Science.gov (United States)

Wang, Zhiguo; Shen, Xiaojing; Wang, Ping; Zhu, Yunmin

2018-04-05

This paper considers the problems of the posterior Cramér-Rao bound and sensor selection for multi-sensor nonlinear systems with uncertain observations. In order to effectively overcome the difficulties caused by uncertainty, we investigate two methods to derive the posterior Cramér-Rao bound. The first method is based on the recursive formula of the Cramér-Rao bound and the Gaussian mixture model. Nevertheless, it needs to compute a complex integral based on the joint probability density function of the sensor measurements and the target state. The computation burden of this method is relatively high, especially in large sensor networks. Inspired by the idea of the expectation maximization algorithm, the second method is to introduce some 0-1 latent variables to deal with the Gaussian mixture model. Since the regular condition of the posterior Cramér-Rao bound is unsatisfied for the discrete uncertain system, we use some continuous variables to approximate the discrete latent variables. Then, a new Cramér-Rao bound can be achieved by a limiting process of the Cramér-Rao bound of the continuous system. It avoids the complex integral, which can reduce the computation burden. Based on the new posterior Cramér-Rao bound, the optimal solution of the sensor selection problem can be derived analytically. Thus, it can be used to deal with the sensor selection of a large-scale sensor networks. Two typical numerical examples verify the effectiveness of the proposed methods.

The Dynamic Responsiveness of Organizations

DEFF Research Database (Denmark)

Andersen, Torben Juul

Organizational studies should address contemporary challenges of dealing effectively with the increasingly complex and dynamic business conditions. In this context we argue that structural features are linked to the corporate strategy process and affect the organization’s ability to respond...... to respond to uncertain and changing conditions. We apply this model to interactions among individuals in organizations where ongoing experiential insights among dispersed operating managers interact with the forward-looking planning considerations around top-management. This identifies an organization...... identifies a dynamic system of interacting fast and slow processes. The fast system observes and reacts to environmental stimuli and the slow system interprets events and reasons about future actions. When the fast and slow processes interact they form a dynamic adaptive system that allows the organization...

Adaptive fuzzy dynamic surface control of nonlinear systems with input saturation and time-varying output constraints

Science.gov (United States)

Edalati, L.; Khaki Sedigh, A.; Aliyari Shooredeli, M.; Moarefianpour, A.

2018-02-01

This paper deals with the design of adaptive fuzzy dynamic surface control for uncertain strict-feedback nonlinear systems with asymmetric time-varying output constraints in the presence of input saturation. To approximate the unknown nonlinear functions and overcome the problem of explosion of complexity, a Fuzzy logic system is combined with the dynamic surface control in the backstepping design technique. To ensure the output constraints satisfaction, an asymmetric time-varying Barrier Lyapunov Function (BLF) is used. Moreover, by applying the minimal learning parameter technique, the number of the online parameters update for each subsystem is reduced to 2. Hence, the semi-globally uniformly ultimately boundedness (SGUUB) of all the closed-loop signals with appropriate tracking error convergence is guaranteed. The effectiveness of the proposed control is demonstrated by two simulation examples.

Stochastic change detection in uncertain nonlinear systems using reduced-order models: classification

International Nuclear Information System (INIS)

Yun, Hae-Bum; Masri, Sami F

2009-01-01

A reliable structural health monitoring methodology (SHM) is proposed to detect relatively small changes in uncertain nonlinear systems. A total of 4000 physical tests were performed using a complex nonlinear magneto-rheological (MR) damper. With the effective (or 'genuine') changes and uncertainties in the system characteristics of the semi-active MR damper, which were precisely controlled with known means and standard deviation of the input current, the tested MR damper was identified with the restoring force method (RFM), a non-parametric system identification method involving two-dimensional orthogonal polynomials. Using the identified RFM coefficients, both supervised and unsupervised pattern recognition techniques (including support vector classification and k-means clustering) were employed to detect system changes in the MR damper. The classification results showed that the identified coefficients with orthogonal basis function can be used as reliable indicators for detecting (small) changes, interpreting the physical meaning of the detected changes without a priori knowledge of the monitored system and quantifying the uncertainty bounds of the detected changes. The classification errors were analyzed using the standard detection theory to evaluate the performance of the developed SHM methodology. An optimal classifier design procedure was also proposed and evaluated to minimize type II (or 'missed') errors

Distributed Fault-Tolerant Control of Networked Uncertain Euler-Lagrange Systems Under Actuator Faults.

Science.gov (United States)

Chen, Gang; Song, Yongduan; Lewis, Frank L

2016-05-03

This paper investigates the distributed fault-tolerant control problem of networked Euler-Lagrange systems with actuator and communication link faults. An adaptive fault-tolerant cooperative control scheme is proposed to achieve the coordinated tracking control of networked uncertain Lagrange systems on a general directed communication topology, which contains a spanning tree with the root node being the active target system. The proposed algorithm is capable of compensating for the actuator bias fault, the partial loss of effectiveness actuation fault, the communication link fault, the model uncertainty, and the external disturbance simultaneously. The control scheme does not use any fault detection and isolation mechanism to detect, separate, and identify the actuator faults online, which largely reduces the online computation and expedites the responsiveness of the controller. To validate the effectiveness of the proposed method, a test-bed of multiple robot-arm cooperative control system is developed for real-time verification. Experiments on the networked robot-arms are conduced and the results confirm the benefits and the effectiveness of the proposed distributed fault-tolerant control algorithms.

Data Envelopment Analysis with Uncertain Inputs and Outputs

Directory of Open Access Journals (Sweden)

Meilin Wen

2014-01-01

Full Text Available Data envelopment analysis (DEA, as a useful management and decision tool, has been widely used since it was first invented by Charnes et al. in 1978. On the one hand, the DEA models need accurate inputs and outputs data. On the other hand, in many situations, inputs and outputs are volatile and complex so that they are difficult to measure in an accurate way. The conflict leads to the researches of uncertain DEA models. This paper will consider DEA in uncertain environment, thus producing a new model based on uncertain measure. Due to the complexity of the new uncertain DEA model, an equivalent deterministic model is presented. Finally, a numerical example is presented to illustrate the effectiveness of the uncertain DEA model.

Near Space Hypersonic Unmanned Aerial Vehicle Dynamic Surface Backstepping Control Design

Directory of Open Access Journals (Sweden)

Jinyong YU

2014-07-01

Full Text Available Compared with traditional aircraft, the near space hypersonic unmanned aerial vehicle control system design must deal with the extra prominent dynamics characters, which are differ from the traditional aircrafts control system design. A new robust adaptive control design method is proposed for one hypersonic unmanned aerial vehicle (HSUAV uncertain MIMO nonaffine block control system by using multilayer neural networks, feedback linearization technology, and dynamic surface backstepping. Multilayer neural networks are used to compensate the influence from the uncertain, which designs the robust terms to solve the problem from approach error. Adaptive backstepping is adopted designed to ensure control law, the dynamic surface control strategy to eliminate “the explosion of terms” by introducing a series of first order filters to obtain the differentiation of the virtual control inputs. Finally, nonlinear six-degree-of-freedom (6-DOF numerical simulation results for a HSUAV model are presented to demonstrate the effectiveness of the proposed method.

Structure identification of an uncertain network coupled with complex-variable chaotic systems via adaptive impulsive control

International Nuclear Information System (INIS)

Liu Dan-Feng; Wu Zhao-Yan; Ye Qing-Ling

2014-01-01

In this paper, structure identification of an uncertain network coupled with complex-variable chaotic systems is investigated. Both the topological structure and the system parameters can be unknown and need to be identified. Based on impulsive stability theory and the Lyapunov function method, an impulsive control scheme combined with an adaptive strategy is adopted to design effective and universal network estimators. The restriction on the impulsive interval is relaxed by adopting an adaptive strategy. Further, the proposed method can monitor the online switching topology effectively. Several numerical simulations are provided to illustrate the effectiveness of the theoretical results. (general)

Green taxes and uncertain timing of technological change

International Nuclear Information System (INIS)

Aronsson, T.

2001-01-01

This paper concerns the role of environmental taxation in a model with endogenous technological change, where the latter implies that natural inputs become more productive. The timing of technological change is, in turn, uncertain and the likelihood of discovering the new technology is related to the amount of resources spent on R and D. The analysis is based on a dynamic general equilibrium model. One purpose of the paper is to design a policy so as to internalize the external effects arising from pollution and R and D. Another is to develop cost benefit rules for green tax reforms, when the initial equilibrium is suboptimal

Adaptive control of a quadrotor aerial vehicle with input constraints and uncertain parameters

Science.gov (United States)

Tran, Trong-Toan; Ge, Shuzhi Sam; He, Wei

2018-05-01

In this paper, we address the problem of adaptive bounded control for the trajectory tracking of a Quadrotor Aerial Vehicle (QAV) while the input saturations and uncertain parameters with the known bounds are simultaneously taken into account. First, to deal with the underactuated property of the QAV model, we decouple and construct the QAV model as a cascaded structure which consists of two fully actuated subsystems. Second, to handle the input constraints and uncertain parameters, we use a combination of the smooth saturation function and smooth projection operator in the control design. Third, to ensure the stability of the overall system of the QAV, we develop the technique for the cascaded system in the presence of both the input constraints and uncertain parameters. Finally, the region of stability of the closed-loop system is constructed explicitly, and our design ensures the asymptotic convergence of the tracking errors to the origin. The simulation results are provided to illustrate the effectiveness of the proposed method.

Forecasting the shortage of neurosurgeons in Iran using a system dynamics model approach.

Science.gov (United States)

Rafiei, Sima; Daneshvaran, Arman; Abdollahzade, Sina

2018-01-01

Shortage of physicians particularly in specialty levels is considered as an important issue in Iran health system. Thus, in an uncertain environment, long-term planning is required for health professionals as a basic priority on a national scale. This study aimed to estimate the number of required neurosurgeons using system dynamic modeling. System dynamic modeling was applied to predict the gap between stock and number of required neurosurgeons in Iran up to 2020. A supply and demand simulation model was constructed for neurosurgeons using system dynamic approach. The demand model included epidemiological, demographic, and utilization variables along with supply model-incorporated current stock of neurosurgeons and flow variables such as attrition, migration, and retirement rate. Data were obtained from various governmental databases and were analyzed by Vensim PLE Version 3.0 to address the flow of health professionals, clinical infrastructure, population demographics, and disease prevalence during the time. It was forecasted that shortage in number of neurosurgeons would disappear at 2020. The most dominant determinants on predicted number of neurosurgeons were the prevalence of neurosurgical diseases, the rate for service utilization, and medical capacity of the region. Shortage of neurosurgeons in some areas of the country relates to maldistribution of the specialists. Accordingly, there is a need to reconsider the allocation system for health professionals within the country instead of increasing the overall number of acceptance quota in training positions.

Finite-Time Robust H∞ Control for Uncertain Linear Continuous-Time Singular Systems with Exogenous Disturbances

Directory of Open Access Journals (Sweden)

Songlin Wo

2018-01-01

Full Text Available Singular systems arise in a great deal of domains of engineering and can be used to solve problems which are more difficult and more extensive than regular systems to solve. Therefore, in this paper, the definition of finite-time robust H∞ control for uncertain linear continuous-time singular systems is presented. The problem we address is to design a robust state feedback controller which can deal with the singular system with time-varying norm-bounded exogenous disturbance, such that the singular system is finite-time robust bounded (FTRB with disturbance attenuation γ. Sufficient conditions for the existence of solutions to this problem are obtained in terms of linear matrix equalities (LMIs. When these LMIs are feasible, the desired robust controller is given. A detailed solving method is proposed for the restricted linear matrix inequalities. Finally, examples are given to show the validity of the methodology.

Characterizing source-sink dynamics with genetic parentage assignments

NARCIS (Netherlands)

Peery, M. Zachariah; Beissinger, Steven R.; House, Roger F.; Berube, Martine; Hall, Laurie A.; Sellas, Anna; Palsboll, Per J.

2008-01-01

Source-sink dynamics have been suggested to characterize the population structure of many species, but the prevalence of source-sink systems in nature is uncertain because of inherent challenges in estimating migration rates among populations. Migration rates are often difficult to estimate directly

Strict Constraint Feasibility in Analysis and Design of Uncertain Systems

Science.gov (United States)

Crespo, Luis G.; Giesy, Daniel P.; Kenny, Sean P.

2006-01-01

This paper proposes a methodology for the analysis and design optimization of models subject to parametric uncertainty, where hard inequality constraints are present. Hard constraints are those that must be satisfied for all parameter realizations prescribed by the uncertainty model. Emphasis is given to uncertainty models prescribed by norm-bounded perturbations from a nominal parameter value, i.e., hyper-spheres, and by sets of independently bounded uncertain variables, i.e., hyper-rectangles. These models make it possible to consider sets of parameters having comparable as well as dissimilar levels of uncertainty. Two alternative formulations for hyper-rectangular sets are proposed, one based on a transformation of variables and another based on an infinity norm approach. The suite of tools developed enable us to determine if the satisfaction of hard constraints is feasible by identifying critical combinations of uncertain parameters. Since this practice is performed without sampling or partitioning the parameter space, the resulting assessments of robustness are analytically verifiable. Strategies that enable the comparison of the robustness of competing design alternatives, the approximation of the robust design space, and the systematic search for designs with improved robustness characteristics are also proposed. Since the problem formulation is generic and the solution methods only require standard optimization algorithms for their implementation, the tools developed are applicable to a broad range of problems in several disciplines.

Dynamical systems

CERN Document Server

Sternberg, Shlomo

2010-01-01

Celebrated mathematician Shlomo Sternberg, a pioneer in the field of dynamical systems, created this modern one-semester introduction to the subject for his classes at Harvard University. Its wide-ranging treatment covers one-dimensional dynamics, differential equations, random walks, iterated function systems, symbolic dynamics, and Markov chains. Supplementary materials offer a variety of online components, including PowerPoint lecture slides for professors and MATLAB exercises.""Even though there are many dynamical systems books on the market, this book is bound to become a classic. The the

Controlling uncertain neutral dynamic systems with delay in control input

International Nuclear Information System (INIS)

Park, Ju H.; Kwon, O.

2005-01-01

This article gives a novel criterion for the asymptotic stabilization of the zero solutions of a class of neutral systems with delays in control input. By constructing Lyapunov functionals, we have obtained the criterion which is expressed in terms of matrix inequalities. The solutions of the inequalities can be easily solved by efficient convex optimization algorithms. A numerical example is included to illustrate the design procedure of the proposed method

Structure Identification of Uncertain Complex Networks Based on Anticipatory Projective Synchronization.

Directory of Open Access Journals (Sweden)

Liu Heng

Full Text Available This paper investigates a method to identify uncertain system parameters and unknown topological structure in general complex networks with or without time delay. A complex network, which has uncertain topology and unknown parameters, is designed as a drive network, and a known response complex network with an input controller is designed to identify the drive network. Under the proposed input controller, the drive network and the response network can achieve anticipatory projective synchronization when the system is steady. Lyapunov theorem and Barbǎlat's lemma guarantee the stability of synchronization manifold between two networks. When the synchronization is achieved, the system parameters and topology in response network can be changed to equal with the parameters and topology in drive network. A numerical example is given to show the effectiveness of the proposed method.

The property investigation of the numerical code TIGER for the uncertain analysis

International Nuclear Information System (INIS)

Ebina, Takanori; Ohi, Takao

2008-03-01

In order to obtain the information concerning the safety of the geological disposal under various geological environments, the sensitivity analysis that considers the uncertainty of parameters resulting from the insufficiency of knowledge and information plays an important role. The numerical code TIGER allows the physical and chemical properties within the system to vary with time in the radionuclide migration analysis from vitrified glass to rock and these function is useful for understanding the effect of the property change of each barrier in such sensitivity analysis. Therefore, at this study, some typical processing methods with the engineered barrier system and the host rock were developed at fast, and through the comparison with the calculation time, the step of preprocessing and postprocessing, the most suitable method was considered. After this consideration, the interrelation between the calculation accuracy and the calculation time at the most suitable method was examined for the purpose of using this method to the uncertain analysis. In addition, the STRIDER that was the program to make the input file for the uncertain analysis with setting random parameter and do the preprocessing and the postprocessing, was improved for the uncertain analysis. Through this consideration, the information of the best processing method, the calculation accuracy, and the analysis tool was arranged for an uncertain analysis using TIGER. (author)

Robust Optimization for Household Load Scheduling with Uncertain Parameters

Directory of Open Access Journals (Sweden)

Jidong Wang

2018-04-01

Full Text Available Home energy management systems (HEMS face many challenges of uncertainty, which have a great impact on the scheduling of home appliances. To handle the uncertain parameters in the household load scheduling problem, this paper uses a robust optimization method to rebuild the household load scheduling model for home energy management. The model proposed in this paper can provide the complete robust schedules for customers while considering the disturbance of uncertain parameters. The complete robust schedules can not only guarantee the customers’ comfort constraints but also cooperatively schedule the electric devices for cost minimization and load shifting. Moreover, it is available for customers to obtain multiple schedules through setting different robust levels while considering the trade-off between the comfort and economy.

Development of radiation risk assessment simulator using system dynamics methodology

International Nuclear Information System (INIS)

Kang, Kyung Min; Jae, Moosung

2008-01-01

The potential magnitudes of radionuclide releases under severe accident loadings and offsite consequences as well as the overall risk (the product of accident frequencies and consequences) are analyzed and evaluated quantitatively in this study. The system dynamics methodology has been applied to predict the time-dependent behaviors such as feedback and dependency as well as to model uncertain behavior of complex physical system. It is used to construct the transfer mechanisms of time dependent radioactivity concentration and to evaluate them. Dynamic variations of radio activities are simulated by considering several effects such as deposition, weathering, washout, re-suspension, root uptake, translocation, leaching, senescence, intake, and excretion of soil. The time-dependent radio-ecological model applicable to Korean specific environment has been developed in order to assess the radiological consequences following the short-term deposition of radio-nuclides during severe accidents nuclear power plant. An ingestion food chain model can estimate time dependent radioactivity concentrations in foodstuffs. And it is also shown that the system dynamics approach is useful for analyzing the phenomenon of the complex system as well as the behavior of structure values with respect to time. The output of this model (Bq ingested per Bq m - 2 deposited) may be multiplied by the deposition and a dose conversion factor (Gy Bq -1 ) to yield organ-specific doses. The model may be run deterministically to yield a single estimate or stochastic distributions by 'Monte-Carlo' calculation that reflects uncertainty of parameter and model uncertainties. The results of this study may contribute to identifying the relative importance of various parameters occurred in consequence analysis, as well as to assessing risk reduction effects in accident management. (author)

Delay-Dependent Stability Criteria of Uncertain Periodic Switched Recurrent Neural Networks with Time-Varying Delays

Directory of Open Access Journals (Sweden)

Xing Yin

2011-01-01

uncertain periodic switched recurrent neural networks with time-varying delays. When uncertain discrete-time recurrent neural network is a periodic system, it is expressed as switched neural network for the finite switching state. Based on the switched quadratic Lyapunov functional approach (SQLF and free-weighting matrix approach (FWM, some linear matrix inequality criteria are found to guarantee the delay-dependent asymptotical stability of these systems. Two examples illustrate the exactness of the proposed criteria.

Adaptive wave filtering for dynamic positioning of marine vessels using maximum likelihood identification: Theory and experiments

Digital Repository Service at National Institute of Oceanography (India)

Hassani, V.; Sorensen, A.J.; Pascoal, A.M.

This paper addresses a filtering problem that arises in the design of dynamic positioning systems for ships and offshore rigs subjected to the influence of sea waves. The dynamic model of the vessel captures explicitly the sea state as an uncertain...

(Approximate) Uncertain Skylines

DEFF Research Database (Denmark)

Afshani, Peyman; Agarwal, Pankaj K.; Arge, Lars Allan

2011-01-01

Given a set of points with uncertain locations, we consider the problem of computing the probability of each point lying on the skyline, that is, the probability that it is not dominated by any other input point. If each point’s uncertainty is described as a probability distribution over a discre...

(Approximate) Uncertain Skylines

DEFF Research Database (Denmark)

Afshani, Peyman; Agarwal, Pankaj K.; Arge, Lars

2013-01-01

Given a set of points with uncertain locations, we consider the problem of computing the probability of each point lying on the skyline, that is, the probability that it is not dominated by any other input point. If each point’s uncertainty is described as a probability distribution over a discre...

Floor response spectra of buildings with uncertain structural properties

International Nuclear Information System (INIS)

Chen, P.C.

1975-01-01

All Category I equipment, such as reactors, vessels, and major piping systems of nuclear power plants, is required to withstand earthquake loadings in order to minimize risk of seismic damage. The equipment is designed by using response spectra of the floor on which the equipment is mounted. The floor response spectra are constructed usually from the floor response time histories which are obtained through a deterministic dynamic analysis. This analysis assumes that all structural parameters, such as mass, stiffness, and damping have been calculated precisely, and that the earthquakes are known. However, structural parameters are usually difficult to determine precisely if the structures are massive and/or irregular, such as nuclear containments and its internal structures with foundation soil incorporated into the analysis. Faced with these uncertainties, it has been the practice to broaden the floor response spectra peaks by +-10 percent of the peak frequencies on the basis of conservatism. This approach is based on engineering judgement and does not have an analytical basis to provide a sufficient level of confidence in using these spectra for equipment design. To insure reliable design, it is necessary to know structural response variations due to variations in structural properties. This consideration leads to the treatment of structural properties as random variables and the use of probabilistic methods to predict structural response more accurately. New results on floor response spectra of buildings with uncertain structural properties obtained by determining the probabilistic dynamic response from the deterministic dynamic response and its standard deviation are presented. The resulting probabilistic floor response spectra are compared with those obtained deterministically, and are shown to provide a more reliable method for determining seismic forces

Adaptive optimization as a design and management methodology for coal-mining enterprise in uncertain and volatile market environment - the conceptual framework

Science.gov (United States)

Mikhalchenko, V. V.; Rubanik, Yu T.

2016-10-01

The work is devoted to the problem of cost-effective adaptation of coal mines to the volatile and uncertain market conditions. Conceptually it can be achieved through alignment of the dynamic characteristics of the coal mining system and power spectrum of market demand for coal product. In practical terms, this ensures the viability and competitiveness of coal mines. Transformation of dynamic characteristics is to be done by changing the structure of production system as well as corporate, logistics and management processes. The proposed methods and algorithms of control are aimed at the development of the theoretical foundations of adaptive optimization as basic methodology for coal mine enterprise management in conditions of high variability and uncertainty of economic and natural environment. Implementation of the proposed methodology requires a revision of the basic principles of open coal mining enterprises design.

Fuzzy Adaptive Compensation Control of Uncertain Stochastic Nonlinear Systems With Actuator Failures and Input Hysteresis.

Science.gov (United States)

Wang, Jianhui; Liu, Zhi; Chen, C L Philip; Zhang, Yun

2017-10-12

Hysteresis exists ubiquitously in physical actuators. Besides, actuator failures/faults may also occur in practice. Both effects would deteriorate the transient tracking performance, and even trigger instability. In this paper, we consider the problem of compensating for actuator failures and input hysteresis by proposing a fuzzy control scheme for stochastic nonlinear systems. Compared with the existing research on stochastic nonlinear uncertain systems, it is found that how to guarantee a prescribed transient tracking performance when taking into account actuator failures and hysteresis simultaneously also remains to be answered. Our proposed control scheme is designed on the basis of the fuzzy logic system and backstepping techniques for this purpose. It is proven that all the signals remain bounded and the tracking error is ensured to be within a preestablished bound with the failures of hysteretic actuator. Finally, simulations are provided to illustrate the effectiveness of the obtained theoretical results.

On the synchronization of uncertain master-slave chaotic systems with disturbance

International Nuclear Information System (INIS)

Wang Bo; Wen Guangjun

2009-01-01

This paper focuses on the synchronization of a class of master-slave chaotic systems with uncertainty and disturbance. A sliding surface is adopted newly to ensure the stability of the error dynamics in sliding mode and a dynamic variable structure controller (DVSC) is derived to realize chaos synchronization better. The typical numerical example is given to demonstrate the effectiveness of the result obtained.

Discrete-Time Sliding-Mode Control of Uncertain Systems with Time-Varying Delays via Descriptor Approach

Directory of Open Access Journals (Sweden)

Maode Yan

2008-01-01

Full Text Available This paper considers the problem of robust discrete-time sliding-mode control (DT-SMC design for a class of uncertain linear systems with time-varying delays. By applying a descriptor model transformation and Moon's inequality for bounding cross terms, a delay-dependent sufficient condition for the existence of stable sliding surface is given in terms of linear matrix inequalities (LMIs. Based on this existence condition, the synthesized sliding mode controller can guarantee the sliding-mode reaching condition of the specified discrete-time sliding surface for all admissible uncertainties and time-varying delays. An illustrative example verifies the effectiveness of the proposed method.

Adaptive critic designs for optimal control of uncertain nonlinear systems with unmatched interconnections.

Science.gov (United States)

Yang, Xiong; He, Haibo

2018-05-26

In this paper, we develop a novel optimal control strategy for a class of uncertain nonlinear systems with unmatched interconnections. To begin with, we present a stabilizing feedback controller for the interconnected nonlinear systems by modifying an array of optimal control laws of auxiliary subsystems. We also prove that this feedback controller ensures a specified cost function to achieve optimality. Then, under the framework of adaptive critic designs, we use critic networks to solve the Hamilton-Jacobi-Bellman equations associated with auxiliary subsystem optimal control laws. The critic network weights are tuned through the gradient descent method combined with an additional stabilizing term. By using the newly established weight tuning rules, we no longer need the initial admissible control condition. In addition, we demonstrate that all signals in the closed-loop auxiliary subsystems are stable in the sense of uniform ultimate boundedness by using classic Lyapunov techniques. Finally, we provide an interconnected nonlinear plant to validate the present control scheme. Copyright © 2018 Elsevier Ltd. All rights reserved.

Asynchronous L1-gain control of uncertain switched positive linear systems with dwell time.

Science.gov (United States)

Li, Yang; Zhang, Hongbin

2018-04-01

In this paper, dwell time (DT) stability, L 1 -gain performance analysis and asynchronous L 1 -gain controller design problems of uncertain switched positive linear systems (SPLSs) are investigated. Via a time-scheduled multiple linear co-positive Lyapunov function (TSMLCLF) approach, convex sufficient conditions of DT stability and L 1 -gain performance of SPLSs with interval and polytopic uncertainties are presented. Furthermore, by utilizing the feature that the TSMLCLF keeps decreasing even if the controller is running asynchronously with the system, the asynchronous L 1 -gain controller design problem of SPLSs with interval and polytopic uncertainties is investigated. Convex sufficient conditions of the existence of time-varying asynchronous state-feedback controller which can ensure the closed-loop system's positivity, stability and L 1 -gain performance are established, and the controller gain matrices can be calculated instantaneously online. The obtained L 1 -gain in the paper is standard. All the results are presented in terms of linear programming. A practical example is provided to show the effectiveness of the results. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Neural basis of uncertain cue processing in trait anxiety.

Science.gov (United States)

Zhang, Meng; Ma, Chao; Luo, Yanyan; Li, Ji; Li, Qingwei; Liu, Yijun; Ding, Cody; Qiu, Jiang

2016-02-19

Individuals with high trait anxiety form a non-clinical group with a predisposition for an anxiety-related bias in emotional and cognitive processing that is considered by some to be a prerequisite for psychiatric disorders. Anxious individuals tend to experience more worry under uncertainty, and processing uncertain information is an important, but often overlooked factor in anxiety. So, we decided to explore the brain correlates of processing uncertain information in individuals with high trait anxiety using the learn-test paradigm. Behaviorally, the percentages on memory test and the likelihood ratios of identifying novel stimuli under uncertainty were similar to the certain fear condition, but different from the certain neutral condition. The brain results showed that the visual cortex, bilateral fusiform gyrus, and right parahippocampal gyrus were active during the processing of uncertain cues. Moreover, we found that trait anxiety was positively correlated with the BOLD signal of the right parahippocampal gyrus during the processing of uncertain cues. No significant results were found in the amygdala during uncertain cue processing. These results suggest that memory retrieval is associated with uncertain cue processing, which is underpinned by over-activation of the right parahippocampal gyrus, in individuals with high trait anxiety.

Robust Stability and Stabilization of Interval Uncertain Descriptor Fractional-Order Systems with the Fractional-Order α: The 1≤α<2 Case

Directory of Open Access Journals (Sweden)

Yuanhua Li

2015-01-01

Full Text Available Stability and stabilization of fractional-order interval system is investigated. By adding parameters to linear matrix inequalities, necessary and sufficient conditions for stability and stabilization of the system are obtained. The results on stability check for uncertain FO-LTI systems with interval coefficients of dimension n only need to solve one 4n-by-4n LMI. Numerical examples are presented to shown the effectiveness of our results.

When, not if: the inescapability of an uncertain climate future.

Science.gov (United States)

Ballard, Timothy; Lewandowsky, Stephan

2015-11-28

Climate change projections necessarily involve uncertainty. Analysis of the physics and mathematics of the climate system reveals that greater uncertainty about future temperature increases is nearly always associated with greater expected damages from climate change. In contrast to those normative constraints, uncertainty is frequently cited in public discourse as a reason to delay mitigative action. This failure to understand the actual implications of uncertainty may incur notable future costs. It is therefore important to communicate uncertainty in a way that improves people's understanding of climate change risks. We examined whether responses to projections were influenced by whether the projection emphasized uncertainty in the outcome or in its time of arrival. We presented participants with statements and graphs indicating projected increases in temperature, sea levels, ocean acidification and a decrease in arctic sea ice. In the uncertain-outcome condition, statements reported the upper and lower confidence bounds of the projected outcome at a fixed time point. In the uncertain time-of-arrival condition, statements reported the upper and lower confidence bounds of the projected time of arrival for a fixed outcome. Results suggested that people perceived the threat as more serious and were more likely to encourage mitigative action in the time-uncertain condition than in the outcome-uncertain condition. This finding has implications for effectively communicating the climate change risks to policy-makers and the general public. © 2015 The Author(s).

Nonfragile Robust Model Predictive Control for Uncertain Constrained Systems with Time-Delay Compensation

Directory of Open Access Journals (Sweden)

Wei Jiang

2016-01-01

Full Text Available This study investigates the problem of asymptotic stabilization for a class of discrete-time linear uncertain time-delayed systems with input constraints. Parametric uncertainty is assumed to be structured, and delay is assumed to be known. In Lyapunov stability theory framework, two synthesis schemes of designing nonfragile robust model predictive control (RMPC with time-delay compensation are put forward, where the additive and the multiplicative gain perturbations are, respectively, considered. First, by designing appropriate Lyapunov-Krasovskii (L-K functions, the robust performance index is defined as optimization problems that minimize upper bounds of infinite horizon cost function. Then, to guarantee closed-loop stability, the sufficient conditions for the existence of desired nonfragile RMPC are obtained in terms of linear matrix inequalities (LMIs. Finally, two numerical examples are provided to illustrate the effectiveness of the proposed approaches.

Cooperative learning neural network output feedback control of uncertain nonlinear multi-agent systems under directed topologies

Science.gov (United States)

Wang, W.; Wang, D.; Peng, Z. H.

2017-09-01

Without assuming that the communication topologies among the neural network (NN) weights are to be undirected and the states of each agent are measurable, the cooperative learning NN output feedback control is addressed for uncertain nonlinear multi-agent systems with identical structures in strict-feedback form. By establishing directed communication topologies among NN weights to share their learned knowledge, NNs with cooperative learning laws are employed to identify the uncertainties. By designing NN-based κ-filter observers to estimate the unmeasurable states, a new cooperative learning output feedback control scheme is proposed to guarantee that the system outputs can track nonidentical reference signals with bounded tracking errors. A simulation example is given to demonstrate the effectiveness of the theoretical results.

Delay-Dependent Stability Analysis of Uncertain Fuzzy Systems with State and Input Delays under Imperfect Premise Matching

Directory of Open Access Journals (Sweden)

Zejian Zhang

2013-01-01

Full Text Available This paper discusses the stability and stabilization problem for uncertain T-S fuzzy systems with time-varying state and input delays. A new augmented Lyapunov function with an additional triple-integral term and different membership functions of the fuzzy models and fuzzy controllers are introduced to derive the stability criterion, which is less conservative than the existing results. Moreover, a new flexibility design method is also provided. Some numerical examples are given to demonstrate the effectiveness and less conservativeness of the proposed method.

Learning from uncertain curves

DEFF Research Database (Denmark)

Mallasto, Anton; Feragen, Aasa

2017-01-01

We introduce a novel framework for statistical analysis of populations of nondegenerate Gaussian processes (GPs), which are natural representations of uncertain curves. This allows inherent variation or uncertainty in function-valued data to be properly incorporated in the population analysis. Us...

Diversified models for portfolio selection based on uncertain semivariance

Science.gov (United States)

Chen, Lin; Peng, Jin; Zhang, Bo; Rosyida, Isnaini

2017-02-01

Since the financial markets are complex, sometimes the future security returns are represented mainly based on experts' estimations due to lack of historical data. This paper proposes a semivariance method for diversified portfolio selection, in which the security returns are given subjective to experts' estimations and depicted as uncertain variables. In the paper, three properties of the semivariance of uncertain variables are verified. Based on the concept of semivariance of uncertain variables, two types of mean-semivariance diversified models for uncertain portfolio selection are proposed. Since the models are complex, a hybrid intelligent algorithm which is based on 99-method and genetic algorithm is designed to solve the models. In this hybrid intelligent algorithm, 99-method is applied to compute the expected value and semivariance of uncertain variables, and genetic algorithm is employed to seek the best allocation plan for portfolio selection. At last, several numerical examples are presented to illustrate the modelling idea and the effectiveness of the algorithm.

System identification for robust and inferential control : with applications to ILC and precision motion systems

NARCIS (Netherlands)

Oomen, T.A.E.

2010-01-01

Feedback control is able to improve the performance of systems in the presence of uncertain dynamical behavior and disturbances. Although a properly designed controller can cope with large uncertainty, certain knowledge regarding the system behavior is crucial for control design. Hence, high

Wind farm investment risks under uncertain CDM benefit in China

International Nuclear Information System (INIS)

Yang, Ming; Nguyen, Francois; T'Serclaes, Philippine de; Buchner, Barbara

2010-01-01

China has set an ambitious target to increase its wind power capacity by 35 GW from 2007 to 2020. The country's hunger for clean power provides great opportunities for wind energy investors. However, risks from China's uncertain electricity market regulation and an uncertain energy policy framework, mainly due to uncertain Clean Development Mechanism (CDM) benefits, prevent foreign investors from investing in China's wind energy. The objectives of this paper are to: (1) quantify wind energy investment risk premiums in an uncertain international energy policy context and (2) evaluate the impact of uncertain CDM benefits on the net present values of wind power projects. With four scenarios, this study simulates possible prices of certified emissions reductions (CERs) from wind power projects. Project net present values (NPVs) have been calculated. The project risk premiums are drawn from different and uncertain CER prices. Our key findings show that uncertain CDM benefits will significantly affect the project NPVs. This paper concludes that the Chinese government needs revising its tariff incentives, most likely by introducing fixed feed-in tariffs (FITs), and re-examining its CDM-granting policy and its wind project tax rates, to facilitate wind power development and enable China to achieve its wind energy target. (author)

Productivity and Regional Employment in Spain: A Dynamic Approach

Directory of Open Access Journals (Sweden)

F. Javier Escribá Pérez

2013-01-01

Full Text Available This paper analyses the impact of sectorial and territorial factors on the dynamics of employment in regional industries in Spain over the period 1980-2006. A dynamic panel data model is estimated using panel data techniques (System-GMM, which provide an alternative methodology for addressing the problem of variable endogeneity. The results confirm the robustness of the contemporary effects: diversification, market size and dynamics in the sector affect employment in the short term. However, effects in the long term are more uncertain.

Memory State Feedback RMPC for Multiple Time-Delayed Uncertain Linear Systems with Input Constraints

Directory of Open Access Journals (Sweden)

Wei-Wei Qin

2014-01-01

Full Text Available This paper focuses on the problem of asymptotic stabilization for a class of discrete-time multiple time-delayed uncertain linear systems with input constraints. Then, based on the predictive control principle of receding horizon optimization, a delayed state dependent quadratic function is considered for incorporating MPC problem formulation. By developing a memory state feedback controller, the information of the delayed plant states can be taken into full consideration. The MPC problem is formulated to minimize the upper bound of infinite horizon cost that satisfies the sufficient conditions. Then, based on the Lyapunov-Krasovskii function, a delay-dependent sufficient condition in terms of linear matrix inequality (LMI can be derived to design a robust MPC algorithm. Finally, the digital simulation results prove availability of the proposed method.

Non-fragile robust stabilization and H{sub {infinity}} control for uncertain stochastic nonlinear time-delay systems

Energy Technology Data Exchange (ETDEWEB)

Zhang Jinhui [Department of Automatic Control, Beijing Institute of Technology, Beijing 100081 (China)], E-mail: jinhuizhang82@gmail.com; Shi Peng [Faculty of Advanced Technology, University of Glamorgan, Pontypridd CF37 1DL (United Kingdom); ILSCM, School of Science and Engineering, Victoria University, Melbourne, Vic. 8001 (Australia); School of Mathematics and Statistics, University of South Australia, Mawson Lakes, SA 5095 (Australia)], E-mail: pshi@glam.ac.uk; Yang Hongjiu [Department of Automatic Control, Beijing Institute of Technology, Beijing 100081 (China)], E-mail: yanghongjiu@gmail.com

2009-12-15

This paper deals with the problem of non-fragile robust stabilization and H{sub {infinity}} control for a class of uncertain stochastic nonlinear time-delay systems. The parametric uncertainties are real time-varying as well as norm bounded. The time-delay factors are unknown and time-varying with known bounds. The aim is to design a memoryless non-fragile state feedback control law such that the closed-loop system is stochastically asymptotically stable in the mean square and the effect of the disturbance input on the controlled output is less than a prescribed level for all admissible parameter uncertainties. New sufficient conditions for the existence of such controllers are presented based on the linear matrix inequalities (LMIs) approach. Numerical example is given to illustrate the effectiveness of the developed techniques.

Dynamical Systems Conference

CERN Document Server

Gils, S; Hoveijn, I; Takens, F; Nonlinear Dynamical Systems and Chaos

1996-01-01

Symmetries in dynamical systems, "KAM theory and other perturbation theories", "Infinite dimensional systems", "Time series analysis" and "Numerical continuation and bifurcation analysis" were the main topics of the December 1995 Dynamical Systems Conference held in Groningen in honour of Johann Bernoulli. They now form the core of this work which seeks to present the state of the art in various branches of the theory of dynamical systems. A number of articles have a survey character whereas others deal with recent results in current research. It contains interesting material for all members of the dynamical systems community, ranging from geometric and analytic aspects from a mathematical point of view to applications in various sciences.

Dynamic Systems and Control Engineering

International Nuclear Information System (INIS)

Kim, Jong Seok

1994-02-01

This book deals with introduction of dynamic system and control engineering, frequency domain modeling of dynamic system, temporal modeling of dynamic system, typical dynamic system and automatic control device, performance and stability of control system, root locus analysis, analysis of frequency domain dynamic system, design of frequency domain dynamic system, design and analysis of space, space of control system and digital control system such as control system design of direct digital and digitalization of consecutive control system.

Dynamic Systems and Control Engineering

Energy Technology Data Exchange (ETDEWEB)

Kim, Jong Seok

1994-02-15

This book deals with introduction of dynamic system and control engineering, frequency domain modeling of dynamic system, temporal modeling of dynamic system, typical dynamic system and automatic control device, performance and stability of control system, root locus analysis, analysis of frequency domain dynamic system, design of frequency domain dynamic system, design and analysis of space, space of control system and digital control system such as control system design of direct digital and digitalization of consecutive control system.

Towards a Framework for Self-Adaptive Reliable Network Services in Highly-Uncertain Environments

DEFF Research Database (Denmark)

Grønbæk, Lars Jesper; Schwefel, Hans-Peter; Ceccarelli, Andrea

2010-01-01

In future inhomogeneous, pervasive and highly dynamic networks, end-nodes may often only rely on unreliable and uncertain observations to diagnose hidden network states and decide upon possible remediation actions. Inherent challenges exists to identify good and timely decision strategies to impr...... execution (and monitoring) of remediation actions. We detail the motivations to the ODDR design, then we present its architecture, and finally we describe our current activities towards the realization and assessment of the framework services and the main results currently achieved....

Decentralized adaptive neural control for high-order interconnected stochastic nonlinear time-delay systems with unknown system dynamics.

Science.gov (United States)

Si, Wenjie; Dong, Xunde; Yang, Feifei

2018-03-01

This paper is concerned with the problem of decentralized adaptive backstepping state-feedback control for uncertain high-order large-scale stochastic nonlinear time-delay systems. For the control design of high-order large-scale nonlinear systems, only one adaptive parameter is constructed to overcome the over-parameterization, and neural networks are employed to cope with the difficulties raised by completely unknown system dynamics and stochastic disturbances. And then, the appropriate Lyapunov-Krasovskii functional and the property of hyperbolic tangent functions are used to deal with the unknown unmatched time-delay interactions of high-order large-scale systems for the first time. At last, on the basis of Lyapunov stability theory, the decentralized adaptive neural controller was developed, and it decreases the number of learning parameters. The actual controller can be designed so as to ensure that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges in the small neighborhood of zero. The simulation example is used to further show the validity of the design method. Copyright © 2018 Elsevier Ltd. All rights reserved.

Adaptive neural network backstepping control for a class of uncertain fractional-order chaotic systems with unknown backlash-like hysteresis

Energy Technology Data Exchange (ETDEWEB)

Wu, Yimin [School of Mathematics and Statistics, Suzhou University, Suzhou 234000 (China); Lv, Hui, E-mail: lvhui207@gmail.com [Department of Applied Mathematics, Huainan Normal University, Huainan 232038 (China)

2016-08-15

In this paper, we consider the control problem of a class of uncertain fractional-order chaotic systems preceded by unknown backlash-like hysteresis nonlinearities based on backstepping control algorithm. We model the hysteresis by using a differential equation. Based on the fractional Lyapunov stability criterion and the backstepping algorithm procedures, an adaptive neural network controller is driven. No knowledge of the upper bound of the disturbance and system uncertainty is required in our controller, and the asymptotical convergence of the tracking error can be guaranteed. Finally, we give two simulation examples to confirm our theoretical results.

System Dynamics

Science.gov (United States)

Morecroft, John

System dynamics is an approach for thinking about and simulating situations and organisations of all kinds and sizes by visualising how the elements fit together, interact and change over time. This chapter, written by John Morecroft, describes modern system dynamics which retains the fundamentals developed in the 1950s by Jay W. Forrester of the MIT Sloan School of Management. It looks at feedback loops and time delays that affect system behaviour in a non-linear way, and illustrates how dynamic behaviour depends upon feedback loop structures. It also recognises improvements as part of the ongoing process of managing a situation in order to achieve goals. Significantly it recognises the importance of context, and practitioner skills. Feedback systems thinking views problems and solutions as being intertwined. The main concepts and tools: feedback structure and behaviour, causal loop diagrams, dynamics, are practically illustrated in a wide variety of contexts from a hot water shower through to a symphony orchestra and the practical application of the approach is described through several real examples of its use for strategic planning and evaluation.

Interactive Dynamic-System Simulation

CERN Document Server

Korn, Granino A

2010-01-01

Showing you how to use personal computers for modeling and simulation, Interactive Dynamic-System Simulation, Second Edition provides a practical tutorial on interactive dynamic-system modeling and simulation. It discusses how to effectively simulate dynamical systems, such as aerospace vehicles, power plants, chemical processes, control systems, and physiological systems. Written by a pioneer in simulation, the book introduces dynamic-system models and explains how software for solving differential equations works. After demonstrating real simulation programs with simple examples, the author

Recursive Estimation for Dynamical Systems with Different Delay Rates Sensor Network and Autocorrelated Process Noises

Directory of Open Access Journals (Sweden)

Jianxin Feng

2014-01-01

Full Text Available The recursive estimation problem is studied for a class of uncertain dynamical systems with different delay rates sensor network and autocorrelated process noises. The process noises are assumed to be autocorrelated across time and the autocorrelation property is described by the covariances between different time instants. The system model under consideration is subject to multiplicative noises or stochastic uncertainties. The sensor delay phenomenon occurs in a random way and each sensor in the sensor network has an individual delay rate which is characterized by a binary switching sequence obeying a conditional probability distribution. By using the orthogonal projection theorem and an innovation analysis approach, the desired recursive robust estimators including recursive robust filter, predictor, and smoother are obtained. Simulation results are provided to demonstrate the effectiveness of the proposed approaches.

Cooperative Strategies for Maximum-Flow Problem in Uncertain Decentralized Systems Using Reliability Analysis

Directory of Open Access Journals (Sweden)

Hadi Heidari Gharehbolagh

2016-01-01

Full Text Available This study investigates a multiowner maximum-flow network problem, which suffers from risky events. Uncertain conditions effect on proper estimation and ignoring them may mislead decision makers by overestimation. A key question is how self-governing owners in the network can cooperate with each other to maintain a reliable flow. Hence, the question is answered by providing a mathematical programming model based on applying the triangular reliability function in the decentralized networks. The proposed method concentrates on multiowner networks which suffer from risky time, cost, and capacity parameters for each network’s arcs. Some cooperative game methods such as τ-value, Shapley, and core center are presented to fairly distribute extra profit of cooperation. A numerical example including sensitivity analysis and the results of comparisons are presented. Indeed, the proposed method provides more reality in decision-making for risky systems, hence leading to significant profits in terms of real cost estimation when compared with unforeseen effects.

Planning in Dynamic and Uncertain Environments

Science.gov (United States)

1994-05-01

119 E.3 SIPE Restrictions .................................. 120 F Demonstrations in the SOCAP Domain 121 F.1 SOCAP Demonstration...planning problem from the U.S. Central Command. Their application system, SOCAP (System for Operations and Crises Action Planning), was a major component of...Technology SCren I Domain Reasoning STechnology Requirements • SOCAP Military Operations -w Planning Planning Problem System Figure 1.1: Overview of SRI

Cooperative Networked Control of Dynamical Peer-to-Peer Vehicle Systems

National Research Council Canada - National Science Library

Dullerud, Geir E; Bullo, Francesco; Feron, Eric; Frazzoli, Emilio; Kumar, P. R; Lall, Sanjay; Liberzon, Daniel; Lynch, Nancy A; Mitchell, John C; Mitter, Sanjoy K

2007-01-01

... and semi-autonomous air vehicles. The research is specifically aimed at the critical reliability and performance issues facing autonomous vehicle systems which operate in highly uncertain environments, and enables the vehicles...

System dynamics with interaction discontinuity

CERN Document Server

Luo, Albert C J

2015-01-01

This book describes system dynamics with discontinuity caused by system interactions and presents the theory of flow singularity and switchability at the boundary in discontinuous dynamical systems. Based on such a theory, the authors address dynamics and motion mechanism of engineering discontinuous systems due to interaction. Stability and bifurcations of fixed points in nonlinear discrete dynamical systems are presented, and mapping dynamics are developed for analytical predictions of periodic motions in engineering discontinuous dynamical systems. Ultimately, the book provides an alternative way to discuss the periodic and chaotic behaviors in discontinuous dynamical systems.

Distributed cooperative H∞ optimal tracking control of MIMO nonlinear multi-agent systems in strict-feedback form via adaptive dynamic programming

Science.gov (United States)

Luy, N. T.

2018-04-01

The design of distributed cooperative H∞ optimal controllers for multi-agent systems is a major challenge when the agents' models are uncertain multi-input and multi-output nonlinear systems in strict-feedback form in the presence of external disturbances. In this paper, first, the distributed cooperative H∞ optimal tracking problem is transformed into controlling the cooperative tracking error dynamics in affine form. Second, control schemes and online algorithms are proposed via adaptive dynamic programming (ADP) and the theory of zero-sum differential graphical games. The schemes use only one neural network (NN) for each agent instead of three from ADP to reduce computational complexity as well as avoid choosing initial NN weights for stabilising controllers. It is shown that despite not using knowledge of cooperative internal dynamics, the proposed algorithms not only approximate values to Nash equilibrium but also guarantee all signals, such as the NN weight approximation errors and the cooperative tracking errors in the closed-loop system, to be uniformly ultimately bounded. Finally, the effectiveness of the proposed method is shown by simulation results of an application to wheeled mobile multi-robot systems.

A MODIFIED GENETIC ALGORITHM FOR FINDING FUZZY SHORTEST PATHS IN UNCERTAIN NETWORKS

Directory of Open Access Journals (Sweden)

A. A. Heidari

2016-06-01

Full Text Available In realistic network analysis, there are several uncertainties in the measurements and computation of the arcs and vertices. These uncertainties should also be considered in realizing the shortest path problem (SPP due to the inherent fuzziness in the body of expert's knowledge. In this paper, we investigated the SPP under uncertainty to evaluate our modified genetic strategy. We improved the performance of genetic algorithm (GA to investigate a class of shortest path problems on networks with vague arc weights. The solutions of the uncertain SPP with considering fuzzy path lengths are examined and compared in detail. As a robust metaheuristic, GA algorithm is modified and evaluated to tackle the fuzzy SPP (FSPP with uncertain arcs. For this purpose, first, a dynamic operation is implemented to enrich the exploration/exploitation patterns of the conventional procedure and mitigate the premature convergence of GA technique. Then, the modified GA (MGA strategy is used to resolve the FSPP. The attained results of the proposed strategy are compared to those of GA with regard to the cost, quality of paths and CPU times. Numerical instances are provided to demonstrate the success of the proposed MGA-FSPP strategy in comparison with GA. The simulations affirm that not only the proposed technique can outperform GA, but also the qualities of the paths are effectively improved. The results clarify that the competence of the proposed GA is preferred in view of quality quantities. The results also demonstrate that the proposed method can efficiently be utilized to handle FSPP in uncertain networks.

Information Fusion for Hypothesis Generation under Uncertain and Partial Information Access Situation

Science.gov (United States)

2008-07-21

been well investigated in the past. LE CHATELIER -BRAUN’S PRINCIPLE provides a basis for response against perturbations for systems in equilibrium, as...layer from the low level voltage fluctuation. (Fig. 3) Fig 3: Airplane as an example of engineered robust system In principle , robustness of the...as fundamental architectural principle Since the system will be used under hostile and uncertain environments, robustness shall be the major

Robustness analysis of uncertain dynamical neural networks with multiple time delays.

Science.gov (United States)

Senan, Sibel

2015-10-01

This paper studies the problem of global robust asymptotic stability of the equilibrium point for the class of dynamical neural networks with multiple time delays with respect to the class of slope-bounded activation functions and in the presence of the uncertainties of system parameters of the considered neural network model. By using an appropriate Lyapunov functional and exploiting the properties of the homeomorphism mapping theorem, we derive a new sufficient condition for the existence, uniqueness and global robust asymptotic stability of the equilibrium point for the class of neural networks with multiple time delays. The obtained stability condition basically relies on testing some relationships imposed on the interconnection matrices of the neural system, which can be easily verified by using some certain properties of matrices. An instructive numerical example is also given to illustrate the applicability of our result and show the advantages of this new condition over the previously reported corresponding results. Copyright © 2015 Elsevier Ltd. All rights reserved.

Homogenization-based interval analysis for structural-acoustic problem involving periodical composites and multi-scale uncertain-but-bounded parameters.

Science.gov (United States)

Chen, Ning; Yu, Dejie; Xia, Baizhan; Liu, Jian; Ma, Zhengdong

2017-04-01

This paper presents a homogenization-based interval analysis method for the prediction of coupled structural-acoustic systems involving periodical composites and multi-scale uncertain-but-bounded parameters. In the structural-acoustic system, the macro plate structure is assumed to be composed of a periodically uniform microstructure. The equivalent macro material properties of the microstructure are computed using the homogenization method. By integrating the first-order Taylor expansion interval analysis method with the homogenization-based finite element method, a homogenization-based interval finite element method (HIFEM) is developed to solve a periodical composite structural-acoustic system with multi-scale uncertain-but-bounded parameters. The corresponding formulations of the HIFEM are deduced. A subinterval technique is also introduced into the HIFEM for higher accuracy. Numerical examples of a hexahedral box and an automobile passenger compartment are given to demonstrate the efficiency of the presented method for a periodical composite structural-acoustic system with multi-scale uncertain-but-bounded parameters.

Robust filtering for uncertain systems a parameter-dependent approach

CERN Document Server

Gao, Huijun

2014-01-01

This monograph provides the reader with a systematic treatment of robust filter design, a key issue in systems, control and signal processing, because of the fact that the inevitable presence of uncertainty in system and signal models often degrades the filtering performance and may even cause instability. The methods described are therefore not subject to the rigorous assumptions of traditional Kalman filtering. The monograph is concerned with robust filtering for various dynamical systems with parametric uncertainties, and focuses on parameter-dependent approaches to filter design. Classical filtering schemes, like H2 filtering and H¥ filtering, are addressed, and emerging issues such as robust filtering with constraints on communication channels and signal frequency characteristics are discussed. The text features: ·        design approaches to robust filters arranged according to varying complexity level, and emphasizing robust filtering in the parameter-dependent framework for the first time; ·...

Robust anti-synchronization of uncertain chaotic systems based on multiple-kernel least squares support vector machine modeling

International Nuclear Information System (INIS)

Chen Qiang; Ren Xuemei; Na Jing

2011-01-01

Highlights: Model uncertainty of the system is approximated by multiple-kernel LSSVM. Approximation errors and disturbances are compensated in the controller design. Asymptotical anti-synchronization is achieved with model uncertainty and disturbances. Abstract: In this paper, we propose a robust anti-synchronization scheme based on multiple-kernel least squares support vector machine (MK-LSSVM) modeling for two uncertain chaotic systems. The multiple-kernel regression, which is a linear combination of basic kernels, is designed to approximate system uncertainties by constructing a multiple-kernel Lagrangian function and computing the corresponding regression parameters. Then, a robust feedback control based on MK-LSSVM modeling is presented and an improved update law is employed to estimate the unknown bound of the approximation error. The proposed control scheme can guarantee the asymptotic convergence of the anti-synchronization errors in the presence of system uncertainties and external disturbances. Numerical examples are provided to show the effectiveness of the proposed method.

Anti-windup adaptive PID control design for a class of uncertain chaotic systems with input saturation.

Science.gov (United States)

Tahoun, A H

2017-01-01

In this paper, the stabilization problem of actuators saturation in uncertain chaotic systems is investigated via an adaptive PID control method. The PID control parameters are auto-tuned adaptively via adaptive control laws. A multi-level augmented error is designed to account for the extra terms appearing due to the use of PID and saturation. The proposed control technique uses both the state-feedback and the output-feedback methodologies. Based on Lyapunov׳s stability theory, new anti-windup adaptive controllers are proposed. Demonstrative examples with MATLAB simulations are studied. The simulation results show the efficiency of the proposed adaptive PID controllers. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Cluster synchronization transmission of different external signals in discrete uncertain network

Science.gov (United States)

Li, Chengren; Lü, Ling; Chen, Liansong; Hong, Yixuan; Zhou, Shuang; Yang, Yiming

2018-07-01

We research cluster synchronization transmissions of different external signals in discrete uncertain network. Based on the Lyapunov theorem, the network controller and the identification law of uncertain adjustment parameter are designed, and they are efficiently used to achieve the cluster synchronization and the identification of uncertain adjustment parameter. In our technical scheme, the network nodes in each cluster and the transmitted external signal can be different, and they allow the presence of uncertain parameters in the network. Especially, we are free to choose the clustering topologies, the cluster number and the node number in each cluster.

MAGDM linear-programming models with distinct uncertain preference structures.

Science.gov (United States)

Xu, Zeshui S; Chen, Jian

2008-10-01

Group decision making with preference information on alternatives is an interesting and important research topic which has been receiving more and more attention in recent years. The purpose of this paper is to investigate multiple-attribute group decision-making (MAGDM) problems with distinct uncertain preference structures. We develop some linear-programming models for dealing with the MAGDM problems, where the information about attribute weights is incomplete, and the decision makers have their preferences on alternatives. The provided preference information can be represented in the following three distinct uncertain preference structures: 1) interval utility values; 2) interval fuzzy preference relations; and 3) interval multiplicative preference relations. We first establish some linear-programming models based on decision matrix and each of the distinct uncertain preference structures and, then, develop some linear-programming models to integrate all three structures of subjective uncertain preference information provided by the decision makers and the objective information depicted in the decision matrix. Furthermore, we propose a simple and straightforward approach in ranking and selecting the given alternatives. It is worth pointing out that the developed models can also be used to deal with the situations where the three distinct uncertain preference structures are reduced to the traditional ones, i.e., utility values, fuzzy preference relations, and multiplicative preference relations. Finally, we use a practical example to illustrate in detail the calculation process of the developed approach.

Adaptive Neural Output Feedback Control for Uncertain Robot Manipulators with Input Saturation

Directory of Open Access Journals (Sweden)

Rong Mei

2017-01-01

Full Text Available This paper presents an adaptive neural output feedback control scheme for uncertain robot manipulators with input saturation using the radial basis function neural network (RBFNN and disturbance observer. First, the RBFNN is used to approximate the system uncertainty, and the unknown approximation error of the RBFNN and the time-varying unknown external disturbance of robot manipulators are integrated as a compounded disturbance. Then, the state observer and the disturbance observer are proposed to estimate the unmeasured system state and the unknown compounded disturbance based on RBFNN. At the same time, the adaptation technique is employed to tackle the control input saturation problem. Utilizing the estimate outputs of the RBFNN, the state observer, and the disturbance observer, the adaptive neural output feedback control scheme is developed for robot manipulators using the backstepping technique. The convergence of all closed-loop signals is rigorously proved via Lyapunov analysis and the asymptotically convergent tracking error is obtained under the integrated effect of the system uncertainty, the unmeasured system state, the unknown external disturbance, and the input saturation. Finally, numerical simulation results are presented to illustrate the effectiveness of the proposed adaptive neural output feedback control scheme for uncertain robot manipulators.

Uncertain relational reasoning in the parietal cortex.

Science.gov (United States)

Ragni, Marco; Franzmeier, Imke; Maier, Simon; Knauff, Markus

2016-04-01

The psychology of reasoning is currently transitioning from the study of deductive inferences under certainty to inferences that have degrees of uncertainty in both their premises and conclusions; however, only a few studies have explored the cortical basis of uncertain reasoning. Using transcranial magnetic stimulation (TMS), we show that areas in the right superior parietal lobe (rSPL) are necessary for solving spatial relational reasoning problems under conditions of uncertainty. Twenty-four participants had to decide whether a single presented order of objects agreed with a given set of indeterminate premises that could be interpreted in more than one way. During the presentation of the order, 10-Hz TMS was applied over the rSPL or a sham control site. Right SPL TMS during the inference phase disrupted performance in uncertain relational reasoning. Moreover, we found differences in the error rates between preferred mental models, alternative models, and inconsistent models. Our results suggest that different mechanisms are involved when people reason spatially and evaluate different kinds of uncertain conclusions. Copyright © 2016 Elsevier Inc. All rights reserved.

Truly random dynamics generated by autonomous dynamical systems

Science.gov (United States)

González, J. A.; Reyes, L. I.

2001-09-01

We investigate explicit functions that can produce truly random numbers. We use the analytical properties of the explicit functions to show that a certain class of autonomous dynamical systems can generate random dynamics. This dynamics presents fundamental differences with the known chaotic systems. We present real physical systems that can produce this kind of random time-series. Some applications are discussed.

Variable-structure approaches analysis, simulation, robust control and estimation of uncertain dynamic processes

CERN Document Server

Senkel, Luise

2016-01-01

This edited book aims at presenting current research activities in the field of robust variable-structure systems. The scope equally comprises highlighting novel methodological aspects as well as presenting the use of variable-structure techniques in industrial applications including their efficient implementation on hardware for real-time control. The target audience primarily comprises research experts in the field of control theory and nonlinear dynamics but the book may also be beneficial for graduate students.

Uncertain sightings and the extinction of the Ivory-billed Woodpecker.

Science.gov (United States)

Solow, Andrew; Smith, Woollcott; Burgman, Mark; Rout, Tracy; Wintle, Brendan; Roberts, David

2012-02-01

The extinction of a species can be inferred from a record of its sightings. Existing methods for doing so assume that all sightings in the record are valid. Often, however, there are sightings of uncertain validity. To date, uncertain sightings have been treated in an ad hoc way, either excluding them from the record or including them as if they were certain. We developed a Bayesian method that formally accounts for such uncertain sightings. The method assumes that valid and invalid sightings follow independent Poisson processes and use noninformative prior distributions for the rate of valid sightings and for a measure of the quality of uncertain sightings. We applied the method to a recently published record of sightings of the Ivory-billed Woodpecker (Campephilus principalis). This record covers the period 1897-2010 and contains 39 sightings classified as certain and 29 classified as uncertain. The Bayes factor in favor of extinction was 4.03, which constitutes substantial support for extinction. The posterior distribution of the time of extinction has 3 main modes in 1944, 1952, and 1988. The method can be applied to sighting records of other purportedly extinct species. ©2011 Society for Conservation Biology.

R&D Collaboration with Uncertain Intellectual Property Rights

DEFF Research Database (Denmark)

Czarnitzki, Dirk; Hussinger, Katrin; Schneider, Cédric

2015-01-01

—uncertain intellectual property rights (IPRs) lead to reduced collaboration between firms and can, hence, hinder knowledge production. This has implications for technology policy as R&D collaborations are exempt from antitrust legislation in order to increase R&D in the economy. We argue that a functional IPR system......Patent pendencies create uncertainty in research and development (R&D) collaboration, which can result in a threat of expropriation of unprotected knowledge, reduced bargaining power and enhanced search costs. We show that—depending of the type of collaboration partner and the size of the company...

Information Extraction, Data Integration, and Uncertain Data Management: The State of The Art

NARCIS (Netherlands)

Habib, Mena Badieh; van Keulen, Maurice

2011-01-01

Information Extraction, data Integration, and uncertain data management are different areas of research that got vast focus in the last two decades. Many researches tackled those areas of research individually. However, information extraction systems should have integrated with data integration

SPECT imaging of dopaminergic system: a preliminary study of nine patients with clinically uncertain Parkinsonism

International Nuclear Information System (INIS)

Dey, S. K.; Gopinath, G.; Buscombe, J. R.

2004-01-01

Parkinsonism is the result of various neuro degenerative disorders, the common and related causes are Parkinson's disease (PD), multiple system atrophy (MSA) and progressive supranuclear palsy (PSP). In each of these three causes, there is degeneration of presynaptic neurons in corpus striatum. Nine patients having clinically uncertain parkinsonian symptoms undergone brain SPECT imaging using the tracer (I-123 Ioflupane) that binds to dopamine transporter (DaT) in the pre-synaptic nerve terminals in basal ganglia. There was significantly decreased tracer uptake in the tail (putamen) portion of basal ganglia in five patients confirming presence of presynaptic neuro degeneration and reported as parkinsonism. Three patients revealed normal tracer uptake with one equivocal result. DaT imaging can effectively confirm parkinsonism and discriminate from normal subjects as well as other clinical simulators like essential tremor and dopa-responsive dystonia where no neuro degeneration occur.(author)

What are System Dynamics Insights?

OpenAIRE

Stave, K.; Zimmermann, N. S.; Kim, H.

2016-01-01

This paper explores the concept of system dynamics insights. In our field, the term “insight” is generally understood to mean dynamic insight, that is, a deep understanding about the relationship between structure and behavior. We argue this is only one aspect of the range of insights possible from system dynamics activities, and describe a broader range of potential system dynamics insights. We also propose an initial framework for discussion that relates different types of system dynamics a...

Analysing the uncertain future of copper with three exploratory system dynamics models

NARCIS (Netherlands)

Auping, W.; Pruyt, E.; Kwakkel, J.H.

2012-01-01

High copper prices, the prospect of a transition to a more sustainable energy mix and increasing copper demands from emerging economies have not led to an in-creased attention to the base metal copper in mineral scarcity discussions. The copper system is well documented, but especially regarding the

Entity resolution for uncertain data

NARCIS (Netherlands)

Ayat, N.; Akbarinia, R.; Afsarmanesh, H.; Valduriez, P.

2012-01-01

Entity resolution (ER), also known as duplicate detection or record matching, is the problem of identifying the tuples that represent the same real world entity. In this paper, we address the problem of ER for uncertain data, which we call ERUD. We propose two different approaches for the ERUD

Technology Proliferation: Acquisition Strategies and Opportunities for an Uncertain Future

Science.gov (United States)

2018-04-20

COVERED (From - To) 07/31/17 to 04/09/18 Technology Proliferation: Acquisition Strategies and Opportunities for an Uncertain Future Colonel Heather A...efficient and expeditious fielding of technologically superior capabilities. In today’s environment, it is commonplace for private industry to be the...first to develop and deploy technologies that can be adopted for defense systems. The result is that the Department of Defense (DoD) is largely a

Polymorphic Uncertain Linear Programming for Generalized Production Planning Problems

Directory of Open Access Journals (Sweden)

Xinbo Zhang

2014-01-01

Full Text Available A polymorphic uncertain linear programming (PULP model is constructed to formulate a class of generalized production planning problems. In accordance with the practical environment, some factors such as the consumption of raw material, the limitation of resource and the demand of product are incorporated into the model as parameters of interval and fuzzy subsets, respectively. Based on the theory of fuzzy interval program and the modified possibility degree for the order of interval numbers, a deterministic equivalent formulation for this model is derived such that a robust solution for the uncertain optimization problem is obtained. Case study indicates that the constructed model and the proposed solution are useful to search for an optimal production plan for the polymorphic uncertain generalized production planning problems.

Analysing stakeholder power dynamics in multi-stakeholder processes : insights of practice from Africa and Asia

NARCIS (Netherlands)

Brouwer, J.H.; Hiemstra, W.; Vugt, van S.M.; Walters, H.

2013-01-01

This paper examines different practical methods for stakeholders to analyse power dynamics in multi-stakeholders processes (MSPs), taking into account the ambiguous and uncertain nature of complex adaptive systems. It reflects on an action learning programme which focused on 12 cases in Africa and

Spaces of Dynamical Systems

CERN Document Server

Pilyugin, Sergei Yu

2012-01-01

Dynamical systems are abundant in theoretical physics and engineering. Their understanding, with sufficient mathematical rigor, is vital to solving many problems. This work conveys the modern theory of dynamical systems in a didactically developed fashion.In addition to topological dynamics, structural stability and chaotic dynamics, also generic properties and pseudotrajectories are covered, as well as nonlinearity. The author is an experienced book writer and his work is based on years of teaching.

Bounding approaches to system identification

CERN Document Server

Norton, John; Piet-Lahanier, Hélène; Walter, Éric

1996-01-01

In response to the growing interest in bounding error approaches, the editors of this volume offer the first collection of papers to describe advances in techniques and applications of bounding of the parameters, or state variables, of uncertain dynamical systems. Contributors explore the application of the bounding approach as an alternative to the probabilistic analysis of such systems, relating its importance to robust control-system design.

H∞ control for uncertain linear system over networks with Bernoulli data dropout and actuator saturation.

Science.gov (United States)

Yu, Jimin; Yang, Chenchen; Tang, Xiaoming; Wang, Ping

2018-03-01

This paper investigates the H ∞ control problems for uncertain linear system over networks with random communication data dropout and actuator saturation. The random data dropout process is modeled by a Bernoulli distributed white sequence with a known conditional probability distribution and the actuator saturation is confined in a convex hull by introducing a group of auxiliary matrices. By constructing a quadratic Lyapunov function, effective conditions for the state feedback-based H ∞ controller and the observer-based H ∞ controller are proposed in the form of non-convex matrix inequalities to take the random data dropout and actuator saturation into consideration simultaneously, and the problem of non-convex feasibility is solved by applying cone complementarity linearization (CCL) procedure. Finally, two simulation examples are given to demonstrate the effectiveness of the proposed new design techniques. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Stability of dynamical systems

CERN Document Server

Liao, Xiaoxin; Yu, P 0

2007-01-01

The main purpose of developing stability theory is to examine dynamic responses of a system to disturbances as the time approaches infinity. It has been and still is the object of intense investigations due to its intrinsic interest and its relevance to all practical systems in engineering, finance, natural science and social science. This monograph provides some state-of-the-art expositions of major advances in fundamental stability theories and methods for dynamic systems of ODE and DDE types and in limit cycle, normal form and Hopf bifurcation control of nonlinear dynamic systems.ʺ Presents

Assessing innovation in emerging energy technologies: Socio-technical dynamics of carbon capture and storage (CCS) and enhanced geothermal systems (EGS) in the USA

International Nuclear Information System (INIS)

Stephens, Jennie C.; Jiusto, Scott

2010-01-01

This study applies a socio-technical systems perspective to explore innovation dynamics of two emerging energy technologies with potential to reduce greenhouse gas emissions from electrical power generation in the United States: carbon capture and storage (CCS) and enhanced geothermal systems (EGS). The goal of the study is to inform sustainability science theory and energy policy deliberations by examining how social and political dynamics are shaping the struggle for resources by these two emerging, not-yet-widely commercializable socio-technical systems. This characterization of socio-technical dynamics of CCS and EGS innovation includes examining the perceived technical, environmental, and financial risks and benefits of each system, as well as the discourses and actor networks through which the competition for resources - particularly public resources - is being waged. CCS and EGS were selected for the study because they vary considerably with respect to their social, technical, and environmental implications and risks, are unproven at scale and uncertain with respect to cost, feasibility, and life-cycle environmental impacts. By assessing the two technologies in parallel, the study highlights important social and political dimensions of energy technology innovation in order to inform theory and suggest new approaches to policy analysis.

Minimizing the regrets of long-term urban floodplain management decisions under deeply uncertain climate change

Science.gov (United States)

Hecht, J. S.; Kirshen, P. H.; Vogel, R. M.

2016-12-01

Making long-term floodplain management decisions under uncertain climate change is a major urban planning challenge of the 21stcentury. To support these efforts, we introduce a screening-level optimization model that identifies adaptation portfolios by minimizing the regrets associated with their flood-control and damage costs under different climate change trajectories that are deeply uncertain, i.e. have probabilities that cannot be specified plausibly. This mixed integer program explicitly considers the coupled damage-reduction impacts of different floodwall designs and property-scale investments (first-floor elevation, wet floodproofing of basements, permanent retreat and insurance), recommends implementation schedules, and assesses impacts to stakeholders residing in three types of homes. An application to a stylized municipality illuminates many nonlinear system dynamics stemming from large fixed capital costs, infrastructure design thresholds, and discharge-depth-damage relationships. If stakeholders tolerate mild damage, floodwalls that fully protect a community from large design events are less cost-effective than portfolios featuring both smaller floodwalls and property-scale measures. Potential losses of property tax revenue from permanent retreat motivate municipal property-tax initiatives for adaptation financing. Yet, insurance incentives for first-floor elevation may discourage locally financed floodwalls, in turn making lower-income residents more vulnerable to severe flooding. A budget constraint analysis underscores the benefits of flexible floodwall designs with low incremental expansion costs while near-optimal solutions demonstrate the scheduling flexibility of many property-scale measures. Finally, an equity analysis shows the importance of evaluating the overpayment and under-design regrets of recommended adaptation portfolios for each stakeholder and contrasts them to single-scenario model results.

An artificial bee colony algorithm for uncertain portfolio selection.

Science.gov (United States)

Chen, Wei

2014-01-01

Portfolio selection is an important issue for researchers and practitioners. In this paper, under the assumption that security returns are given by experts' evaluations rather than historical data, we discuss the portfolio adjusting problem which takes transaction costs and diversification degree of portfolio into consideration. Uncertain variables are employed to describe the security returns. In the proposed mean-variance-entropy model, the uncertain mean value of the return is used to measure investment return, the uncertain variance of the return is used to measure investment risk, and the entropy is used to measure diversification degree of portfolio. In order to solve the proposed model, a modified artificial bee colony (ABC) algorithm is designed. Finally, a numerical example is given to illustrate the modelling idea and the effectiveness of the proposed algorithm.

Learning (from) the errors of a systems biology model.

Science.gov (United States)

Engelhardt, Benjamin; Frőhlich, Holger; Kschischo, Maik

2016-02-11

Mathematical modelling is a labour intensive process involving several iterations of testing on real data and manual model modifications. In biology, the domain knowledge guiding model development is in many cases itself incomplete and uncertain. A major problem in this context is that biological systems are open. Missed or unknown external influences as well as erroneous interactions in the model could thus lead to severely misleading results. Here we introduce the dynamic elastic-net, a data driven mathematical method which automatically detects such model errors in ordinary differential equation (ODE) models. We demonstrate for real and simulated data, how the dynamic elastic-net approach can be used to automatically (i) reconstruct the error signal, (ii) identify the target variables of model error, and (iii) reconstruct the true system state even for incomplete or preliminary models. Our work provides a systematic computational method facilitating modelling of open biological systems under uncertain knowledge.

An Artificial Bee Colony Algorithm for Uncertain Portfolio Selection

OpenAIRE

Chen, Wei

2014-01-01

Portfolio selection is an important issue for researchers and practitioners. In this paper, under the assumption that security returns are given by experts’ evaluations rather than historical data, we discuss the portfolio adjusting problem which takes transaction costs and diversification degree of portfolio into consideration. Uncertain variables are employed to describe the security returns. In the proposed mean-variance-entropy model, the uncertain mean value of the return is ...

Minimal-Approximation-Based Distributed Consensus Tracking of a Class of Uncertain Nonlinear Multiagent Systems With Unknown Control Directions.

Science.gov (United States)

Choi, Yun Ho; Yoo, Sung Jin

2017-03-28

A minimal-approximation-based distributed adaptive consensus tracking approach is presented for strict-feedback multiagent systems with unknown heterogeneous nonlinearities and control directions under a directed network. Existing approximation-based consensus results for uncertain nonlinear multiagent systems in lower-triangular form have used multiple function approximators in each local controller to approximate unmatched nonlinearities of each follower. Thus, as the follower's order increases, the number of the approximators used in its local controller increases. However, the proposed approach employs only one function approximator to construct the local controller of each follower regardless of the order of the follower. The recursive design methodology using a new error transformation is derived for the proposed minimal-approximation-based design. Furthermore, a bounding lemma on parameters of Nussbaum functions is presented to handle the unknown control direction problem in the minimal-approximation-based distributed consensus tracking framework and the stability of the overall closed-loop system is rigorously analyzed in the Lyapunov sense.

Quantify uncertain emergency search techniques (QUEST) -- Theory and user's guide

International Nuclear Information System (INIS)

Johnson, M.M.; Goldsby, M.E.; Plantenga, T.D.; Porter, T.L.; West, T.H.; Wilcox, W.B.; Hensley, W.K.

1998-01-01

As recent world events show, criminal and terrorist access to nuclear materials is a growing national concern. The national laboratories are taking the lead in developing technologies to counter these potential threats to the national security. Sandia National laboratories, with support from Pacific Northwest National Laboratory and the Bechtel Nevada, Remote Sensing Laboratory, has developed QUEST (a model to Quantify Uncertain Emergency Search Techniques), to enhance the performance of organizations in the search for lost or stolen nuclear material. In addition, QUEST supports a wide range of other applications, such as environmental monitoring, nuclear facilities inspections, and searcher training. QUEST simulates the search for nuclear materials and calculates detector response for various source types and locations. The probability of detecting a radioactive source during a search is a function of many different variables, including source type, search location and structure geometry (including shielding), search dynamics (path and speed), and detector type and size. Through calculation of dynamic detector response, QUEST makes possible quantitative comparisons of various sensor technologies and search patterns. The QUEST model can be used as a tool to examine the impact of new detector technologies, explore alternative search concepts, and provide interactive search/inspector training

Uncertain hybrid model for the response calculation of an alternator

International Nuclear Information System (INIS)

Kuczkowiak, Antoine

2014-01-01

The complex structural dynamic behavior of alternator must be well understood in order to insure their reliable and safe operation. The numerical model is however difficult to construct mainly due to the presence of a high level of uncertainty. The objective of this work is to provide decision support tools in order to assess the vibratory levels in operation before to restart the alternator. Based on info-gap theory, a first decision support tool is proposed: the objective here is to assess the robustness of the dynamical response to the uncertain modal model. Based on real data, the calibration of an info-gap model of uncertainty is also proposed in order to enhance its fidelity to reality. Then, the extended constitutive relation error is used to expand identified mode shapes which are used to assess the vibratory levels. The robust expansion process is proposed in order to obtain robust expanded mode shapes to parametric uncertainties. In presence of lack-of knowledge, the trade-off between fidelity-to-data and robustness-to-uncertainties which expresses that robustness improves as fidelity deteriorates is emphasized on an industrial structure by using both reduced order model and surrogate model techniques. (author)

Flexibility configurations and preventive maintenance impact on job-shop manufacturing systems

OpenAIRE

Paolo Renna

2017-01-01

Manufacturing systems need to be able to work under the dynamic and uncertain production environment. Machine and routing flexibility combined with preventive maintenance actions can improve the performance of the manufacturing systems under dynamic conditions. This paper evaluates different levels of machine and routing flexibility combined with different degrees of preventive maintenance policy. The performance measures considered are throughput, work in process and throughput. The performa...

Tsallis’ non-extensive free energy as a subjective value of an uncertain reward

Science.gov (United States)

Takahashi, Taiki

2009-03-01

Recent studies in neuroeconomics and econophysics revealed the importance of reward expectation in decision under uncertainty. Behavioral neuroeconomic studies have proposed that the unpredictability and the probability of an uncertain reward are distinctly encoded as entropy and a distorted probability weight, respectively, in the separate neural systems. However, previous behavioral economic and decision-theoretic models could not quantify reward-seeking and uncertainty aversion in a theoretically consistent manner. In this paper, we have: (i) proposed that generalized Helmholtz free energy in Tsallis’ non-extensive thermostatistics can be utilized to quantify a perceived value of an uncertain reward, and (ii) empirically examined the explanatory powers of the models. Future study directions in neuroeconomics and econophysics by utilizing the Tsallis’ free energy model are discussed.

Millennial Teachers and Multiculturalism: Considerations for Teaching in Uncertain Times

Science.gov (United States)

Hallman, Heidi L.

2017-01-01

Purpose: This paper aims to explore the intersection of generational traits of millennial teachers, multiculturalism and teaching in an era of Uncertain Times. Uncertain Times, as a framework for the paper, characterizes changing aspects of the current era in which we live, such as the rise of the internet and interconnectivity, globalization and…

Multiple point statistical simulation using uncertain (soft) conditional data

Science.gov (United States)

Hansen, Thomas Mejer; Vu, Le Thanh; Mosegaard, Klaus; Cordua, Knud Skou

2018-05-01

Geostatistical simulation methods have been used to quantify spatial variability of reservoir models since the 80s. In the last two decades, state of the art simulation methods have changed from being based on covariance-based 2-point statistics to multiple-point statistics (MPS), that allow simulation of more realistic Earth-structures. In addition, increasing amounts of geo-information (geophysical, geological, etc.) from multiple sources are being collected. This pose the problem of integration of these different sources of information, such that decisions related to reservoir models can be taken on an as informed base as possible. In principle, though difficult in practice, this can be achieved using computationally expensive Monte Carlo methods. Here we investigate the use of sequential simulation based MPS simulation methods conditional to uncertain (soft) data, as a computational efficient alternative. First, it is demonstrated that current implementations of sequential simulation based on MPS (e.g. SNESIM, ENESIM and Direct Sampling) do not account properly for uncertain conditional information, due to a combination of using only co-located information, and a random simulation path. Then, we suggest two approaches that better account for the available uncertain information. The first make use of a preferential simulation path, where more informed model parameters are visited preferentially to less informed ones. The second approach involves using non co-located uncertain information. For different types of available data, these approaches are demonstrated to produce simulation results similar to those obtained by the general Monte Carlo based approach. These methods allow MPS simulation to condition properly to uncertain (soft) data, and hence provides a computationally attractive approach for integration of information about a reservoir model.

On-site Identification of Dynamic Annular Seal Forces in Turbo Machinery Using Active Magnetic Bearings - An Experimental Investigation

DEFF Research Database (Denmark)

Lauridsen, Jonas S.; Santos, Ilmar F.

2017-01-01

Significant dynamic forces can be generated by annular seals in rotordynamics and can under certain conditions destabilize the system leading to machine failure. Mathematical modelling of dynamic seal forces are still challenging, especially for multiphase fluids and for seals with complex...... geometries. This results in much uncertainty in the estimation of the dynamic seal forces which often leads to unexpected system behaviour. This paper presents the results of a method suitable for on-site identification of uncertain dynamic annular seal forces in rotordynamic systems supported by Active...... Magnetic Bearings (AMB). An excitation current is applied through the AMBs to obtain perturbation forces and a system response, from which, the seal coefficients are extracted by utilizing optimization and a-priori information about the mathematical model structure and its known system dynamics. As a study...

The role of uncertain self-esteem in self-handicapping.

Science.gov (United States)

Harris, R N; Snyder, C R

1986-08-01

In this article, the hypothesis that some individuals confronted with an intellectual evaluation use a lack of preparation as a "self-handicapping" strategy (Jones & Berglas, 1978) was studied. Sex and both level and certainty of self-esteem were examined in regard to the self-handicapping strategy of lack of effort. Subjects were 54 men and 54 women, certain and uncertain, high and low self-esteem college students, who believed that the experiment was designed to update local norms for a nonverbal test of intellectual ability. After subjects' level of state anxiety was assessed, they were instructed in the benefits of practicing for the evaluation. Subsequently, subjects' state anxiety and preparatory efforts (the primary dependent variables) were measured. Subjects' practice, self-protective attributions, and related affect supported a self-handicapping interpretation for uncertain males but not for uncertain females.

A Comparison of the Mathematical Modeling Methods in the Inventory Systems under Uncertain Conditions

OpenAIRE

A. Mirzazadeh

2011-01-01

The inventory models, generally, are derived with considering two methods: (1) minimizing the average annual cost or (2) minimizing the discounted cost. This paper compares the optimal ordering policies determined by these methods under uncertain inflationary situations. The inventory and shortages behavior have been analyzed with using the differential equations. The numerical examples are used to illustrate the theoretical results. A detailed analysis on the models parameters has been perfo...

Quality Measures in Uncertain Data Management

NARCIS (Netherlands)

de Keijzer, Ander; van Keulen, Maurice; Prade, H.; Subrahmanian, V.S.

2007-01-01

Many applications deal with data that is uncertain. Some examples are applications dealing with sensor information, data integration applications and healthcare applications. Instead of these applications having to deal with the uncertainty, it should be the responsibility of the DBMS to manage all

Nonlinear dynamics non-integrable systems and chaotic dynamics

CERN Document Server

Borisov, Alexander

2017-01-01

This monograph reviews advanced topics in the area of nonlinear dynamics. Starting with theory of integrable systems – including methods to find and verify integrability – the remainder of the book is devoted to non-integrable systems with an emphasis on dynamical chaos. Topics include structural stability, mechanisms of emergence of irreversible behaviour in deterministic systems as well as chaotisation occurring in dissipative systems.

Distributed adaptive asymptotically consensus tracking control of uncertain Euler-Lagrange systems under directed graph condition.

Science.gov (United States)

Wang, Wei; Wen, Changyun; Huang, Jiangshuai; Fan, Huijin

2017-11-01

In this paper, a backstepping based distributed adaptive control scheme is proposed for multiple uncertain Euler-Lagrange systems under directed graph condition. The common desired trajectory is allowed totally unknown by part of the subsystems and the linearly parameterized trajectory model assumed in currently available results is no longer needed. To compensate the effects due to unknown trajectory information, a smooth function of consensus errors and certain positive integrable functions are introduced in designing virtual control inputs. Besides, to overcome the difficulty of completely counteracting the coupling terms of distributed consensus errors and parameter estimation errors in the presence of asymmetric Laplacian matrix, extra information transmission of local parameter estimates are introduced among linked subsystem and adaptive gain technique is adopted to generate distributed torque inputs. It is shown that with the proposed distributed adaptive control scheme, global uniform boundedness of all the closed-loop signals and asymptotically output consensus tracking can be achieved. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Dynamics of Financial System: A System Dynamics Approach

OpenAIRE

Girish K. Nair; Lewlyn Lester Raj Rodrigues

2013-01-01

There are several ratios which define the financial health of an organization but the importance of Net cash flow, Gross income, Net income, Pending bills, Receivable bills, Debt, and Book value can never be undermined as they give the exact picture of the financial condition. While there are several approaches to study the dynamics of these variables, system dynamics based modelling and simulation is one of the modern techniques. The paper explores this method to simulate the before mentione...

Optimization and analysis of the current control loop of VSCs connected to uncertain grids through LCL filters

OpenAIRE

Cóbreces Álvarez, Santiago

2009-01-01

Premio Extraordinario de Doctorado 2011 This thesis focuses on the design and analysis of the control of voltage source converters connected to the grid through LCL filters. Particularly it is centered on grids presenting uncertainty in their intrinsic dynamic parameters and their influence over the inner control loop of a grid converter: the current control. To that end, the thesis follows a three-fold discussion. Firstly, the thesis studies the grid model, its uncertain parameters and pr...

Robust scheduling in an uncertain environment

NARCIS (Netherlands)

Wilson, M.

2016-01-01

This thesis presents research on scheduling in an uncertain environment, which forms a part of the rolling stock life cycle logistics applied research and development program funded by Dutch railway industry companies. The focus therefore lies on scheduling of maintenance operations on rolling stock

Strong practical stability and stabilization of uncertain discrete linear repetitive processes

Czech Academy of Sciences Publication Activity Database

Dabkowski, Pavel; Galkowski, K.; Bachelier, O.; Rogers, E.; Kummert, A.; Lam, J.

2013-01-01

Roč. 20, č. 2 (2013), s. 220-233 ISSN 1070-5325 R&D Projects: GA MŠk(CZ) 1M0567 Institutional research plan: CEZ:AV0Z10750506 Institutional support: RVO:67985556 Keywords : strong practical stability * stabilization * uncertain discrete linear repetitive processes * linear matrix inequality Subject RIV: BC - Control Systems Theory Impact factor: 1.424, year: 2013 http://onlinelibrary.wiley.com/doi/10.1002/nla.812/abstract

Self-Supervised Dynamical Systems

Science.gov (United States)

Zak, Michail

2003-01-01

Some progress has been made in a continuing effort to develop mathematical models of the behaviors of multi-agent systems known in biology, economics, and sociology (e.g., systems ranging from single or a few biomolecules to many interacting higher organisms). Living systems can be characterized by nonlinear evolution of probability distributions over different possible choices of the next steps in their motions. One of the main challenges in mathematical modeling of living systems is to distinguish between random walks of purely physical origin (for instance, Brownian motions) and those of biological origin. Following a line of reasoning from prior research, it has been assumed, in the present development, that a biological random walk can be represented by a nonlinear mathematical model that represents coupled mental and motor dynamics incorporating the psychological concept of reflection or self-image. The nonlinear dynamics impart the lifelike ability to behave in ways and to exhibit patterns that depart from thermodynamic equilibrium. Reflection or self-image has traditionally been recognized as a basic element of intelligence. The nonlinear mathematical models of the present development are denoted self-supervised dynamical systems. They include (1) equations of classical dynamics, including random components caused by uncertainties in initial conditions and by Langevin forces, coupled with (2) the corresponding Liouville or Fokker-Planck equations that describe the evolutions of probability densities that represent the uncertainties. The coupling is effected by fictitious information-based forces, denoted supervising forces, composed of probability densities and functionals thereof. The equations of classical mechanics represent motor dynamics that is, dynamics in the traditional sense, signifying Newton s equations of motion. The evolution of the probability densities represents mental dynamics or self-image. Then the interaction between the physical and

Fuzzy parametric uncertainty analysis of linear dynamical systems: A surrogate modeling approach

Science.gov (United States)

Chowdhury, R.; Adhikari, S.

2012-10-01

Uncertainty propagation engineering systems possess significant computational challenges. This paper explores the possibility of using correlated function expansion based metamodelling approach when uncertain system parameters are modeled using Fuzzy variables. In particular, the application of High-Dimensional Model Representation (HDMR) is proposed for fuzzy finite element analysis of dynamical systems. The HDMR expansion is a set of quantitative model assessment and analysis tools for capturing high-dimensional input-output system behavior based on a hierarchy of functions of increasing dimensions. The input variables may be either finite-dimensional (i.e., a vector of parameters chosen from the Euclidean space RM) or may be infinite-dimensional as in the function space CM[0,1]. The computational effort to determine the expansion functions using the alpha cut method scales polynomially with the number of variables rather than exponentially. This logic is based on the fundamental assumption underlying the HDMR representation that only low-order correlations among the input variables are likely to have significant impacts upon the outputs for most high-dimensional complex systems. The proposed method is integrated with a commercial Finite Element software. Modal analysis of a simplified aircraft wing with Fuzzy parameters has been used to illustrate the generality of the proposed approach. In the numerical examples, triangular membership functions have been used and the results have been validated against direct Monte Carlo simulations.

Globally Stable Adaptive Backstepping Neural Network Control for Uncertain Strict-Feedback Systems With Tracking Accuracy Known a Priori.

Science.gov (United States)

Chen, Weisheng; Ge, Shuzhi Sam; Wu, Jian; Gong, Maoguo

2015-09-01

This paper addresses the problem of globally stable direct adaptive backstepping neural network (NN) tracking control design for a class of uncertain strict-feedback systems under the assumption that the accuracy of the ultimate tracking error is given a priori. In contrast to the classical adaptive backstepping NN control schemes, this paper analyzes the convergence of the tracking error using Barbalat's Lemma via some nonnegative functions rather than the positive-definite Lyapunov functions. Thus, the accuracy of the ultimate tracking error can be determined and adjusted accurately a priori, and the closed-loop system is guaranteed to be globally uniformly ultimately bounded. The main technical novelty is to construct three new n th-order continuously differentiable functions, which are used to design the control law, the virtual control variables, and the adaptive laws. Finally, two simulation examples are given to illustrate the effectiveness and advantages of the proposed control method.

Allowances for evolving coastal flood risk under uncertain local sea-level rise

Science.gov (United States)

Buchanan, M. K.; Kopp, R. E.; Oppenheimer, M.; Tebaldi, C.

2015-12-01

Sea-level rise (SLR) causes estimates of flood risk made under the assumption of stationary mean sea level to be biased low. However, adjustments to flood return levels made assuming fixed increases of sea level are also inaccurate when applied to sea level that is rising over time at an uncertain rate. To accommodate both the temporal dynamics of SLR and their uncertainty, we develop an Average Annual Design Life Level (AADLL) metric and associated SLR allowances [1,2]. The AADLL is the flood level corresponding to a time-integrated annual expected probability of occurrence (AEP) under uncertainty over the lifetime of an asset; AADLL allowances are the adjustment from 2000 levels that maintain current risk. Given non-stationary and uncertain SLR, AADLL flood levels and allowances provide estimates of flood protection heights and offsets for different planning horizons and different levels of confidence in SLR projections in coastal areas. Allowances are a function primarily of local SLR and are nearly independent of AEP. Here we employ probabilistic SLR projections [3] to illustrate the calculation of AADLL flood levels and allowances with a representative set of long-duration tide gauges along U.S. coastlines. [1] Rootzen et al., 2014, Water Resources Research 49: 5964-5972. [2] Hunter, 2013, Ocean Engineering 71: 17-27. [3] Kopp et al., 2014, Earth's Future 2: 383-406.

Shadowing in dynamical systems

CERN Document Server

Pilyugin, Sergei Yu

1999-01-01

This book is an introduction to the theory of shadowing of approximate trajectories in dynamical systems by exact ones. This is the first book completely devoted to the theory of shadowing. It shows the importance of shadowing theory for both the qualitative theory of dynamical systems and the theory of numerical methods. Shadowing Methods allow us to estimate differences between exact and approximate solutions on infinite time intervals and to understand the influence of error terms. The book is intended for specialists in dynamical systems, for researchers and graduate students in the theory of numerical methods.

Airline Overbooking Problem with Uncertain No-Shows

Directory of Open Access Journals (Sweden)

Chunxiao Zhang

2014-01-01

Full Text Available This paper considers an airline overbooking problem of a new single-leg flight with discount fare. Due to the absence of historical data of no-shows for a new flight, and various uncertain human behaviors or unexpected events which causes that a few passengers cannot board their aircraft on time, we fail to obtain the probability distribution of no-shows. In this case, the airlines have to invite some domain experts to provide belief degree of no-shows to estimate its distribution. However, human beings often overestimate unlikely events, which makes the variance of belief degree much greater than that of the frequency. If we still regard the belief degree as a subjective probability, the derived results will exceed our expectations. In order to deal with this uncertainty, the number of no-shows of new flight is assumed to be an uncertain variable in this paper. Given the chance constraint of social reputation, an overbooking model with discount fares is developed to maximize the profit rate based on uncertain programming theory. Finally, the analytic expression of the optimal booking limit is obtained through a numerical example, and the results of sensitivity analysis indicate that the optimal booking limit is affected by flight capacity, discount, confidence level, and parameters of the uncertainty distribution significantly.

Effective production planning for purchased part under long lead time and uncertain demand: MRP Vs demand-driven MRP

Science.gov (United States)

Shofa, M. J.; Moeis, A. O.; Restiana, N.

2018-04-01

MRP as a production planning system is appropriate for the deterministic environment. Unfortunately, most production systems such as customer demands are stochastic, so that MRP is inappropriate at the time. Demand-Driven MRP (DDMRP) is new approach for production planning system dealing with demand uncertainty. The objective of this paper is to compare the MRP and DDMRP for purchased part under long lead time and uncertain demand in terms of average inventory levels. The evaluation is conducted through a discrete event simulation with the long lead time and uncertain demand scenarios. The next step is evaluating the performance of DDMRP by comparing the inventory level of DDMRP with MRP. As result, DDMRP is more effective production planning than MRP in terms of average inventory levels.

Preparing for an Uncertain Forecast

Science.gov (United States)

Karolak, Eric

2011-01-01

Navigating the world of government relations and public policy can be a little like predicting the weather. One can't always be sure what's in store or how it will affect him/her down the road. But there are common patterns and a few basic steps that can help one best prepare for a change in the forecast. Though the forecast is uncertain, early…

A design control structure for architectural firms in a highly complex and uncertain situation

NARCIS (Netherlands)

Schijlen, J.T.H.A.M.; Otter, den A.F.H.J.; Pels, H.J.

2011-01-01

A large architectural firm in a highly complex and uncertain production situation asked to improve its existing ?production control? system for design projects. To that account a research and design project of nine months at the spot was defined. The production control in the organization was based

Possible world based consistency learning model for clustering and classifying uncertain data.

Science.gov (United States)

Liu, Han; Zhang, Xianchao; Zhang, Xiaotong

2018-06-01

Possible world has shown to be effective for handling various types of data uncertainty in uncertain data management. However, few uncertain data clustering and classification algorithms are proposed based on possible world. Moreover, existing possible world based algorithms suffer from the following issues: (1) they deal with each possible world independently and ignore the consistency principle across different possible worlds; (2) they require the extra post-processing procedure to obtain the final result, which causes that the effectiveness highly relies on the post-processing method and the efficiency is also not very good. In this paper, we propose a novel possible world based consistency learning model for uncertain data, which can be extended both for clustering and classifying uncertain data. This model utilizes the consistency principle to learn a consensus affinity matrix for uncertain data, which can make full use of the information across different possible worlds and then improve the clustering and classification performance. Meanwhile, this model imposes a new rank constraint on the Laplacian matrix of the consensus affinity matrix, thereby ensuring that the number of connected components in the consensus affinity matrix is exactly equal to the number of classes. This also means that the clustering and classification results can be directly obtained without any post-processing procedure. Furthermore, for the clustering and classification tasks, we respectively derive the efficient optimization methods to solve the proposed model. Experimental results on real benchmark datasets and real world uncertain datasets show that the proposed model outperforms the state-of-the-art uncertain data clustering and classification algorithms in effectiveness and performs competitively in efficiency. Copyright © 2018 Elsevier Ltd. All rights reserved.

Synchronization dynamics of two different dynamical systems

International Nuclear Information System (INIS)

Luo, Albert C.J.; Min Fuhong

2011-01-01

Highlights: → Synchronization dynamics of two distinct dynamical systems. → Synchronization, de-synchronization and instantaneous synchronization. → A controlled pendulum synchronizing with the Duffing oscillator. → Synchronization invariant set. → Synchronization parameter map. - Abstract: In this paper, synchronization dynamics of two different dynamical systems is investigated through the theory of discontinuous dynamical systems. The necessary and sufficient conditions for the synchronization, de-synchronization and instantaneous synchronization (penetration or grazing) are presented. Using such a synchronization theory, the synchronization of a controlled pendulum with the Duffing oscillator is systematically discussed as a sampled problem, and the corresponding analytical conditions for the synchronization are presented. The synchronization parameter study is carried out for a better understanding of synchronization characteristics of the controlled pendulum and the Duffing oscillator. Finally, the partial and full synchronizations of the controlled pendulum with periodic and chaotic motions are presented to illustrate the analytical conditions. The synchronization of the Duffing oscillator and pendulum are investigated in order to show the usefulness and efficiency of the methodology in this paper. The synchronization invariant domain is obtained. The technique presented in this paper should have a wide spectrum of applications in engineering. For example, this technique can be applied to the maneuvering target tracking, and the others.

Research on nonlinear stochastic dynamical price model

International Nuclear Information System (INIS)

Li Jiaorui; Xu Wei; Xie Wenxian; Ren Zhengzheng

2008-01-01

In consideration of many uncertain factors existing in economic system, nonlinear stochastic dynamical price model which is subjected to Gaussian white noise excitation is proposed based on deterministic model. One-dimensional averaged Ito stochastic differential equation for the model is derived by using the stochastic averaging method, and applied to investigate the stability of the trivial solution and the first-passage failure of the stochastic price model. The stochastic price model and the methods presented in this paper are verified by numerical studies

Complexity in Dynamical Systems

Science.gov (United States)

Moore, Cristopher David

The study of chaos has shown us that deterministic systems can have a kind of unpredictability, based on a limited knowledge of their initial conditions; after a finite time, the motion appears essentially random. This observation has inspired a general interest in the subject of unpredictability, and more generally, complexity; how can we characterize how "complex" a dynamical system is?. In this thesis, we attempt to answer this question with a paradigm of complexity that comes from computer science, we extract sets of symbol sequences, or languages, from a dynamical system using standard methods of symbolic dynamics; we then ask what kinds of grammars or automata are needed a generate these languages. This places them in the Chomsky heirarchy, which in turn tells us something about how subtle and complex the dynamical system's behavior is. This gives us insight into the question of unpredictability, since these automata can also be thought of as computers attempting to predict the system. In the culmination of the thesis, we find a class of smooth, two-dimensional maps which are equivalent to the highest class in the Chomsky heirarchy, the turning machine; they are capable of universal computation. Therefore, these systems possess a kind of unpredictability qualitatively different from the usual "chaos": even if the initial conditions are known exactly, questions about the system's long-term dynamics are undecidable. No algorithm exists to answer them. Although this kind of unpredictability has been discussed in the context of distributed, many-degree-of -freedom systems (for instance, cellular automata) we believe this is the first example of such phenomena in a smooth, finite-degree-of-freedom system.

Management of complex dynamical systems

Science.gov (United States)

MacKay, R. S.

2018-02-01

Complex dynamical systems are systems with many interdependent components which evolve in time. One might wish to control their trajectories, but a more practical alternative is to control just their statistical behaviour. In many contexts this would be both sufficient and a more realistic goal, e.g. climate and socio-economic systems. I refer to it as ‘management’ of complex dynamical systems. In this paper, some mathematics for management of complex dynamical systems is developed in the weakly dependent regime, and questions are posed for the strongly dependent regime.

Vehicle systems: coupled and interactive dynamics analysis

Science.gov (United States)

Vantsevich, Vladimir V.

2014-11-01

This article formulates a new direction in vehicle dynamics, described as coupled and interactive vehicle system dynamics. Formalised procedures and analysis of case studies are presented. An analytical consideration, which explains the physics of coupled system dynamics and its consequences for dynamics of a vehicle, is given for several sets of systems including: (i) driveline and suspension of a 6×6 truck, (ii) a brake mechanism and a limited slip differential of a drive axle and (iii) a 4×4 vehicle steering system and driveline system. The article introduces a formal procedure to turn coupled system dynamics into interactive dynamics of systems. A new research direction in interactive dynamics of an active steering and a hybrid-electric power transmitting unit is presented and analysed to control power distribution between the drive axles of a 4×4 vehicle. A control strategy integrates energy efficiency and lateral dynamics by decoupling dynamics of the two systems thus forming their interactive dynamics.

An efficient heuristic method for dynamic portfolio selection problem under transaction costs and uncertain conditions

Science.gov (United States)

Najafi, Amir Abbas; Pourahmadi, Zahra

2016-04-01

Selecting the optimal combination of assets in a portfolio is one of the most important decisions in investment management. As investment is a long term concept, looking into a portfolio optimization problem just in a single period may cause loss of some opportunities that could be exploited in a long term view. Hence, it is tried to extend the problem from single to multi-period model. We include trading costs and uncertain conditions to this model which made it more realistic and complex. Hence, we propose an efficient heuristic method to tackle this problem. The efficiency of the method is examined and compared with the results of the rolling single-period optimization and the buy and hold method which shows the superiority of the proposed method.

Complexified dynamical systems

International Nuclear Information System (INIS)

Bender, Carl M; Holm, Darryl D; Hook, Daniel W

2007-01-01

Many dynamical systems, such as the Lotka-Volterra predator-prey model and the Euler equations for the free rotation of a rigid body, are PT symmetric. The standard and well-known real solutions to such dynamical systems constitute an infinitessimal subclass of the full set of complex solutions. This paper examines a subset of the complex solutions that contains the real solutions, namely those having PT symmetry. The condition of PT symmetry selects out complex solutions that are periodic. (fast track communication)

Similarity estimators for irregular and age uncertain time series

Science.gov (United States)

Rehfeld, K.; Kurths, J.

2013-09-01

Paleoclimate time series are often irregularly sampled and age uncertain, which is an important technical challenge to overcome for successful reconstruction of past climate variability and dynamics. Visual comparison and interpolation-based linear correlation approaches have been used to infer dependencies from such proxy time series. While the first is subjective, not measurable and not suitable for the comparison of many datasets at a time, the latter introduces interpolation bias, and both face difficulties if the underlying dependencies are nonlinear. In this paper we investigate similarity estimators that could be suitable for the quantitative investigation of dependencies in irregular and age uncertain time series. We compare the Gaussian-kernel based cross correlation (gXCF, Rehfeld et al., 2011) and mutual information (gMI, Rehfeld et al., 2013) against their interpolation-based counterparts and the new event synchronization function (ESF). We test the efficiency of the methods in estimating coupling strength and coupling lag numerically, using ensembles of synthetic stalagmites with short, autocorrelated, linear and nonlinearly coupled proxy time series, and in the application to real stalagmite time series. In the linear test case coupling strength increases are identified consistently for all estimators, while in the nonlinear test case the correlation-based approaches fail. The lag at which the time series are coupled is identified correctly as the maximum of the similarity functions in around 60-55% (in the linear case) to 53-42% (for the nonlinear processes) of the cases when the dating of the synthetic stalagmite is perfectly precise. If the age uncertainty increases beyond 5% of the time series length, however, the true coupling lag is not identified more often than the others for which the similarity function was estimated. Age uncertainty contributes up to half of the uncertainty in the similarity estimation process. Time series irregularity

Similarity estimators for irregular and age-uncertain time series

Science.gov (United States)

Rehfeld, K.; Kurths, J.

2014-01-01

Paleoclimate time series are often irregularly sampled and age uncertain, which is an important technical challenge to overcome for successful reconstruction of past climate variability and dynamics. Visual comparison and interpolation-based linear correlation approaches have been used to infer dependencies from such proxy time series. While the first is subjective, not measurable and not suitable for the comparison of many data sets at a time, the latter introduces interpolation bias, and both face difficulties if the underlying dependencies are nonlinear. In this paper we investigate similarity estimators that could be suitable for the quantitative investigation of dependencies in irregular and age-uncertain time series. We compare the Gaussian-kernel-based cross-correlation (gXCF, Rehfeld et al., 2011) and mutual information (gMI, Rehfeld et al., 2013) against their interpolation-based counterparts and the new event synchronization function (ESF). We test the efficiency of the methods in estimating coupling strength and coupling lag numerically, using ensembles of synthetic stalagmites with short, autocorrelated, linear and nonlinearly coupled proxy time series, and in the application to real stalagmite time series. In the linear test case, coupling strength increases are identified consistently for all estimators, while in the nonlinear test case the correlation-based approaches fail. The lag at which the time series are coupled is identified correctly as the maximum of the similarity functions in around 60-55% (in the linear case) to 53-42% (for the nonlinear processes) of the cases when the dating of the synthetic stalagmite is perfectly precise. If the age uncertainty increases beyond 5% of the time series length, however, the true coupling lag is not identified more often than the others for which the similarity function was estimated. Age uncertainty contributes up to half of the uncertainty in the similarity estimation process. Time series irregularity

Nonautonomous dynamical systems

CERN Document Server

Kloeden, Peter E

2011-01-01

The theory of nonautonomous dynamical systems in both of its formulations as processes and skew product flows is developed systematically in this book. The focus is on dissipative systems and nonautonomous attractors, in particular the recently introduced concept of pullback attractors. Linearization theory, invariant manifolds, Lyapunov functions, Morse decompositions and bifurcations for nonautonomous systems and set-valued generalizations are also considered as well as applications to numerical approximations, switching systems and synchronization. Parallels with corresponding theories of control and random dynamical systems are briefly sketched. With its clear and systematic exposition, many examples and exercises, as well as its interesting applications, this book can serve as a text at the beginning graduate level. It is also useful for those who wish to begin their own independent research in this rapidly developing area.

Issues with performance measures for dynamic multi-objective optimisation

CSIR Research Space (South Africa)

Helbig, M

2013-06-01

Full Text Available Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE), Mexico, 20-23 June 2013 Issues with Performance Measures for Dynamic Multi-objective Optimisation Mard´e Helbig CSIR: Meraka Institute Brummeria, South Africa...

Chaos for Discrete Dynamical System

Directory of Open Access Journals (Sweden)

Lidong Wang

2013-01-01

Full Text Available We prove that a dynamical system is chaotic in the sense of Martelli and Wiggins, when it is a transitive distributively chaotic in a sequence. Then, we give a sufficient condition for the dynamical system to be chaotic in the strong sense of Li-Yorke. We also prove that a dynamical system is distributively chaotic in a sequence, when it is chaotic in the strong sense of Li-Yorke.

Adaptive control of an exoskeleton robot with uncertainties on kinematics and dynamics.

Science.gov (United States)

Brahmi, Brahim; Saad, Maarouf; Ochoa-Luna, Cristobal; Rahman, Mohammad H

2017-07-01

In this paper, we propose a new adaptive control technique based on nonlinear sliding mode control (JSTDE) taking into account kinematics and dynamics uncertainties. This approach is applied to an exoskeleton robot with uncertain kinematics and dynamics. The adaptation design is based on Time Delay Estimation (TDE). The proposed strategy does not necessitate the well-defined dynamic and kinematic models of the system robot. The updated laws are designed using Lyapunov-function to solve the adaptation problem systematically, proving the close loop stability and ensuring the convergence asymptotically of the outputs tracking errors. Experiments results show the effectiveness and feasibility of JSTDE technique to deal with the variation of the unknown nonlinear dynamics and kinematics of the exoskeleton model.

Synchronization of uncertain time-varying network based on sliding mode control technique

Science.gov (United States)

Lü, Ling; Li, Chengren; Bai, Suyuan; Li, Gang; Rong, Tingting; Gao, Yan; Yan, Zhe

2017-09-01

We research synchronization of uncertain time-varying network based on sliding mode control technique. The sliding mode control technique is first modified so that it can be applied to network synchronization. Further, by choosing the appropriate sliding surface, the identification law of uncertain parameter, the adaptive law of the time-varying coupling matrix element and the control input of network are designed, it is sure that the uncertain time-varying network can synchronize effectively the synchronization target. At last, we perform some numerical simulations to demonstrate the effectiveness of the proposed results.

Ergodic theory and dynamical systems

CERN Document Server

Coudène, Yves

2016-01-01

This textbook is a self-contained and easy-to-read introduction to ergodic theory and the theory of dynamical systems, with a particular emphasis on chaotic dynamics. This book contains a broad selection of topics and explores the fundamental ideas of the subject. Starting with basic notions such as ergodicity, mixing, and isomorphisms of dynamical systems, the book then focuses on several chaotic transformations with hyperbolic dynamics, before moving on to topics such as entropy, information theory, ergodic decomposition and measurable partitions. Detailed explanations are accompanied by numerous examples, including interval maps, Bernoulli shifts, toral endomorphisms, geodesic flow on negatively curved manifolds, Morse-Smale systems, rational maps on the Riemann sphere and strange attractors. Ergodic Theory and Dynamical Systems will appeal to graduate students as well as researchers looking for an introduction to the subject. While gentle on the beginning student, the book also contains a number of commen...

Practical Stabilization of Uncertain Nonholonomic Mobile Robots Based on Visual Servoing Model with Uncalibrated Camera Parameters

Directory of Open Access Journals (Sweden)

Hua Chen

2013-01-01

Full Text Available The practical stabilization problem is addressed for a class of uncertain nonholonomic mobile robots with uncalibrated visual parameters. Based on the visual servoing kinematic model, a new switching controller is presented in the presence of parametric uncertainties associated with the camera system. In comparison with existing methods, the new design method is directly used to control the original system without any state or input transformation, which is effective to avoid singularity. Under the proposed control law, it is rigorously proved that all the states of closed-loop system can be stabilized to a prescribed arbitrarily small neighborhood of the zero equilibrium point. Furthermore, this switching control technique can be applied to solve the practical stabilization problem of a kind of mobile robots with uncertain parameters (and angle measurement disturbance which appeared in some literatures such as Morin et al. (1998, Hespanha et al. (1999, Jiang (2000, and Hong et al. (2005. Finally, the simulation results show the effectiveness of the proposed controller design approach.

Dynamical Systems for Creative Technology

NARCIS (Netherlands)

van Amerongen, J.

2010-01-01

Dynamical Systems for Creative Technology gives a concise description of the physical properties of electrical, mechanical and hydraulic systems. Emphasis is placed on modelling the dynamical properties of these systems. By using a system’s approach it is shown that a limited number of mathematical

Dynamics robustness of cascading systems.

Directory of Open Access Journals (Sweden)

Jonathan T Young

2017-03-01

Full Text Available A most important property of biochemical systems is robustness. Static robustness, e.g., homeostasis, is the insensitivity of a state against perturbations, whereas dynamics robustness, e.g., homeorhesis, is the insensitivity of a dynamic process. In contrast to the extensively studied static robustness, dynamics robustness, i.e., how a system creates an invariant temporal profile against perturbations, is little explored despite transient dynamics being crucial for cellular fates and are reported to be robust experimentally. For example, the duration of a stimulus elicits different phenotypic responses, and signaling networks process and encode temporal information. Hence, robustness in time courses will be necessary for functional biochemical networks. Based on dynamical systems theory, we uncovered a general mechanism to achieve dynamics robustness. Using a three-stage linear signaling cascade as an example, we found that the temporal profiles and response duration post-stimulus is robust to perturbations against certain parameters. Then analyzing the linearized model, we elucidated the criteria of when signaling cascades will display dynamics robustness. We found that changes in the upstream modules are masked in the cascade, and that the response duration is mainly controlled by the rate-limiting module and organization of the cascade's kinetics. Specifically, we found two necessary conditions for dynamics robustness in signaling cascades: 1 Constraint on the rate-limiting process: The phosphatase activity in the perturbed module is not the slowest. 2 Constraints on the initial conditions: The kinase activity needs to be fast enough such that each module is saturated even with fast phosphatase activity and upstream changes are attenuated. We discussed the relevance of such robustness to several biological examples and the validity of the above conditions therein. Given the applicability of dynamics robustness to a variety of systems, it

The Dynamical Invariant of Open Quantum System

OpenAIRE

Wu, S. L.; Zhang, X. Y.; Yi, X. X.

2015-01-01

The dynamical invariant, whose expectation value is constant, is generalized to open quantum system. The evolution equation of dynamical invariant (the dynamical invariant condition) is presented for Markovian dynamics. Different with the dynamical invariant for the closed quantum system, the evolution of the dynamical invariant for the open quantum system is no longer unitary, and the eigenvalues of it are time-dependent. Since any hermitian operator fulfilling dynamical invariant condition ...

Functional System Dynamics

NARCIS (Netherlands)

Ligterink, N.E.

2007-01-01

Functional system dynamics is the analysis, modelling, and simulation of continuous systems usually described by partial differential equations. From the infinite degrees of freedom of such systems only a finite number of relevant variables have to be chosen for a practical model description. The

International Conference on Dynamical Systems : Theory and Applications

CERN Document Server

2016-01-01

The book is a collection of contributions devoted to analytical, numerical and experimental techniques of dynamical systems, presented at the international conference "Dynamical Systems: Theory and Applications," held in Lódz, Poland on December 7-10, 2015. The studies give deep insight into new perspectives in analysis, simulation, and optimization of dynamical systems, emphasizing directions for future research. Broadly outlined topics covered include: bifurcation and chaos in dynamical systems, asymptotic methods in nonlinear dynamics, dynamics in life sciences and bioengineering, original numerical methods of vibration analysis, control in dynamical systems, stability of dynamical systems, vibrations of lumped and continuous sytems, non-smooth systems, engineering systems and differential equations, mathematical approaches to dynamical systems, and mechatronics.

International Conference on Dynamical Systems : Theory and Applications

CERN Document Server

2016-01-01

The book is the second volume of a collection of contributions devoted to analytical, numerical and experimental techniques of dynamical systems, presented at the international conference "Dynamical Systems: Theory and Applications," held in Lódz, Poland on December 7-10, 2015. The studies give deep insight into new perspectives in analysis, simulation, and optimization of dynamical systems, emphasizing directions for future research. Broadly outlined topics covered include: bifurcation and chaos in dynamical systems, asymptotic methods in nonlinear dynamics, dynamics in life sciences and bioengineering, original numerical methods of vibration analysis, control in dynamical systems, stability of dynamical systems, vibrations of lumped and continuous sytems, non-smooth systems, engineering systems and differential equations, mathematical approaches to dynamical systems, and mechatronics.

Uncertain estimation of activity measurement in Nuclear Medicine

International Nuclear Information System (INIS)

Lopez Diaz, A.; Palau, S.P.A.; Cardenas, T.A.I.; Garcia, A.I.; Tulio, H.A.

2007-01-01

Full text: Accuracy and precision of dose is mandatory in radiopharmaceutical therapy procedures to guarantee the treatment success. The evaluation of uncertain in dose measurement in NM lab becomes a very important step, moreover if the operational parameters change between different equipment. In order to assure the traceability of activity measurements, and the quality assurance of dose administration, the behavior of two dose calibrator Capintec CRC-15R and PTW Curimentor 3, were studied. Accuracy, precision, linearity of activity response and reproducibility, and the activity uncertain determination with a second lab source were determined. Accuracy was evaluate using Tc 99m and I 131 Pstandard source (Secondary Standard Laboratory), for 10R vial (V) and 5 ml syringe (S) geometry, obtaining 1.3% (V-I 131 ), 1.4% (V -Tc 99m ), 0.8% (S -Tc 99m ) for CRC-15R and 2.3% (V-I 131 ), 1.1%(V-Tc 99m ), 1.0% (S-Tc 99m ) for Curimentor 3. Precision and Reproducibility was calculate using Cs 137 source. The reproducibility of CRC-15R and Curimentor 3 was less than 1.1% and 1.2 % respectively, during all evaluation time. The linearity of activity response was evaluate only for Tc 99m , and the results obtained were CRC 15R (672.97mCi 0.07 mCi, 1.3 % like mayor deviation) and Curimentor 3 (670.91mCi 0.08 mCi; 0.6 % like mayor deviation). To calculate the uncertain was use I 131 sources, the influences of calibration factor, linearity to activity, precision, reproducibility, background, half live time took into account. The typical combine uncertain for I 131 activity of was 2.24% for CRC-15R and 2.41% for Curimentor 3. The results were traceable between two equipment, no statistical significant differences were found for all tests. The equipment have a proper performance in the checked parameters, showing compliance with AIEA and National Authorities recommendation. Conclusion: The two equipmentcan be used in NM services with high level of traceability and confidence, with

Functional brain networks involved in decision-making under certain and uncertain conditions

Energy Technology Data Exchange (ETDEWEB)

Farrar, Danielle C.; Moss, Mark B.; Killiany, Ronald J. [Boston University School of Medicine, Department of Anatomy and Neurobiology, Boston, MA (United States); Mian, Asim Z. [Boston University School of Medicine, Department of Radiology, Boston, MA (United States); Budson, Andrew E. [VA Boston Healthcare System, Boston, MA (United States)

2018-01-15

The aim of this study was to describe imaging markers of decision-making under uncertain conditions in normal individuals, in order to provide baseline activity to compare to impaired decision-making in pathological states. In this cross-sectional study, 19 healthy subjects ages 18-35 completed a novel decision-making card-matching task using a Phillips T3 Scanner and a 32-channel head coil. Functional data were collected in six functional runs. In one condition of the task, the participant was certain of the rule to apply to match the cards; in the other condition, the participant was uncertain. We performed cluster-based comparison of the two conditions using FSL fMRI Expert Analysis Tool and network-based analysis using MATLAB. The uncertain > certain comparison yielded three clusters - a midline cluster that extended through the midbrain, the thalamus, bilateral prefrontal cortex, the striatum, and bilateral parietal/occipital clusters. The certain > uncertain comparison yielded bilateral clusters in the insula, parietal and temporal lobe, as well as a medial frontal cluster. A larger, more connected functional network was found in the uncertain condition. The involvement of the insula, parietal cortex, temporal cortex, ventromedial prefrontal cortex, and orbitofrontal cortex of the certain condition reinforces the notion that certainty is inherently rewarding. For the uncertain condition, the involvement of the prefrontal cortex, parietal cortex, striatum, thalamus, amygdala, and hippocampal involvement was expected, as these are areas involved in resolving uncertainty and rule updating. The involvement of occipital cortical involvement and midbrain involvement may be attributed to increased visual attention and increased motor control. (orig.)

Functional brain networks involved in decision-making under certain and uncertain conditions

International Nuclear Information System (INIS)

Farrar, Danielle C.; Moss, Mark B.; Killiany, Ronald J.; Mian, Asim Z.; Budson, Andrew E.

2018-01-01

The aim of this study was to describe imaging markers of decision-making under uncertain conditions in normal individuals, in order to provide baseline activity to compare to impaired decision-making in pathological states. In this cross-sectional study, 19 healthy subjects ages 18-35 completed a novel decision-making card-matching task using a Phillips T3 Scanner and a 32-channel head coil. Functional data were collected in six functional runs. In one condition of the task, the participant was certain of the rule to apply to match the cards; in the other condition, the participant was uncertain. We performed cluster-based comparison of the two conditions using FSL fMRI Expert Analysis Tool and network-based analysis using MATLAB. The uncertain > certain comparison yielded three clusters - a midline cluster that extended through the midbrain, the thalamus, bilateral prefrontal cortex, the striatum, and bilateral parietal/occipital clusters. The certain > uncertain comparison yielded bilateral clusters in the insula, parietal and temporal lobe, as well as a medial frontal cluster. A larger, more connected functional network was found in the uncertain condition. The involvement of the insula, parietal cortex, temporal cortex, ventromedial prefrontal cortex, and orbitofrontal cortex of the certain condition reinforces the notion that certainty is inherently rewarding. For the uncertain condition, the involvement of the prefrontal cortex, parietal cortex, striatum, thalamus, amygdala, and hippocampal involvement was expected, as these are areas involved in resolving uncertainty and rule updating. The involvement of occipital cortical involvement and midbrain involvement may be attributed to increased visual attention and increased motor control. (orig.)

Self-supervised dynamical systems

International Nuclear Information System (INIS)

Zak, Michail

2004-01-01

A new type of dynamical systems which capture the interactions via information flows typical for active multi-agent systems is introduced. The mathematical formalism is based upon coupling the classical dynamical system (with random components caused by uncertainties in initial conditions as well as by Langevin forces) with the corresponding Liouville or the Fokker-Planck equations describing evolution of these uncertainties in terms of probability density. The coupling is implemented by information-based supervising forces which fundamentally change the patterns of probability evolution. It is demonstrated that the probability density can approach prescribed attractors while exhibiting such patterns as shock waves, solitons and chaos in probability space. Applications of these phenomena to information-based neural nets, expectation-based cooperation, self-programmed systems, control chaos using terminal attractors as well as to games with incomplete information, are addressed. A formal similarity between the mathematical structure of the introduced dynamical systems and quantum mechanics is discussed

Journal of Earth System Science | Indian Academy of Sciences

Indian Academy of Sciences (India)

This paper examines a possible effect of uncertainties, variability or heterogeneity of any dynamic system when being included in its evolution rule; the notion is illustrated with the advection dispersion equation, which describes the groundwater pollution model. An uncertain derivative is defined; some properties of the ...

Including local rainfall dynamics and uncertain boundary conditions into a 2-D regional-local flood modelling cascade

Science.gov (United States)

Bermúdez, María; Neal, Jeffrey C.; Bates, Paul D.; Coxon, Gemma; Freer, Jim E.; Cea, Luis; Puertas, Jerónimo

2016-04-01

Flood inundation models require appropriate boundary conditions to be specified at the limits of the domain, which commonly consist of upstream flow rate and downstream water level. These data are usually acquired from gauging stations on the river network where measured water levels are converted to discharge via a rating curve. Derived streamflow estimates are therefore subject to uncertainties in this rating curve, including extrapolating beyond the maximum observed ratings magnitude. In addition, the limited number of gauges in reach-scale studies often requires flow to be routed from the nearest upstream gauge to the boundary of the model domain. This introduces additional uncertainty, derived not only from the flow routing method used, but also from the additional lateral rainfall-runoff contributions downstream of the gauging point. Although generally assumed to have a minor impact on discharge in fluvial flood modeling, this local hydrological input may become important in a sparse gauge network or in events with significant local rainfall. In this study, a method to incorporate rating curve uncertainty and the local rainfall-runoff dynamics into the predictions of a reach-scale flood inundation model is proposed. Discharge uncertainty bounds are generated by applying a non-parametric local weighted regression approach to stage-discharge measurements for two gauging stations, while measured rainfall downstream from these locations is cascaded into a hydrological model to quantify additional inflows along the main channel. A regional simplified-physics hydraulic model is then applied to combine these inputs and generate an ensemble of discharge and water elevation time series at the boundaries of a local-scale high complexity hydraulic model. Finally, the effect of these rainfall dynamics and uncertain boundary conditions are evaluated on the local-scale model. Improvements in model performance when incorporating these processes are quantified using observed

Online Recorded Data-Based Composite Neural Control of Strict-Feedback Systems With Application to Hypersonic Flight Dynamics.

Science.gov (United States)

Xu, Bin; Yang, Daipeng; Shi, Zhongke; Pan, Yongping; Chen, Badong; Sun, Fuchun

2017-09-25

This paper investigates the online recorded data-based composite neural control of uncertain strict-feedback systems using the backstepping framework. In each step of the virtual control design, neural network (NN) is employed for uncertainty approximation. In previous works, most designs are directly toward system stability ignoring the fact how the NN is working as an approximator. In this paper, to enhance the learning ability, a novel prediction error signal is constructed to provide additional correction information for NN weight update using online recorded data. In this way, the neural approximation precision is highly improved, and the convergence speed can be faster. Furthermore, the sliding mode differentiator is employed to approximate the derivative of the virtual control signal, and thus, the complex analysis of the backstepping design can be avoided. The closed-loop stability is rigorously established, and the boundedness of the tracking error can be guaranteed. Through simulation of hypersonic flight dynamics, the proposed approach exhibits better tracking performance.

Dynamical System Approaches to Combinatorial Optimization

DEFF Research Database (Denmark)

Starke, Jens

2013-01-01

of large times as an asymptotically stable point of the dynamics. The obtained solutions are often not globally optimal but good approximations of it. Dynamical system and neural network approaches are appropriate methods for distributed and parallel processing. Because of the parallelization......Several dynamical system approaches to combinatorial optimization problems are described and compared. These include dynamical systems derived from penalty methods; the approach of Hopfield and Tank; self-organizing maps, that is, Kohonen networks; coupled selection equations; and hybrid methods...... thereof can be used as models for many industrial problems like manufacturing planning and optimization of flexible manufacturing systems. This is illustrated for an example in distributed robotic systems....

Dynamical systems examples of complex behaviour

CERN Document Server

Jost, Jürgen

2005-01-01

Our aim is to introduce, explain, and discuss the fundamental problems, ideas, concepts, results, and methods of the theory of dynamical systems and to show how they can be used in speci?c examples. We do not intend to give a comprehensive overview of the present state of research in the theory of dynamical systems, nor a detailed historical account of its development. We try to explain the important results, often neglecting technical re?nements 1 and, usually, we do not provide proofs. One of the basic questions in studying dynamical systems, i.e. systems that evolve in time, is the construction of invariants that allow us to classify qualitative types of dynamical evolution, to distinguish between qualitatively di?erent dynamics, and to studytransitions between di?erent types. Itis also important to ?nd out when a certain dynamic behavior is stable under small perturbations, as well as to understand the various scenarios of instability. Finally, an essential aspect of a dynamic evolution is the transformat...

Functional System Dynamics

OpenAIRE

Ligterink, N.E.

2007-01-01

Functional system dynamics is the analysis, modelling, and simulation of continuous systems usually described by partial differential equations. From the infinite degrees of freedom of such systems only a finite number of relevant variables have to be chosen for a practical model description. The proper input and output of the system are an important part of the relevant variables.

Adaptive Integration of Nonsmooth Dynamical Systems

Science.gov (United States)

2017-10-11

2017 W911NF-12-R-0012-03: Adaptive Integration of Nonsmooth Dynamical Systems The views, opinions and/or findings contained in this report are those of...Integration of Nonsmooth Dynamical Systems Report Term: 0-Other Email: drum@gwu.edu Distribution Statement: 1-Approved for public release; distribution is...classdrake_1_1systems_1_1_integrator_base.html ; 3) a solver for dynamical systems with arbitrary unilateral and bilateral constraints (the key component of the time stepping systems )- see

Chance constrained uncertain classification via robust optimization

NARCIS (Netherlands)

Ben-Tal, A.; Bhadra, S.; Bhattacharayya, C.; Saketha Nat, J.

2011-01-01

This paper studies the problem of constructing robust classifiers when the training is plagued with uncertainty. The problem is posed as a Chance-Constrained Program (CCP) which ensures that the uncertain data points are classified correctly with high probability. Unfortunately such a CCP turns out

Licensing Uncertain Patents: Per-Unit Royalty vs Up-Front Fee

OpenAIRE

Encaoua , David; Lefouili , Yassine

2008-01-01

ED EPS; In this paper we examine the implications of uncertainty over patent validity on patentholders' licensing strategies. Two licensing mechanisms are examined: per-unit royalty and up-front fee.We provide conditions under which uncertain patents are licensed in order to avoid patent litigation. It is shown that while it is possible for the patentholder to reap som e "extra profit" by selling an uncertain patent under the pure per-unit royalty regime, the opportunity to do so does not exi...

Butschli Dynamic Droplet System

DEFF Research Database (Denmark)

Armstrong, R.; Hanczyc, M.

2013-01-01

Dynamical oil-water systems such as droplets display lifelike properties and may lend themselves to chemical programming to perform useful work, specifically with respect to the built environment. We present Butschli water-in-oil droplets as a model for further investigation into the development...... reconstructed the Butschli system and observed its life span under a light microscope, observing chemical patterns and droplet behaviors in nearly three hundred replicate experiments. Self-organizing patterns were observed, and during this dynamic, embodied phase the droplets provided a means of introducing...... temporal and spatial order in the system with the potential for chemical programmability. The authors propose that the discrete formation of dynamic droplets, characterized by their lifelike behavior patterns, during a variable window of time (from 30 s to 30 min after the addition of alkaline water...

Constructor:synthesizing information about uncertain variables.

Energy Technology Data Exchange (ETDEWEB)

Tucker, W. Troy (Applied Biomathematics, Setauket, NY); Ferson, Scott (Applied Biomathematics, Setauket, NY); Hajagos, Janos (Applied Biomathematics, Setauket, NY); Myers, David S. (Applied Biomathematics, Setauket, NY)

2005-09-01

Constructor is software for the Microsoft Windows microcomputer environment that facilitates the collation of empirical information and expert judgment for the specification of probability distributions, probability boxes, random sets or Dempster-Shafer structures from data, qualitative shape information, constraints on moments, order statistics, densities, and coverage probabilities about uncertain unidimensional quantities. These quantities may be real-valued, integer-valued or logical values.

Sustainability of socio-hydro system with changing value and preference to an uncertain future climate and economic conditions.

Science.gov (United States)

Roobavannan, Mahendran; Kandasamy, Jaya; Vigneswaran, Saravanamuththu; Sivapalan, Murugesu

2016-04-01

Water-human systems are coupled and display co-evolutionary dynamics influenced by society's values and preference. This has been observed in the Murrumbidgee basin, Australia where water usage initially focused on agriculture production and until mid-1990's favoured agriculture. This turned around as society became more concerned about the degradation of ecosystems and ultimately water was reallocated back towards the environment. This new water management adversely impacted the agriculture sector and created economic stress in the basin. The basin communities were able to transform and cope with water allocation favouring the environment through sectoral transformation facilitated by movement of capital in a free economy, supported by appropriate strategies and funding. This was helped by the adaptive capacity of people through reemployment in other economic sectors of the basin economy, unemployment for a period of time and migration out of the basin, and crop diversification. This study looks to the future and focuses on how water managers could be informed and prepare for un-foreseen issues coming out of societies changing values and preferences and emerging as different systems in the basin interact with each other at different times and speed. The issues of this type that concern the Murray Darling Basin Authority include a renewed focus and priority on food production due to food scarcity; increased impact and frequency of natural disasters (eg. climate change); regional economic diversification due to the growth of peri-urban development in the basin; institutional capacity for water reform due to new political paradigms (eg. new water sharing plans); and improvement in science and technology (eg. farm practices, water efficiency, water reuse). To undertake this, the study uses a coupled socio-hydrological dynamical system that model the major drivers of changing economic conditions, society values and preference, climatic condition and science and

Optimized maritime emergency resource allocation under dynamic demand.

Directory of Open Access Journals (Sweden)

Wenfen Zhang

Full Text Available Emergency resource is important for people evacuation and property rescue when accident occurs. The relief efforts could be promoted by a reasonable emergency resource allocation schedule in advance. As the marine environment is complicated and changeful, the place, type, severity of maritime accident is uncertain and stochastic, bringing about dynamic demand of emergency resource. Considering dynamic demand, how to make a reasonable emergency resource allocation schedule is challenging. The key problem is to determine the optimal stock of emergency resource for supplier centers to improve relief efforts. This paper studies the dynamic demand, and which is defined as a set. Then a maritime emergency resource allocation model with uncertain data is presented. Afterwards, a robust approach is developed and used to make sure that the resource allocation schedule performs well with dynamic demand. Finally, a case study shows that the proposed methodology is feasible in maritime emergency resource allocation. The findings could help emergency manager to schedule the emergency resource allocation more flexibly in terms of dynamic demand.

Quantum dynamics in open quantum-classical systems.

Science.gov (United States)

Kapral, Raymond

2015-02-25

Often quantum systems are not isolated and interactions with their environments must be taken into account. In such open quantum systems these environmental interactions can lead to decoherence and dissipation, which have a marked influence on the properties of the quantum system. In many instances the environment is well-approximated by classical mechanics, so that one is led to consider the dynamics of open quantum-classical systems. Since a full quantum dynamical description of large many-body systems is not currently feasible, mixed quantum-classical methods can provide accurate and computationally tractable ways to follow the dynamics of both the system and its environment. This review focuses on quantum-classical Liouville dynamics, one of several quantum-classical descriptions, and discusses the problems that arise when one attempts to combine quantum and classical mechanics, coherence and decoherence in quantum-classical systems, nonadiabatic dynamics, surface-hopping and mean-field theories and their relation to quantum-classical Liouville dynamics, as well as methods for simulating the dynamics.

Sustained, Low-Intensity Exercise Achieved by a Dynamic Feeding System Decreases Body Fat in Ponies.

Science.gov (United States)

de Laat, M A; Hampson, B A; Sillence, M N; Pollitt, C C

2016-09-01

Obesity in horses is increasing in prevalence and can be associated with insulin insensitivity and laminitis. Current treatment strategies for obesity include dietary restriction and exercise. However, whether exercise alone is effective for decreasing body fat is uncertain. Our hypothesis was that twice daily use of a dynamic feeding system for 3 months would induce sustained, low-intensity exercise thereby decreasing adiposity and improving insulin sensitivity (SI). Eight, university-owned, mixed-breed, adult ponies with body condition scores (BCS) ≥5/9 were used. Two treatments ("feeder on" or "feeder off") were administered for a 3-month period by a randomized, crossover design (n = 4/treatment). An interim equilibration period of 6 weeks at pasture separated the 2 study phases. Measurements of body mass (body weight, BCS, cresty neck score [CrNS], and morphometry), body fat (determined before and after the "feeder on" treatment only), triglycerides, and insulin sensitivity (SI; combined glucose-insulin test) were undertaken before and after treatments. The dynamic feeding system induced a 3.7-fold increase in the daily distance travelled (n = 6), compared to with a stationary feeder, which significantly decreased mean BCS (6.53 ± 0.94 to 5.38 ± 1.71), CrNS (2.56 ± 1.12 to 1.63 ± 1.06) and body fat (by 4.95%). An improvement in SI did not occur in all ponies. A dynamic feeding system can be used to induce sustained (daily), low-intensity exercise that promotes weight loss in ponies. However, this exercise may not be sufficient to substantially improve SI. Copyright © 2016 The Authors. Journal of Veterinary Internal Medicine published by Wiley Periodicals, Inc. on behalf of the American College of Veterinary Internal Medicine.

Non probabilistic solution of uncertain neutron diffusion equation for imprecisely defined homogeneous bare reactor

International Nuclear Information System (INIS)

Chakraverty, S.; Nayak, S.

2013-01-01

Highlights: • Uncertain neutron diffusion equation of bare square homogeneous reactor is studied. • Proposed interval arithmetic is extended for fuzzy numbers. • The developed fuzzy arithmetic is used to handle uncertain parameters. • Governing differential equation is modelled by modified fuzzy finite element method. • Fuzzy critical eigenvalues and effective multiplication factors are investigated. - Abstract: The scattering of neutron collision inside a reactor depends upon geometry of the reactor, diffusion coefficient and absorption coefficient etc. In general these parameters are not crisp and hence we get uncertain neutron diffusion equation. In this paper we have investigated the above equation for a bare square homogeneous reactor. Here the uncertain governing differential equation is modelled by a modified fuzzy finite element method. Using modified fuzzy finite element method, obtained eigenvalues and effective multiplication factors are studied. Corresponding results are compared with the classical finite element method in special cases and various uncertain results have been discussed

Dynamics of glassy systems

International Nuclear Information System (INIS)

Cugliandolo, Leticia F.

2003-09-01

These lecture notes can be read in two ways. The first two Sections contain a review of the phenomenology of several physical systems with slow nonequilibrium dynamics. In the Conclusions we summarize the scenario for this temporal evolution derived from the solution to some solvable models (p spin and the like) that are intimately connected to the mode coupling approach (and similar ones) to super-cooled liquids. At the end we list a number of open problems of great relevance in this context. These Sections can be read independently of the body of the paper where we present some of the basic analytic techniques used to study the out of equilibrium dynamics of classical and quantum models with and without disorder. We start the technical part by briefly discussing the role played by the environment and by introducing and comparing its representation in the equilibrium and dynamic treatment of classical and quantum systems. We next explain the role played by explicit quenched disorder in both approaches. Later on we focus on analytical techniques; we expand on the dynamic functional methods, and the diagrammatic expansions and resummations used to derive macroscopic equations from the microscopic dynamics. We show why the macroscopic dynamic equations for disordered models and those resulting from self-consistent approximations to non-disordered ones coincide. We review some generic properties of dynamic systems evolving out of equilibrium like the modifications of the fluctuation-dissipation theorem, generic scaling forms of the correlation functions, etc. Finally we solve a family of mean-field models. The connection between the dynamic treatment and the analysis of the free-energy landscape of these models is also presented. We use pedagogical examples all along these lectures to illustrate the properties and results. (author)

Uncertain climate policy and the green paradox

NARCIS (Netherlands)

Smulders, Sjak A.; Tsur, Y.; Zemel, A.; Moser, E.; Semmler, W.; Tragler, G.; Veliov, V.

2014-01-01

Unintended consequences of announcing a climate policy well in advance of its implementation have been studied in a variety of situations. We show that a phenomenon akin to the so-called “Green-Paradox” holds also when the policy implementation date is uncertain. Governments are compelled, by

Sensitivity in risk analyses with uncertain numbers.

Energy Technology Data Exchange (ETDEWEB)

Tucker, W. Troy; Ferson, Scott

2006-06-01

Sensitivity analysis is a study of how changes in the inputs to a model influence the results of the model. Many techniques have recently been proposed for use when the model is probabilistic. This report considers the related problem of sensitivity analysis when the model includes uncertain numbers that can involve both aleatory and epistemic uncertainty and the method of calculation is Dempster-Shafer evidence theory or probability bounds analysis. Some traditional methods for sensitivity analysis generalize directly for use with uncertain numbers, but, in some respects, sensitivity analysis for these analyses differs from traditional deterministic or probabilistic sensitivity analyses. A case study of a dike reliability assessment illustrates several methods of sensitivity analysis, including traditional probabilistic assessment, local derivatives, and a ''pinching'' strategy that hypothetically reduces the epistemic uncertainty or aleatory uncertainty, or both, in an input variable to estimate the reduction of uncertainty in the outputs. The prospects for applying the methods to black box models are also considered.

Exponential stability result for discrete-time stochastic fuzzy uncertain neural networks

International Nuclear Information System (INIS)

Mathiyalagan, K.; Sakthivel, R.; Marshal Anthoni, S.

2012-01-01

This Letter addresses the stability analysis problem for a class of uncertain discrete-time stochastic fuzzy neural networks (DSFNNs) with time-varying delays. By constructing a new Lyapunov–Krasovskii functional combined with the free weighting matrix technique, a new set of delay-dependent sufficient conditions for the robust exponential stability of the considered DSFNNs is established in terms of Linear Matrix Inequalities (LMIs). Finally, numerical examples with simulation results are provided to illustrate the applicability and usefulness of the obtained theory. -- Highlights: ► Applications of neural networks require the knowledge of dynamic behaviors. ► Exponential stability of discrete-time stochastic fuzzy neural networks is studied. ► Linear matrix inequality optimization approach is used to obtain the result. ► Delay-dependent stability criterion is established in terms of LMIs. ► Examples with simulation are provided to show the effectiveness of the result.

Modelling of risk events with uncertain likelihoods and impacts in large infrastructure projects

DEFF Research Database (Denmark)

Schjær-Jacobsen, Hans

2010-01-01

to prevent future budget overruns. One of the central ideas is to introduce improved risk management processes and the present paper addresses this particular issue. A relevant cost function in terms of unit prices and quantities is developed and an event impact matrix with uncertain impacts from independent......This paper presents contributions to the mathematical core of risk and uncertainty management in compliance with the principles of New Budgeting laid out in 2008 by the Danish Ministry of Transport to be used in large infrastructure projects. Basically, the new principles are proposed in order...... uncertain risk events is used to calculate the total uncertain risk budget. Cost impacts from the individual risk events on the individual project activities are kept precisely track of in order to comply with the requirements of New Budgeting. Additionally, uncertain likelihoods for the occurrence of risk...

Dynamical systems, attractors, and neural circuits.

Science.gov (United States)

Miller, Paul

2016-01-01

Biology is the study of dynamical systems. Yet most of us working in biology have limited pedagogical training in the theory of dynamical systems, an unfortunate historical fact that can be remedied for future generations of life scientists. In my particular field of systems neuroscience, neural circuits are rife with nonlinearities at all levels of description, rendering simple methodologies and our own intuition unreliable. Therefore, our ideas are likely to be wrong unless informed by good models. These models should be based on the mathematical theories of dynamical systems since functioning neurons are dynamic-they change their membrane potential and firing rates with time. Thus, selecting the appropriate type of dynamical system upon which to base a model is an important first step in the modeling process. This step all too easily goes awry, in part because there are many frameworks to choose from, in part because the sparsely sampled data can be consistent with a variety of dynamical processes, and in part because each modeler has a preferred modeling approach that is difficult to move away from. This brief review summarizes some of the main dynamical paradigms that can arise in neural circuits, with comments on what they can achieve computationally and what signatures might reveal their presence within empirical data. I provide examples of different dynamical systems using simple circuits of two or three cells, emphasizing that any one connectivity pattern is compatible with multiple, diverse functions.

Dynamic Stability Experiment of Maglev Systems,

Science.gov (United States)

1995-04-01

This report summarizes the research performed on maglev vehicle dynamic stability at Argonne National Laboratory during the past few years. It also... maglev system, it is important to consider this phenomenon in the development of all maglev systems. This report presents dynamic stability experiments...on maglev systems and compares their numerical simulation with predictions calculated by a nonlinear dynamic computer code. Instabilities of an

Dynamical systems

CERN Document Server

Birkhoff, George D

1927-01-01

His research in dynamics constitutes the middle period of Birkhoff's scientific career, that of maturity and greatest power. -Yearbook of the American Philosophical Society The author's great book€¦is well known to all, and the diverse active modern developments in mathematics which have been inspired by this volume bear the most eloquent testimony to its quality and influence. -Zentralblatt MATH In 1927, G. D. Birkhoff wrote a remarkable treatise on the theory of dynamical systems that would inspire many later mathematicians to do great work. To a large extent, Birkhoff was writing about his o

An Integrated Multiechelon Logistics Model with Uncertain Delivery Lead Time and Quality Unreliability

Directory of Open Access Journals (Sweden)

Ming-Feng Yang

2016-01-01

Full Text Available Nowadays, in order to achieve advantages in supply chain management, how to keep inventory in adequate level and how to enhance customer service level are two critical practices for decision makers. Generally, uncertain lead time and defective products have much to do with inventory and service level. Therefore, this study mainly aims at developing a multiechelon integrated just-in-time inventory model with uncertain lead time and imperfect quality to enhance the benefits of the logistics model. In addition, the Ant Colony Algorithm (ACA is established to determine the optimal solutions. Moreover, based on our proposed model and analysis, the ACA is more efficient than Particle Swarm Optimization (PSO and Lingo in SMEIJI model. An example is provided in this study to illustrate how production run and defective rate have an effect on system costs. Finally, the results of our research could provide some managerial insights which support decision makers in real-world operations.

On the adaptive sliding mode controller for a hyperchaotic fractional-order financial system

Science.gov (United States)

Hajipour, Ahamad; Hajipour, Mojtaba; Baleanu, Dumitru

2018-05-01

This manuscript mainly focuses on the construction, dynamic analysis and control of a new fractional-order financial system. The basic dynamical behaviors of the proposed system are studied such as the equilibrium points and their stability, Lyapunov exponents, bifurcation diagrams, phase portraits of state variables and the intervals of system parameters. It is shown that the system exhibits hyperchaotic behavior for a number of system parameters and fractional-order values. To stabilize the proposed hyperchaotic fractional system with uncertain dynamics and disturbances, an efficient adaptive sliding mode controller technique is developed. Using the proposed technique, two hyperchaotic fractional-order financial systems are also synchronized. Numerical simulations are presented to verify the successful performance of the designed controllers.

Novel pinning control strategies for synchronisation of complex networks with nonlinear coupling dynamics

International Nuclear Information System (INIS)

Zhao-Bing, Liu; Hua-Guang, Zhang; Qiu-Ye, Sun

2010-01-01

This paper considers the global stability of controlling an uncertain complex network to a homogeneous trajectory of the uncoupled system by a local pinning control strategy. Several sufficient conditions are derived to guarantee the network synchronisation by investigating the relationship among pinning synchronisation, network topology, and coupling strength. Also, some fundamental and yet challenging problems in the pinning control of complex networks are discussed: (1) what nodes should be selected as pinned candidates? (2) How many nodes are needed to be pinned for a fixed coupling strength? Furthermore, an adaptive pinning control scheme is developed. In order to achieve synchronisation of an uncertain complex network, the adaptive tuning strategy of either the coupling strength or the control gain is utilised. As an illustrative example, a network with the Lorenz system as node self-dynamics is simulated to verify the efficacy of theoretical results. (general)

Dynamics of unstable systems

International Nuclear Information System (INIS)

Posch, H.A.; Narnhofer, H.; Thirring, W.

1990-01-01

We study the dynamics of classical particles interacting with attractive Gaussian potentials. This system is thermodynamically not stable and exhibits negative specific heat. The results of the computer simulation of the dynamics are discussed in comparison with various theories. In particular, we find that the condensed phase is a stationary solution of the Vlasov equation, but the Vlasov dynamics cannot describe the collapse. 14 refs., 1 tab., 11 figs. (Authors)

Altered brain activation and connectivity during anticipation of uncertain threat in trait anxiety.

Science.gov (United States)

Geng, Haiyang; Wang, Yi; Gu, Ruolei; Luo, Yue-Jia; Xu, Pengfei; Huang, Yuxia; Li, Xuebing

2018-06-08

In the research field of anxiety, previous studies generally focus on emotional responses following threat. A recent model of anxiety proposes that altered anticipation prior to uncertain threat is related with the development of anxiety. Behavioral findings have built the relationship between anxiety and distinct anticipatory processes including attention, estimation of threat, and emotional responses. However, few studies have characterized the brain organization underlying anticipation of uncertain threat and its role in anxiety. In the present study, we used an emotional anticipation paradigm with functional magnetic resonance imaging (fMRI) to examine the aforementioned topics by employing brain activation and general psychophysiological interactions (gPPI) analysis. In the activation analysis, we found that high trait anxious individuals showed significantly increased activation in the thalamus, middle temporal gyrus (MTG), and dorsomedial prefrontal cortex (dmPFC), as well as decreased activation in the precuneus, during anticipation of uncertain threat compared to the certain condition. In the gPPI analysis, the key regions including the amygdala, dmPFC, and precuneus showed altered connections with distributed brain areas including the ventromedial prefrontal cortex (vmPFC), dorsolateral prefrontal cortex (dlPFC), inferior parietal sulcus (IPS), insula, para-hippocampus gyrus (PHA), thalamus, and MTG involved in anticipation of uncertain threat in anxious individuals. Taken together, our findings indicate that during the anticipation of uncertain threat, anxious individuals showed altered activations and functional connectivity in widely distributed brain areas, which may be critical for abnormal perception, estimation, and emotion reactions during the anticipation of uncertain threat. © 2018 Wiley Periodicals, Inc.

Multibody system dynamics, robotics and control

CERN Document Server

Gerstmayr, Johannes

2013-01-01

The volume contains 19 contributions by international experts in the field of multibody system dynamics, robotics and control. The book aims to bridge the gap between the modeling of mechanical systems by means of multibody dynamics formulations and robotics. In the classical approach, a multibody dynamics model contains a very high level of detail, however, the application of such models to robotics or control is usually limited. The papers aim to connect the different scientific communities in multibody dynamics, robotics and control. Main topics are flexible multibody systems, humanoid robots, elastic robots, nonlinear control, optimal path planning, and identification.

Modular interdependency in complex dynamical systems.

Science.gov (United States)

Watson, Richard A; Pollack, Jordan B

2005-01-01

Herbert A. Simon's characterization of modularity in dynamical systems describes subsystems as having dynamics that are approximately independent of those of other subsystems (in the short term). This fits with the general intuition that modules must, by definition, be approximately independent. In the evolution of complex systems, such modularity may enable subsystems to be modified and adapted independently of other subsystems, whereas in a nonmodular system, modifications to one part of the system may result in deleterious side effects elsewhere in the system. But this notion of modularity and its effect on evolvability is not well quantified and is rather simplistic. In particular, modularity need not imply that intermodule dependences are weak or unimportant. In dynamical systems this is acknowledged by Simon's suggestion that, in the long term, the dynamical behaviors of subsystems do interact with one another, albeit in an "aggregate" manner--but this kind of intermodule interaction is omitted in models of modularity for evolvability. In this brief discussion we seek to unify notions of modularity in dynamical systems with notions of how modularity affects evolvability. This leads to a quantifiable measure of modularity and a different understanding of its effect on evolvability.

Probabilistic logic networks a comprehensive framework for uncertain inference

CERN Document Server

Goertzel, Ben; Goertzel, Izabela Freire; Heljakka, Ari

2008-01-01

This comprehensive book describes Probabilistic Logic Networks (PLN), a novel conceptual, mathematical and computational approach to uncertain inference. A broad scope of reasoning types are considered.

Partnership Selection Involving Mixed Types of Uncertain Preferences

Directory of Open Access Journals (Sweden)

Li-Ching Ma

2013-01-01

Full Text Available Partnership selection is an important issue in management science. This study proposes a general model based on mixed integer programming and goal-programming analytic hierarchy process (GP-AHP to solve partnership selection problems involving mixed types of uncertain or inconsistent preferences. The proposed approach is designed to deal with crisp, interval, step, fuzzy, or mixed comparison preferences, derive crisp priorities, and improve multiple solution problems. The degree of fulfillment of a decision maker’s preferences is also taken into account. The results show that the proposed approach keeps more solution ratios within the given preferred intervals and yields less deviation. In addition, the proposed approach can treat incomplete preference matrices with flexibility in reducing the number of pairwise comparisons required and can also be conveniently developed into a decision support system.

Uncertain Portfolio Selection with Background Risk and Liquidity Constraint

Directory of Open Access Journals (Sweden)

Jia Zhai

2017-01-01

Full Text Available This paper discusses an uncertain portfolio selection problem with consideration of background risk and asset liquidity. In addition, the transaction costs are also considered. The security returns, background asset return, and asset liquidity are estimated by experienced experts instead of historical data. Regarding them as uncertain variables, a mean-risk model with background risk, liquidity, and transaction costs is proposed for portfolio selection and the crisp forms of the model are provided when security returns obey different uncertainty distributions. Moreover, for better understanding of the impact of background risk and liquidity on portfolio selection, some important theorems are proved. Finally, numerical experiments are presented to illustrate the modeling idea.

Constraint Embedding for Multibody System Dynamics

Science.gov (United States)

Jain, Abhinandan

2009-01-01

This paper describes a constraint embedding approach for the handling of local closure constraints in multibody system dynamics. The approach uses spatial operator techniques to eliminate local-loop constraints from the system and effectively convert the system into tree-topology systems. This approach allows the direct derivation of recursive O(N) techniques for solving the system dynamics and avoiding the expensive steps that would otherwise be required for handling the closedchain dynamics. The approach is very effective for systems where the constraints are confined to small-subgraphs within the system topology. The paper provides background on the spatial operator O(N) algorithms, the extensions for handling embedded constraints, and concludes with some examples of such constraints.

Operationalizing sustainability in urban coastal systems: a system dynamics analysis.

Science.gov (United States)

Mavrommati, Georgia; Bithas, Kostas; Panayiotidis, Panayiotis

2013-12-15

We propose a system dynamics approach for Ecologically Sustainable Development (ESD) in urban coastal systems. A systematic analysis based on theoretical considerations, policy analysis and experts' knowledge is followed in order to define the concept of ESD. The principles underlying ESD feed the development of a System Dynamics Model (SDM) that connects the pollutant loads produced by urban systems' socioeconomic activities with the ecological condition of the coastal ecosystem that it is delineated in operational terms through key biological elements defined by the EU Water Framework Directive. The receiving waters of the Athens Metropolitan area, which bears the elements of typical high population density Mediterranean coastal city but which currently has also new dynamics induced by the ongoing financial crisis, are used as an experimental system for testing a system dynamics approach to apply the concept of ESD. Systems' thinking is employed to represent the complex relationships among the components of the system. Interconnections and dependencies that determine the potentials for achieving ESD are revealed. The proposed system dynamics analysis can facilitate decision makers to define paths of development that comply with the principles of ESD. Copyright © 2013 Elsevier Ltd. All rights reserved.

The Aharonov-Anandan phase of a classical dynamical system seen mathematically as a quantum dynamical system

OpenAIRE

Segre, Gavriel

2005-01-01

It is shown that the non-adiabatic Hannay's angle of an integrable non-degenerate classical hamiltonian dynamical system may be related to the Aharonov-Anandan phase it develops when it is looked mathematically as a quantum dynamical system.

Robust output feedback cruise control for high-speed train movement with uncertain parameters

International Nuclear Information System (INIS)

Li Shu-Kai; Yang Li-Xing; Li Ke-Ping

2015-01-01

In this paper, the robust output feedback cruise control for high-speed train movement with uncertain parameters is investigated. The dynamic of a high-speed train is modeled by a cascade of cars connected by flexible couplers, which is subject to rolling mechanical resistance, aerodynamic drag and wind gust. Based on Lyapunov’s stability theory, the sufficient condition for the existence of the robust output feedback cruise control law is given in terms of linear matrix inequalities (LMIs), under which the high-speed train tracks the desired speed, the relative spring displacement between the two neighboring cars is stable at the equilibrium state, and meanwhile a small prescribed H ∞ disturbance attenuation level is guaranteed. One numerical example is given to illustrate the effectiveness of the proposed methods. (paper)

Probabilistic confidence for decisions based on uncertain reliability estimates

Science.gov (United States)

Reid, Stuart G.

2013-05-01

Reliability assessments are commonly carried out to provide a rational basis for risk-informed decisions concerning the design or maintenance of engineering systems and structures. However, calculated reliabilities and associated probabilities of failure often have significant uncertainties associated with the possible estimation errors relative to the 'true' failure probabilities. For uncertain probabilities of failure, a measure of 'probabilistic confidence' has been proposed to reflect the concern that uncertainty about the true probability of failure could result in a system or structure that is unsafe and could subsequently fail. The paper describes how the concept of probabilistic confidence can be applied to evaluate and appropriately limit the probabilities of failure attributable to particular uncertainties such as design errors that may critically affect the dependability of risk-acceptance decisions. This approach is illustrated with regard to the dependability of structural design processes based on prototype testing with uncertainties attributable to sampling variability.

Dynamical systems in population biology

CERN Document Server

Zhao, Xiao-Qiang

2017-01-01

This research monograph provides an introduction to the theory of nonautonomous semiflows with applications to population dynamics. It develops dynamical system approaches to various evolutionary equations such as difference, ordinary, functional, and partial differential equations, and pays more attention to periodic and almost periodic phenomena. The presentation includes persistence theory, monotone dynamics, periodic and almost periodic semiflows, basic reproduction ratios, traveling waves, and global analysis of prototypical population models in ecology and epidemiology. Research mathematicians working with nonlinear dynamics, particularly those interested in applications to biology, will find this book useful. It may also be used as a textbook or as supplementary reading for a graduate special topics course on the theory and applications of dynamical systems. Dr. Xiao-Qiang Zhao is a University Research Professor at Memorial University of Newfoundland, Canada. His main research interests involve applied...

Dynamism in Electronic Performance Support Systems.

Science.gov (United States)

Laffey, James

1995-01-01

Describes a model for dynamic electronic performance support systems based on NNAble, a system developed by the training group at Apple Computer. Principles for designing dynamic performance support are discussed, including a systems approach, performer-centered design, awareness of situated cognition, organizational memory, and technology use.…

Frequent Symptom Sets Identification from Uncertain Medical Data in Differentially Private Way

Directory of Open Access Journals (Sweden)

Zhe Ding

2017-01-01

Full Text Available Data mining techniques are applied to identify hidden patterns in large amounts of patient data. These patterns can assist physicians in making more accurate diagnosis. For different physical conditions of patients, the same physiological index corresponds to a different symptom association probability for each patient. Data mining technologies based on certain data cannot be directly applied to these patients’ data. Patient data are sensitive data. An adversary with sufficient background information can make use of the patterns mined from uncertain medical data to obtain the sensitive information of patients. In this paper, a new algorithm is presented to determine the top K most frequent itemsets from uncertain medical data and to protect data privacy. Based on traditional algorithms for mining frequent itemsets from uncertain data, our algorithm applies sparse vector algorithm and the Laplace mechanism to ensure differential privacy for the top K most frequent itemsets for uncertain medical data and the expected supports of these frequent itemsets. We prove that our algorithm can guarantee differential privacy in theory. Moreover, we carry out experiments with four real-world scenario datasets and two synthetic datasets. The experimental results demonstrate the performance of our algorithm.

Flexible and adaptive water systems operations through more informed and dynamic decisions

Science.gov (United States)

Castelletti, A.; Giuliani, M.

2016-12-01

Timely adapting the operations of water systems to be resilient against rapid changes in both hydroclimatic and socioeconomic forcing is generally recommended as a part of planning and managing water resources under uncertain futures. A great opportunity to make the operations more flexible and adaptive is offered by the unprecedented amount of information that is becoming available to water system operators, providing a wide range of data at increasingly higher temporal and spatial resolution. Yet, many water systems are still operated using very simple information systems, typically based on basic statistical analysis and the operator's experience. In this work, we discuss the potential offered by incorporating improved information to enhance water systems operation and increase their ability of adapting to different external conditions and resolving potential conflicts across sectors. In particular, we focus on the use of different variables associated to different dynamics of the system (slow and fast) diversely impacting the operating objectives on the short-, medium- and long-term. The multi-purpose operations of the Hoa Binh reservoir in the Red River Basin (Vietnam) is used to demonstrate our approach. Numerical results show that our procedure is able to automatically select the most valuable information for improving the Hoa Binh operations and mitigating the conflict between short-term objectives, i.e. hydropower production and flood control. Moreover, we also successfully identify low-frequency climate information associated to El-Nino Southern Oscillation for improving the performance in terms of long-term objectives, i.e. water supply. Finally, we assess the value of better informing operational decisions for adapting the system operations to changing conditions by considering different climate change projections.

q-entropy for symbolic dynamical systems

International Nuclear Information System (INIS)

Zhao, Yun; Pesin, Yakov

2015-01-01

For symbolic dynamical systems we use the Carathéodory construction as described in (Pesin 1997 Dimension Theory in Dynamical Systems, ConTemporary Views and Applications (Chicago: University of Chicago Press)) to introduce the notions of q-topological and q-metric entropies. We describe some basic properties of these entropies and in particular, discuss relations between q-metric entropy and local metric entropy. Both q-topological and q-metric entropies are new invariants respectively under homeomorphisms and metric isomorphisms of dynamical systems. (paper)

Nonlinear control of linear parameter varying systems with applications to hypersonic vehicles

Science.gov (United States)

Wilcox, Zachary Donald

The focus of this dissertation is to design a controller for linear parameter varying (LPV) systems, apply it specifically to air-breathing hypersonic vehicles, and examine the interplay between control performance and the structural dynamics design. Specifically a Lyapunov-based continuous robust controller is developed that yields exponential tracking of a reference model, despite the presence of bounded, nonvanishing disturbances. The hypersonic vehicle has time varying parameters, specifically temperature profiles, and its dynamics can be reduced to an LPV system with additive disturbances. Since the HSV can be modeled as an LPV system the proposed control design is directly applicable. The control performance is directly examined through simulations. A wide variety of applications exist that can be effectively modeled as LPV systems. In particular, flight systems have historically been modeled as LPV systems and associated control tools have been applied such as gain-scheduling, linear matrix inequalities (LMIs), linear fractional transformations (LFT), and mu-types. However, as the type of flight environments and trajectories become more demanding, the traditional LPV controllers may no longer be sufficient. In particular, hypersonic flight vehicles (HSVs) present an inherently difficult problem because of the nonlinear aerothermoelastic coupling effects in the dynamics. HSV flight conditions produce temperature variations that can alter both the structural dynamics and flight dynamics. Starting with the full nonlinear dynamics, the aerothermoelastic effects are modeled by a temperature dependent, parameter varying state-space representation with added disturbances. The model includes an uncertain parameter varying state matrix, an uncertain parameter varying non-square (column deficient) input matrix, and an additive bounded disturbance. In this dissertation, a robust dynamic controller is formulated for a uncertain and disturbed LPV system. The developed

A model for the inverse 1-median problem on trees under uncertain costs

Directory of Open Access Journals (Sweden)

Kien Trung Nguyen

2016-01-01

Full Text Available We consider the problem of justifying vertex weights of a tree under uncertain costs so that a prespecified vertex become optimal and the total cost should be optimal in the uncertainty scenario. We propose a model which delivers the information about the optimal cost which respect to each confidence level \\(\\alpha \\in [0,1]\\. To obtain this goal, we first define an uncertain variable with respect to the minimum cost in each confidence level. If all costs are independently linear distributed, we present the inverse distribution function of this uncertain variable in \\(O(n^{2}\\log n\\ time, where \\(n\\ is the number of vertices in the tree.

The fractional dynamics of quantum systems

Science.gov (United States)

Lu, Longzhao; Yu, Xiangyang

2018-05-01

The fractional dynamic process of a quantum system is a novel and complicated problem. The establishment of a fractional dynamic model is a significant attempt that is expected to reveal the mechanism of fractional quantum system. In this paper, a generalized time fractional Schrödinger equation is proposed. To study the fractional dynamics of quantum systems, we take the two-level system as an example and derive the time fractional equations of motion. The basic properties of the system are investigated by solving this set of equations in the absence of light field analytically. Then, when the system is subject to the light field, the equations are solved numerically. It shows that the two-level system described by the time fractional Schrödinger equation we proposed is a confirmable system.

Dynamics of vehicle-road coupled system

CERN Document Server

Yang, Shaopu; Li, Shaohua

2015-01-01

Vehicle dynamics and road dynamics are usually considered to be two largely independent subjects. In vehicle dynamics, road surface roughness is generally regarded as random excitation of the vehicle, while in road dynamics, the vehicle is generally regarded as a moving load acting on the pavement. This book suggests a new research concept to integrate the vehicle and the road system with the help of a tire model, and establishes a cross-subject research framework dubbed vehicle-pavement coupled system dynamics. In this context, the dynamics of the vehicle, road and the vehicle-road coupled system are investigated by means of theoretical analysis, numerical simulations and field tests. This book will be a valuable resource for university professors, graduate students and engineers majoring in automotive design, mechanical engineering, highway engineering and other related areas. Shaopu Yang is a professor and deputy president of Shijiazhuang Tiedao University, China; Liqun Chen is a professor at Shanghai Univ...

Session 6: Dynamic Modeling and Systems Analysis

Science.gov (United States)

Csank, Jeffrey; Chapman, Jeffryes; May, Ryan

2013-01-01

These presentations cover some of the ongoing work in dynamic modeling and dynamic systems analysis. The first presentation discusses dynamic systems analysis and how to integrate dynamic performance information into the systems analysis. The ability to evaluate the dynamic performance of an engine design may allow tradeoffs between the dynamic performance and operability of a design resulting in a more efficient engine design. The second presentation discusses the Toolbox for Modeling and Analysis of Thermodynamic Systems (T-MATS). T-MATS is a Simulation system with a library containing the basic building blocks that can be used to create dynamic Thermodynamic Systems. Some of the key features include Turbo machinery components, such as turbines, compressors, etc., and basic control system blocks. T-MAT is written in the Matlab-Simulink environment and is open source software. The third presentation focuses on getting additional performance from the engine by allowing the limit regulators only to be active when a limit is danger of being violated. Typical aircraft engine control architecture is based on MINMAX scheme, which is designed to keep engine operating within prescribed mechanical/operational safety limits. Using a conditionally active min-max limit regulator scheme, additional performance can be gained by disabling non-relevant limit regulators

Nonlinear transport of dynamic system phase space

International Nuclear Information System (INIS)

Xie Xi; Xia Jiawen

1993-01-01

The inverse transform of any order solution of the differential equation of general nonlinear dynamic systems is derived, realizing theoretically the nonlinear transport for the phase space of nonlinear dynamic systems. The result is applicable to general nonlinear dynamic systems, with the transport of accelerator beam phase space as a typical example

A Newton--Galerkin Method for Fluid Flow Exhibiting Uncertain Periodic Dynamics

KAUST Repository

Schick, M.; Heuveline, V.; Le Ma, O. P.

2014-01-01

The determination of stable limit-cycles plays an important role in quantifying the characteristics of dynamical systems. In practice, exact knowledge of model parameters is rarely available leading to parameter uncertainties, which can be modeled as an input of random variables. This has the effect that the limit-cycles become stochastic themselves, resulting in almost surely time-periodic solutions with a stochastic period. In this paper we introduce a novel numerical method for the computation of stable stochastic limit-cycles based on the spectral stochastic finite element method using polynomial chaos (PC). We are able to overcome the difficulties of PC regarding its well-known convergence breakdown for long term integration. To this end, we introduce a stochastic time scaling which treats the stochastic period as an additional random variable and controls the phase-drift of the stochastic trajectories, keeping the necessary PC order low. Based on the rescaled governing equations, we aim at determining an initial condition and a period such that the trajectories close after completion of one stochastic cycle. Furthermore, we verify the numerical method by computation of a vortex shedding of a flow around a circular domain with stochastic inflow boundary conditions as a benchmark problem. The results are verified by comparison to purely deterministic reference problems and demonstrate high accuracy up to machine precision in capturing the stochastic variations of the limit-cycle.

Stochastic runaway of dynamical systems

International Nuclear Information System (INIS)

Pfirsch, D.; Graeff, P.

1984-10-01

One-dimensional, stochastic, dynamical systems are well studied with respect to their stability properties. Less is known for the higher dimensional case. This paper derives sufficient and necessary criteria for the asymptotic divergence of the entropy (runaway) and sufficient ones for the moments of n-dimensional, stochastic, dynamical systems. The crucial implication is the incompressibility of their flow defined by the equations of motion in configuration space. Two possible extensions to compressible flow systems are outlined. (orig.)

Electric-power systems planning and greenhouse-gas emission management under uncertainty

International Nuclear Information System (INIS)

Li, Y.P.; Huang, G.H.

2012-01-01

Highlight: ►A multistage stochastic integer programming model is developed for planning electric-power systems. ►Uncertain and dynamic information can be incorporated within a multilayer scenario tree. ►This can help minimize system cost under random energy demand and greenhouse gas (GHG) abatement goal. ►Results can support decisions of facility expansion, electricity supply and GHG mitigation. - Abstract: In this study, a multistage interval-stochastic integer programming model is formulated for managing greenhouse gas (GHG) emissions and planning electric-power systems under uncertainty. The developed model can reflect dynamic, interactive, and uncertain characteristics of energy systems. Besides, the model can be used for answering questions related to types, times, demands and mitigations of energy systems planning practices, with the objective of minimizing system cost over a long-time planning horizon. The solutions can help generate electricity-generation schemes and capacity-expansion plans under different GHG-mitigation options and electricity-demand levels. Tradeoffs among system cost, energy security, and emission management can also be tackled. A high system cost will increase renewable energy supply and reduce GHG emission, while a desire for a low cost will run into risks of a high energy deficiency and a high GHG emission.

The brain as a dynamic physical system.

Science.gov (United States)

McKenna, T M; McMullen, T A; Shlesinger, M F

1994-06-01

The brain is a dynamic system that is non-linear at multiple levels of analysis. Characterization of its non-linear dynamics is fundamental to our understanding of brain function. Identifying families of attractors in phase space analysis, an approach which has proven valuable in describing non-linear mechanical and electrical systems, can prove valuable in describing a range of behaviors and associated neural activity including sensory and motor repertoires. Additionally, transitions between attractors may serve as useful descriptors for analysing state changes in neurons and neural ensembles. Recent observations of synchronous neural activity, and the emerging capability to record the spatiotemporal dynamics of neural activity by voltage-sensitive dyes and electrode arrays, provide opportunities for observing the population dynamics of neural ensembles within a dynamic systems context. New developments in the experimental physics of complex systems, such as the control of chaotic systems, selection of attractors, attractor switching and transient states, can be a source of powerful new analytical tools and insights into the dynamics of neural systems.

System dynamics and control with bond graph modeling

CERN Document Server

Kypuros, Javier

2013-01-01

Part I Dynamic System ModelingIntroduction to System DynamicsIntroductionSystem Decomposition and Model ComplexityMathematical Modeling of Dynamic SystemsAnalysis and Design of Dynamic SystemsControl of Dynamic SystemsDiagrams of Dynamic SystemsA Graph-Centered Approach to ModelingSummaryPracticeExercisesBasic Bond Graph ElementsIntroductionPower and Energy VariablesBasic 1-Port ElementsBasic 2-Ports ElementsJunction ElementsSimple Bond Graph ExamplesSummaryPracticeExercisesBond Graph Synthesis and Equation DerivationIntroductionGeneral GuidelinesMechanical TranslationMechanical RotationElectrical CircuitsHydraulic CircuitsMixed SystemsState Equation DerivationState-Space RepresentationsAlgebraic Loops and Derivative CausalitySummaryPracticeExercisesImpedance Bond GraphsIntroductionLaplace Transform of the State-Space EquationBasic 1-Port ImpedancesImpedance Bond Graph SynthesisJunctions, Transformers, and GyratorsEffort and Flow DividersSign ChangesTransfer Function DerivationAlternative Derivation of Transf...

DYNAMICS OF FINANCIAL SYSTEM: A SYSTEM DYNAMICS APPROACH

Directory of Open Access Journals (Sweden)

Girish K Nair

2013-01-01

Full Text Available There are several ratios which define the financial health of an organization but the importance of Net cash flow, Gross income, Net income, Pending bills, Receivable bills, Debt, and Book value can never be undermined as they give the exact picture of the financial condition. While there are several approaches to study the dynamics of these variables, system dynamics based modelling and simulation is one of the modern techniques. The paper explores this method to simulate the before mentioned parameters during production capacity expansion in an electronic industry. Debt and Book value have shown a non-linear pattern of variation which is discussed. The model can be used by the financial experts as a decision support tool in arriving at conclusions in connection to the expansion plans of the organization.

An Axiomatic Representation of System Dynamics

CERN Document Server

Baianu, I

2004-01-01

An axiomatic representation of system dynamics is introduced in terms of categories, functors, organismal supercategories, limits and colimits of diagrams. Specific examples are considered in Complex Systems Biology, such as ribosome biogenesis and Hormonal Control in human subjects. "Fuzzy" Relational Structures are also proposed for flexible representations of biological system dynamics and organization.

Controlling chaos in discontinuous dynamical systems

International Nuclear Information System (INIS)

Danca, Marius-F.

2004-01-01

In this paper we consider the possibility to implement the technique of changes in the system variables to control the chaos introduced by Gueemez and Matias for continuous dynamical systems to a class of discontinuous dynamical systems. The approach is realized via differential inclusions following the Filippov theory. Three practical examples are considered

Solar System Dynamics

Science.gov (United States)

Wisdom, Jack

2002-01-01

In these 18 years, the research has touched every major dynamical problem in the solar system, including: the effect of chaotic zones on the distribution of asteroids, the delivery of meteorites along chaotic pathways, the chaotic motion of Pluto, the chaotic motion of the outer planets and that of the whole solar system, the delivery of short period comets from the Kuiper belt, the tidal evolution of the Uranian arid Galilean satellites, the chaotic tumbling of Hyperion and other irregular satellites, the large chaotic variations of the obliquity of Mars, the evolution of the Earth-Moon system, and the resonant core- mantle dynamics of Earth and Venus. It has introduced new analytical and numerical tools that are in widespread use. Today, nearly every long-term integration of our solar system, its subsystems, and other solar systems uses algorithms that was invented. This research has all been primarily Supported by this sequence of PGG NASA grants. During this period published major investigations of tidal evolution of the Earth-Moon system and of the passage of the Earth and Venus through non-linear core-mantle resonances were completed. It has published a major innovation in symplectic algorithms: the symplectic corrector. A paper was completed on non-perturbative hydrostatic equilibrium.

Dynamical systems in classical mechanics

CERN Document Server

Kozlov, V V

1995-01-01

This book shows that the phenomenon of integrability is related not only to Hamiltonian systems, but also to a wider variety of systems having invariant measures that often arise in nonholonomic mechanics. Each paper presents unique ideas and original approaches to various mathematical problems related to integrability, stability, and chaos in classical dynamics. Topics include… the inverse Lyapunov theorem on stability of equilibria geometrical aspects of Hamiltonian mechanics from a hydrodynamic perspective current unsolved problems in the dynamical systems approach to classical mechanics

A New Bi-Directional Projection Model Based on Pythagorean Uncertain Linguistic Variable

OpenAIRE

Huidong Wang; Shifan He; Xiaohong Pan

2018-01-01

To solve the multi-attribute decision making (MADM) problems with Pythagorean uncertain linguistic variable, an extended bi-directional projection method is proposed. First, we utilize the linguistic scale function to convert uncertain linguistic variable and provide a new projection model, subsequently. Then, to depict the bi-directional projection method, the formative vectors of alternatives and ideal alternatives are defined. Furthermore, a comparative analysis with projection model is co...

Planar dynamical systems selected classical problems

CERN Document Server

Liu, Yirong; Huang, Wentao

2014-01-01

This book presents in an elementary way the recent significant developments in the qualitative theory of planar dynamical systems. The subjects are covered as follows: the studies of center and isochronous center problems, multiple Hopf bifurcations and local and global bifurcations of the equivariant planar vector fields which concern with Hilbert's 16th problem. This book is intended for graduate students, post-doctors and researchers in the area of theories and applications of dynamical systems. For all engineers who are interested the theory of dynamical systems, it is also a reasona

Fault diagnosis for dynamic power system

International Nuclear Information System (INIS)

Thabet, A.; Abdelkrim, M.N.; Boutayeb, M.; Didier, G.; Chniba, S.

2011-01-01

The fault diagnosis problem for dynamic power systems is treated, the nonlinear dynamic model based on a differential algebraic equations is transformed with reduced index to a simple dynamic model. Two nonlinear observers are used for generating the fault signals for comparison purposes, one of them being an extended Kalman estimator and the other a new extended kalman filter with moving horizon with a study of convergence based on the choice of matrix of covariance of the noises of system and measurements. The paper illustrates a simulation study applied on IEEE 3 buses test system.

Systems-Dynamic Analysis for Neighborhood Study

Science.gov (United States)

Systems-dynamic analysis (or system dynamics (SD)) helps planners identify interrelated impacts of transportation and land-use policies on neighborhood-scale economic outcomes for households and businesses, among other applications. This form of analysis can show benefits and tr...

The Uncertain of Scientific Process

Directory of Open Access Journals (Sweden)

Jovina dÁvila Bordoni

2016-10-01

Full Text Available The study assesses the existence of certainty in the scientific process, it seeks the truth, however, faced with the unknown, causes uncertainties and doubts. We used the bibliographical research, in which it systematized the scientific literature on epistemology and knowledge related to the scientific process and the uncertainties that surround him. The scientific process, though continuously seeks the truth, will not attain perfection, because the researcher deals with the unknown. The science seeks constantly new knowledge and progress with the criticism of the mistakes, seeks the truth, however these are provisional. It is concluded that all scientific knowledge is uncertain.

Information Processing Capacity of Dynamical Systems

Science.gov (United States)

Dambre, Joni; Verstraeten, David; Schrauwen, Benjamin; Massar, Serge

2012-07-01

Many dynamical systems, both natural and artificial, are stimulated by time dependent external signals, somehow processing the information contained therein. We demonstrate how to quantify the different modes in which information can be processed by such systems and combine them to define the computational capacity of a dynamical system. This is bounded by the number of linearly independent state variables of the dynamical system, equaling it if the system obeys the fading memory condition. It can be interpreted as the total number of linearly independent functions of its stimuli the system can compute. Our theory combines concepts from machine learning (reservoir computing), system modeling, stochastic processes, and functional analysis. We illustrate our theory by numerical simulations for the logistic map, a recurrent neural network, and a two-dimensional reaction diffusion system, uncovering universal trade-offs between the non-linearity of the computation and the system's short-term memory.

Information Processing Capacity of Dynamical Systems

Science.gov (United States)

Dambre, Joni; Verstraeten, David; Schrauwen, Benjamin; Massar, Serge

2012-01-01

Many dynamical systems, both natural and artificial, are stimulated by time dependent external signals, somehow processing the information contained therein. We demonstrate how to quantify the different modes in which information can be processed by such systems and combine them to define the computational capacity of a dynamical system. This is bounded by the number of linearly independent state variables of the dynamical system, equaling it if the system obeys the fading memory condition. It can be interpreted as the total number of linearly independent functions of its stimuli the system can compute. Our theory combines concepts from machine learning (reservoir computing), system modeling, stochastic processes, and functional analysis. We illustrate our theory by numerical simulations for the logistic map, a recurrent neural network, and a two-dimensional reaction diffusion system, uncovering universal trade-offs between the non-linearity of the computation and the system's short-term memory. PMID:22816038

Overcoming Learning Aversion in Evaluating and Managing Uncertain Risks.

Science.gov (United States)

Cox, Louis Anthony Tony

2015-10-01

Decision biases can distort cost-benefit evaluations of uncertain risks, leading to risk management policy decisions with predictably high retrospective regret. We argue that well-documented decision biases encourage learning aversion, or predictably suboptimal learning and premature decision making in the face of high uncertainty about the costs, risks, and benefits of proposed changes. Biases such as narrow framing, overconfidence, confirmation bias, optimism bias, ambiguity aversion, and hyperbolic discounting of the immediate costs and delayed benefits of learning, contribute to deficient individual and group learning, avoidance of information seeking, underestimation of the value of further information, and hence needlessly inaccurate risk-cost-benefit estimates and suboptimal risk management decisions. In practice, such biases can create predictable regret in selection of potential risk-reducing regulations. Low-regret learning strategies based on computational reinforcement learning models can potentially overcome some of these suboptimal decision processes by replacing aversion to uncertain probabilities with actions calculated to balance exploration (deliberate experimentation and uncertainty reduction) and exploitation (taking actions to maximize the sum of expected immediate reward, expected discounted future reward, and value of information). We discuss the proposed framework for understanding and overcoming learning aversion and for implementing low-regret learning strategies using regulation of air pollutants with uncertain health effects as an example. © 2015 Society for Risk Analysis.

Attractors for discrete periodic dynamical systems

Science.gov (United States)

John E. Franke; James F. Selgrade

2003-01-01

A mathematical framework is introduced to study attractors of discrete, nonautonomous dynamical systems which depend periodically on time. A structure theorem for such attractors is established which says that the attractor of a time-periodic dynamical system is the unin of attractors of appropriate autonomous maps. If the nonautonomous system is a perturbation of an...

Dynamically analyzing cell interactions in biological environments using multiagent social learning framework.

Science.gov (United States)

Zhang, Chengwei; Li, Xiaohong; Li, Shuxin; Feng, Zhiyong

2017-09-20

Biological environment is uncertain and its dynamic is similar to the multiagent environment, thus the research results of the multiagent system area can provide valuable insights to the understanding of biology and are of great significance for the study of biology. Learning in a multiagent environment is highly dynamic since the environment is not stationary anymore and each agent's behavior changes adaptively in response to other coexisting learners, and vice versa. The dynamics becomes more unpredictable when we move from fixed-agent interaction environments to multiagent social learning framework. Analytical understanding of the underlying dynamics is important and challenging. In this work, we present a social learning framework with homogeneous learners (e.g., Policy Hill Climbing (PHC) learners), and model the behavior of players in the social learning framework as a hybrid dynamical system. By analyzing the dynamical system, we obtain some conditions about convergence or non-convergence. We experimentally verify the predictive power of our model using a number of representative games. Experimental results confirm the theoretical analysis. Under multiagent social learning framework, we modeled the behavior of agent in biologic environment, and theoretically analyzed the dynamics of the model. We present some sufficient conditions about convergence or non-convergence and prove them theoretically. It can be used to predict the convergence of the system.

Vibration control of uncertain multiple launch rocket system using radial basis function neural network

Science.gov (United States)

Li, Bo; Rui, Xiaoting

2018-01-01

Poor dispersion characteristics of rockets due to the vibration of Multiple Launch Rocket System (MLRS) have always restricted the MLRS development for several decades. Vibration control is a key technique to improve the dispersion characteristics of rockets. For a mechanical system such as MLRS, the major difficulty in designing an appropriate control strategy that can achieve the desired vibration control performance is to guarantee the robustness and stability of the control system under the occurrence of uncertainties and nonlinearities. To approach this problem, a computed torque controller integrated with a radial basis function neural network is proposed to achieve the high-precision vibration control for MLRS. In this paper, the vibration response of a computed torque controlled MLRS is described. The azimuth and elevation mechanisms of the MLRS are driven by permanent magnet synchronous motors and supposed to be rigid. First, the dynamic model of motor-mechanism coupling system is established using Lagrange method and field-oriented control theory. Then, in order to deal with the nonlinearities, a computed torque controller is designed to control the vibration of the MLRS when it is firing a salvo of rockets. Furthermore, to compensate for the lumped uncertainty due to parametric variations and un-modeled dynamics in the design of the computed torque controller, a radial basis function neural network estimator is developed to adapt the uncertainty based on Lyapunov stability theory. Finally, the simulated results demonstrate the effectiveness of the proposed control system and show that the proposed controller is robust with regard to the uncertainty.

Dynamical system approach to phyllotaxis

DEFF Research Database (Denmark)

D'ovidio, Francesco; Mosekilde, Erik

2000-01-01

and not a dynamical system, mainly because new active elements are added at each step, and thus the dimension of the "natural" phase space is not conserved. Here a construction is presented by which a well defined dynamical system can be obtained, and a bifurcation analysis can be carried out. Stable and unstable...... of the Jacobian, and thus the eigenvalues, is given. It is likely that problems of the above type often arise in biology, and especially in morphogenesis, where growing systems are modeled....

Inferring about the extinction of a species using certain and uncertain sightings.

Science.gov (United States)

Kodikara, Saritha; Demirhan, Haydar; Stone, Lewi

2018-04-07

The sighting record of threatened species is often used to infer the possibility of extinction. Most of these sightings have uncertain validity. Solow and Beet(2014) developed two models using a Bayesian approach which allowed for uncertainty in the sighting record by formally incorporating both certain and uncertain sightings, but in different ways. Interestingly, the two methods give completely different conclusions concerning the extinction of the Ivory-billed Woodpecker. We further examined these two methods to provide a mathematical explanation, and to explore in more depth, as to why the results differed from one another. It was found that the first model was more sensitive to the last uncertain sighting, while the second was more sensitive to the last certain sighting. The difficulties in choosing the appropriate model are discussed. Copyright © 2018 Elsevier Ltd. All rights reserved.

System dynamics modelling of situation awareness

CSIR Research Space (South Africa)

Oosthuizen, R

2015-11-01

Full Text Available . The feedback loops and delays in the Command and Control system also contribute to the complex dynamic behavior. This paper will build on existing situation awareness models to develop a System Dynamics model to support a qualitative investigation through...

Uncertain R and D, backstop technology and GHGs stabilization

International Nuclear Information System (INIS)

Bosetti, Valentina; Tavoni, Massimo

2009-01-01

This paper analyses optimal investments in innovation when dealing with a stringent climate target and with the uncertain effectiveness of R and D. The innovation needed to achieve the deep cut in emissions is modeled by a backstop carbon-free technology whose cost depends on R and D investments. To better represent the process of technological progress, we assume that R and D effectiveness is uncertain. By means of a simple analytical model, we show how accounting for the uncertainty that characterizes technological advancement yields higher investments in innovation and lower policy costs. We then confirm the results via a numerical analysis performed with a stochastic version of WITCH, an energy-economy-climate model. The results stress the importance of a correct specification of the technological change process in economy-climate models. (author)

Dynamical critical phenomena in driven-dissipative systems.

Science.gov (United States)

Sieberer, L M; Huber, S D; Altman, E; Diehl, S

2013-05-10

We explore the nature of the Bose condensation transition in driven open quantum systems, such as exciton-polariton condensates. Using a functional renormalization group approach formulated in the Keldysh framework, we characterize the dynamical critical behavior that governs decoherence and an effective thermalization of the low frequency dynamics. We identify a critical exponent special to the driven system, showing that it defines a new dynamical universality class. Hence critical points in driven systems lie beyond the standard classification of equilibrium dynamical phase transitions. We show how the new critical exponent can be probed in experiments with driven cold atomic systems and exciton-polariton condensates.

Parameterizing Coefficients of a POD-Based Dynamical System

Science.gov (United States)

Kalb, Virginia L.

2010-01-01

A method of parameterizing the coefficients of a dynamical system based of a proper orthogonal decomposition (POD) representing the flow dynamics of a viscous fluid has been introduced. (A brief description of POD is presented in the immediately preceding article.) The present parameterization method is intended to enable construction of the dynamical system to accurately represent the temporal evolution of the flow dynamics over a range of Reynolds numbers. The need for this or a similar method arises as follows: A procedure that includes direct numerical simulation followed by POD, followed by Galerkin projection to a dynamical system has been proven to enable representation of flow dynamics by a low-dimensional model at the Reynolds number of the simulation. However, a more difficult task is to obtain models that are valid over a range of Reynolds numbers. Extrapolation of low-dimensional models by use of straightforward Reynolds-number-based parameter continuation has proven to be inadequate for successful prediction of flows. A key part of the problem of constructing a dynamical system to accurately represent the temporal evolution of the flow dynamics over a range of Reynolds numbers is the problem of understanding and providing for the variation of the coefficients of the dynamical system with the Reynolds number. Prior methods do not enable capture of temporal dynamics over ranges of Reynolds numbers in low-dimensional models, and are not even satisfactory when large numbers of modes are used. The basic idea of the present method is to solve the problem through a suitable parameterization of the coefficients of the dynamical system. The parameterization computations involve utilization of the transfer of kinetic energy between modes as a function of Reynolds number. The thus-parameterized dynamical system accurately predicts the flow dynamics and is applicable to a range of flow problems in the dynamical regime around the Hopf bifurcation. Parameter

Calibration of uncertain inputs to computer models using experimentally measured quantities and the BMARS emulator

International Nuclear Information System (INIS)

Stripling, H.F.; McClarren, R.G.; Kuranz, C.C.; Grosskopf, M.J.; Rutter, E.; Torralva, B.R.

2011-01-01

We present a method for calibrating the uncertain inputs to a computer model using available experimental data. The goal of the procedure is to produce posterior distributions of the uncertain inputs such that when samples from the posteriors are used as inputs to future model runs, the model is more likely to replicate (or predict) the experimental response. The calibration is performed by sampling the space of the uncertain inputs, using the computer model (or, more likely, an emulator for the computer model) to assign weights to the samples, and applying the weights to produce the posterior distributions and generate predictions of new experiments within confidence bounds. The method is similar to the Markov chain Monte Carlo (MCMC) calibration methods with independent sampling with the exception that we generate samples beforehand and replace the candidate acceptance routine with a weighting scheme. We apply our method to the calibration of a Hyades 2D model of laser energy deposition in beryllium. We employ a Bayesian Multivariate Adaptive Regression Splines (BMARS) emulator as a surrogate for Hyades 2D. We treat a range of uncertainties in our system, including uncertainties in the experimental inputs, experimental measurement error, and systematic experimental timing errors. The results of the calibration are posterior distributions that both agree with intuition and improve the accuracy and decrease the uncertainty in experimental predictions. (author)

Stochastic Thermodynamics: A Dynamical Systems Approach

Directory of Open Access Journals (Sweden)

Tanmay Rajpurohit

2017-12-01

Full Text Available In this paper, we develop an energy-based, large-scale dynamical system model driven by Markov diffusion processes to present a unified framework for statistical thermodynamics predicated on a stochastic dynamical systems formalism. Specifically, using a stochastic state space formulation, we develop a nonlinear stochastic compartmental dynamical system model characterized by energy conservation laws that is consistent with statistical thermodynamic principles. In particular, we show that the difference between the average supplied system energy and the average stored system energy for our stochastic thermodynamic model is a martingale with respect to the system filtration. In addition, we show that the average stored system energy is equal to the mean energy that can be extracted from the system and the mean energy that can be delivered to the system in order to transfer it from a zero energy level to an arbitrary nonempty subset in the state space over a finite stopping time.

Dynamics of the diffusive DM-DE interaction – Dynamical system approach

Energy Technology Data Exchange (ETDEWEB)

Haba, Zbigniew [Institute of Theoretical Physics, University of Wroclaw, Plac Maxa Borna 9, 50-204 Wrocław (Poland); Stachowski, Aleksander; Szydłowski, Marek, E-mail: zhab@ift.uni.wroc.pl, E-mail: aleksander.stachowski@uj.edu.pl, E-mail: marek.szydlowski@uj.edu.pl [Astronomical Observatory, Jagiellonian University, Orla 171, 30-244 Krakow (Poland)

2016-07-01

We discuss dynamics of a model of an energy transfer between dark energy (DE) and dark matter (DM) . The energy transfer is determined by a non-conservation law resulting from a diffusion of dark matter in an environment of dark energy. The relativistic invariance defines the diffusion in a unique way. The system can contain baryonic matter and radiation which do not interact with the dark sector. We treat the Friedman equation and the conservation laws as a closed dynamical system. The dynamics of the model is examined using the dynamical systems methods for demonstration how solutions depend on initial conditions. We also fit the model parameters using astronomical observation: SNIa, H ( z ), BAO and Alcock-Paczynski test. We show that the model with diffuse DM-DE is consistent with the data.

Optimizing Bus Frequencies under Uncertain Demand: Case Study of the Transit Network in a Developing City

Directory of Open Access Journals (Sweden)

Zhengfeng Huang

2013-01-01

Full Text Available Various factors can make predicting bus passenger demand uncertain. In this study, a bilevel programming model for optimizing bus frequencies based on uncertain bus passenger demand is formulated. There are two terms constituting the upper-level objective. The first is transit network cost, consisting of the passengers’ expected travel time and operating costs, and the second is transit network robustness performance, indicated by the variance in passenger travel time. The second term reflects the risk aversion of decision maker, and it can make the most uncertain demand be met by the bus operation with the optimal transit frequency. With transit link’s proportional flow eigenvalues (mean and covariance obtained from the lower-level model, the upper-level objective is formulated by the analytical method. In the lower-level model, the above two eigenvalues are calculated by analyzing the propagation of mean transit trips and their variation in the optimal strategy transit assignment process. The genetic algorithm (GA used to solve the model is tested in an example network. Finally, the model is applied to determining optimal bus frequencies in the city of Liupanshui, China. The total cost of the transit system in Liupanshui can be reduced by about 6% via this method.

Reconceptualizing Learning as a Dynamical System.

Science.gov (United States)

Ennis, Catherine D.

1992-01-01

Dynamical systems theory can increase our understanding of the constantly evolving learning process. Current research using experimental and interpretive paradigms focuses on describing the attractors and constraints stabilizing the educational process. Dynamical systems theory focuses attention on critical junctures in the learning process as…

Introduction to turbulent dynamical systems in complex systems

CERN Document Server

Majda, Andrew J

2016-01-01

This volume is a research expository article on the applied mathematics of turbulent dynamical systems through the paradigm of modern applied mathematics. It involves the blending of rigorous mathematical theory, qualitative and quantitative modeling, and novel numerical procedures driven by the goal of understanding physical phenomena which are of central importance to the field. The contents cover general framework, concrete examples, and instructive qualitative models. Accessible open problems are mentioned throughout. Topics covered include: · Geophysical flows with rotation, topography, deterministic and random forcing · New statistical energy principles for general turbulent dynamical systems, with applications · Linear statistical response theory combined with information theory to cope with model errors · Reduced low order models · Recent mathematical strategies for online data assimilation of turbulent dynamical systems as well as rigorous results for finite ensemble Kalman filters The volume wi...

Optimal Passive Dynamics for Physical Interaction: Catching a Mass

Directory of Open Access Journals (Sweden)

Kevin Kemper

2013-05-01

Full Text Available For manipulation tasks in uncertain environments, intentionally designed series impedance in mechanical systems can provide significant benefits that cannot be achieved in software. Traditionally, the design of actuated systems revolves around sizing torques, speeds, and control strategies without considering the system’s passive dynamics. However, the passive dynamics of the mechanical system, including inertia, stiffness, and damping along with other parameters such as torque and stroke limits often impose performance limitations that cannot be overcome with software control. In this paper, we develop relationships between an actuator’s passive dynamics and the resulting performance for the purpose of better understanding how to tune the passive dynamics for catching an unexpected object. We use a mathematically optimal controller subject to force limitations to stop the incoming object without breaking contact and bouncing. The use of an optimal controller is important so that our results directly reflect the physical system’s performance. We analytically calculate the maximum velocity that can be caught by a realistic actuator with limitations such as force and stroke limits. The results show that in order to maximize the velocity of an object that can be caught without exceeding the actuator’s torque and stroke limits, a soft spring along with a strong damper will be desired.

Identification of Parameters in Active Magnetic Bearing Systems

DEFF Research Database (Denmark)

Lauridsen, Jonas Skjødt; Voigt, Andreas Jauernik; Mandrup-Poulsen, Christian

2016-01-01

A method for identifying uncertain parameters in Active Magnetic Bearing (AMB) based rotordynamic systems is introduced and adapted for experimental application. The Closed Loop Identification (CLI) method is utilised to estimate the current/force factors Ki and the displacement/force factors Ks...... as well as a time constant Τe for a first order approxima-tion of unknown actuator dynamics. To assess the precision with which CLI method can be employed to estimate AMBparameters the factors Ki, estimated using the CLI method, is compared to Ki factors attained through a Static Loading(SL) method....... The CLI method and SL method produce similar results, indicating that the CLI method is able to performclosed loop identification of uncertain AMB parameters....

Some problems of dynamical systems on three dimensional manifolds

International Nuclear Information System (INIS)

Dong Zhenxie.

1985-08-01

It is important to study the dynamical systems on 3-dimensional manifolds, its importance is showing up in its close relation with our life. Because of the complication of topological structure of Dynamical systems on 3-dimensional manifolds, generally speaking, the search for 3-dynamical systems is not easier than 2-dynamical systems. This paper is a summary of the partial result of dynamical systems on 3-dimensional manifolds. (author)

Dynamic Reconfiguration in Mobile Systems

NARCIS (Netherlands)

Smit, Gerardus Johannes Maria; Glesner, Manfred; Zipf, Peter; Smit, L.T.; Havinga, Paul J.M.; Heysters, P.M.; Renovell, Michel; Rosien, M.A.J.

Dynamically reconfigurable systems have the potential of realising efficient systems as well as providing adaptability to changing system requirements. Such systems are suitable for future mobile multimedia systems that have limited battery resources, must handle diverse data types, and must operate

Study of Robust H∞ Filtering Application in Loosely Coupled INS/GPS System

Directory of Open Access Journals (Sweden)

Lin Zhao

2014-01-01

model, unstable model case is considered. We give an explanation for Kalman filter divergence under uncertain dynamic system and simultaneously investigate the relationship between H∞ filter and Kalman filter. A loosely coupled INS/GPS simulation system is given here to verify this application. Result shows that the robust H∞ filter has a better performance when system suffers uncertainty; also it is more robust compared to the conventional Kalman filter.

Research on Sliding Mode Control for Steer-by-Wire System in Forklift

Directory of Open Access Journals (Sweden)

Huang Jun-Jie

2017-01-01

Full Text Available Aiming at steering stability and wheel angle tracking of steer-by-wire (SBW three wheeled forklift, steering dynamic model and SBW system mathematical model of three wheeled forklift are established. A control strategy for the ideal transmission ratio is introduced based on this model, which ensures forklift steering gain invariant. A sliding mode controller can then be designed based on the bound information of uncertain system parameters, uncertain self-aligning torque, and external disturbances. The results of simulation show the control strategies above can effectively reduce the sideslip angle when the forklift is steering and improve the sensitivity and stability of the steering forklift; at the same time can effectively restrain the parameter perturbation of internal system and external disturbance, which improves the tracking performance of the wheel angle.

Understanding and Modeling Teams As Dynamical Systems

Science.gov (United States)

Gorman, Jamie C.; Dunbar, Terri A.; Grimm, David; Gipson, Christina L.

2017-01-01

By its very nature, much of teamwork is distributed across, and not stored within, interdependent people working toward a common goal. In this light, we advocate a systems perspective on teamwork that is based on general coordination principles that are not limited to cognitive, motor, and physiological levels of explanation within the individual. In this article, we present a framework for understanding and modeling teams as dynamical systems and review our empirical findings on teams as dynamical systems. We proceed by (a) considering the question of why study teams as dynamical systems, (b) considering the meaning of dynamical systems concepts (attractors; perturbation; synchronization; fractals) in the context of teams, (c) describe empirical studies of team coordination dynamics at the perceptual-motor, cognitive-behavioral, and cognitive-neurophysiological levels of analysis, and (d) consider the theoretical and practical implications of this approach, including new kinds of explanations of human performance and real-time analysis and performance modeling. Throughout our discussion of the topics we consider how to describe teamwork using equations and/or modeling techniques that describe the dynamics. Finally, we consider what dynamical equations and models do and do not tell us about human performance in teams and suggest future research directions in this area. PMID:28744231

Dynamic MR imaging of the musculoskeletal system

International Nuclear Information System (INIS)

Shah, A.S.; Hylton, H.; Hentz, V.R.; Schattner, P.

1991-01-01

This paper reports on dynamic MR imaging which is an MR technique that allows imaging of the musculoskeletal system in motion. Current methods for observing the articulation of muscles and joints are limited to acquisition of stationary images at different spatial orientations. These images are then replayed from computer memory to simulate motion. Unlike stationary acquisition, dynamic MR imaging allows the volume of interest to be subjected to motion and dynamic stress, which is important for detecting stress-induced pathology. To demonstrate the utility of dynamic MR imaging, a system for imaging a moving wrist has been developed. The system consists of apparatus capable of providing simultaneous radialulnar deviation and flexion-extension, and hardware for system control and acquisition gating. The apparatus is mounted on the patient bed and is transferable to a variety of standard clinical MR imaging systems. Images were obtained during motion, and the ability of dynamic MR imaging to accurately image the moving wrist with very little motion artifact was demonstrated

Partial dynamical systems, fell bundles and applications

CERN Document Server

Exel, Ruy

2017-01-01

Partial dynamical systems, originally developed as a tool to study algebras of operators in Hilbert spaces, has recently become an important branch of algebra. Its most powerful results allow for understanding structural properties of algebras, both in the purely algebraic and in the C*-contexts, in terms of the dynamical properties of certain systems which are often hiding behind algebraic structures. The first indication that the study of an algebra using partial dynamical systems may be helpful is the presence of a grading. While the usual theory of graded algebras often requires gradings to be saturated, the theory of partial dynamical systems is especially well suited to treat nonsaturated graded algebras which are in fact the source of the notion of "partiality". One of the main results of the book states that every graded algebra satisfying suitable conditions may be reconstructed from a partial dynamical system via a process called the partial crossed product. Running in parallel with partial dynamica...

Dynamical systems on 2- and 3-manifolds

CERN Document Server

Grines, Viacheslav Z; Pochinka, Olga V

2016-01-01

This book provides an introduction to the topological classification of smooth structurally stable diffeomorphisms on closed orientable 2- and 3-manifolds.The topological classification is one of the main problems of the theory of dynamical systems and the results presented in this book are mostly for dynamical systems satisfying Smale's Axiom A. The main results on the topological classification of discrete dynamical systems are widely scattered among many papers and surveys. This book presents these results fluidly, systematically, and for the first time in one publication. Additionally, this book discusses the recent results on the topological classification of Axiom A diffeomorphisms focusing on the nontrivial effects of the dynamical systems on 2- and 3-manifolds. The classical methods and approaches which are considered to be promising for the further research are also discussed. < The reader needs to be familiar with the basic concepts of the qualitative theory of dynamical systems which are present...

Narcissistic group dynamics of multiparty systems

NARCIS (Netherlands)

Schruijer, S.G.L.

2015-01-01

Purpose – This paper aims to introduce and illustrate the notion of narcissistic group dynamics. It is claimed that narcissism does not simply reside within individuals but can be characteristic of groups and social systems. In this case, the focus is on narcissistic dynamics in multiparty systems.

An Active Fault-Tolerant Control Method Ofunmanned Underwater Vehicles with Continuous and Uncertain Faults

Directory of Open Access Journals (Sweden)

Daqi Zhu

2008-11-01

Full Text Available This paper introduces a novel thruster fault diagnosis and accommodation system for open-frame underwater vehicles with abrupt faults. The proposed system consists of two subsystems: a fault diagnosis subsystem and a fault accommodation sub-system. In the fault diagnosis subsystem a ICMAC(Improved Credit Assignment Cerebellar Model Articulation Controllers neural network is used to realize the on-line fault identification and the weighting matrix computation. The fault accommodation subsystem uses a control algorithm based on weighted pseudo-inverse to find the solution of the control allocation problem. To illustrate the proposed method effective, simulation example, under multi-uncertain abrupt faults, is given in the paper.

Nonlinear dynamics of fractional order Duffing system

International Nuclear Information System (INIS)

Li, Zengshan; Chen, Diyi; Zhu, Jianwei; Liu, Yongjian

2015-01-01

In this paper, we analyze the nonlinear dynamics of fractional order Duffing system. First, we present the fractional order Duffing system and the numerical algorithm. Second, nonlinear dynamic behaviors of Duffing system with a fixed fractional order is studied by using bifurcation diagrams, phase portraits, Poincare maps and time domain waveforms. The fractional order Duffing system shows some interesting dynamical behaviors. Third, a series of Duffing systems with different fractional orders are analyzed by using bifurcation diagrams. The impacts of fractional orders on the tendency of dynamical motion, the periodic windows in chaos, the bifurcation points and the distance between the first and the last bifurcation points are respectively studied, in which some basic laws are discovered and summarized. This paper reflects that the integer order system and the fractional order one have close relationship and an integer order system is a special case of fractional order ones.

Dynamics of Open Systems with Affine Maps

International Nuclear Information System (INIS)

Zhang Da-Jian; Liu Chong-Long; Tong Dian-Min

2015-01-01

Many quantum systems of interest are initially correlated with their environments and the reduced dynamics of open systems are an interesting while challenging topic. Affine maps, as an extension of completely positive maps, are a useful tool to describe the reduced dynamics of open systems with initial correlations. However, it is unclear what kind of initial state shares an affine map. In this study, we give a sufficient condition of initial states, in which the reduced dynamics can always be described by an affine map. Our result shows that if the initial states of the combined system constitute a convex set, and if the correspondence between the initial states of the open system and those of the combined system, defined by taking the partial trace, is a bijection, then the reduced dynamics of the open system can be described by an affine map. (paper)

The Emergency Vehicle Routing Problem with Uncertain Demand under Sustainability Environments

Directory of Open Access Journals (Sweden)

Jin Qin

2017-02-01

Full Text Available The reasonable utilization of limited resources is critical to realize the sustainable developments. In the initial 72-h crucial rescue period after the disaster, emergency supplies have always been insufficient and the demands in the affected area have always been uncertain. In order to improve timeliness, utilization and sustainability of emergency service, the allocation of the emergency supplies and the emergency vehicle routes should be determined simultaneously. Assuming the uncertain demands follow normal distribution, an optimization model for the emergency vehicle routing, by considering the insufficient supplies and the uncertain demands, is developed. The objective function is applied to minimize the total costs, including the penalty costs induced by more or less supplies than the actual demands at all demand points, as well as the constraints of the time windows and vehicle load capacity taken into account. In more details, a solution method for the model, based on the genetic algorithm, is proposed, which solves the problem in two stages. A numerical example is presented to demonstrate the efficiency and validity of the proposed model and algorithm.

Rendezvous with connectivity preservation for multi-robot systems with an unknown leader

Science.gov (United States)

Dong, Yi

2018-02-01

This paper studies the leader-following rendezvous problem with connectivity preservation for multi-agent systems composed of uncertain multi-robot systems subject to external disturbances and an unknown leader, both of which are generated by a so-called exosystem with parametric uncertainty. By combining internal model design, potential function technique and adaptive control, two distributed control strategies are proposed to maintain the connectivity of the communication network, to achieve the asymptotic tracking of all the followers to the output of the unknown leader system, as well as to reject unknown external disturbances. It is also worth to mention that the uncertain parameters in the multi-robot systems and exosystem are further allowed to belong to unknown and unbounded sets when applying the second fully distributed control law containing a dynamic gain inspired by high-gain adaptive control or self-tuning regulator.

Energy trading and pricing in microgrids with uncertain energy supply

DEFF Research Database (Denmark)

Ma, Kai; Hu, Shubing; Yang, Jie

2017-01-01

This paper studies an energy trading and pricing problem for microgrids with uncertain energy supply. The energy provider with the renewable energy (RE) generation (wind power) determines the energy purchase from the electricity markets and the pricing strategy for consumers to maximize its profi....... In particular, the uncertainty of the energy supply from the energy provider is considered. Simulation results show that the energy provider can obtain more profit using the proposed decision-making scheme.......This paper studies an energy trading and pricing problem for microgrids with uncertain energy supply. The energy provider with the renewable energy (RE) generation (wind power) determines the energy purchase from the electricity markets and the pricing strategy for consumers to maximize its profit...

Novel density-based and hierarchical density-based clustering algorithms for uncertain data.

Science.gov (United States)

Zhang, Xianchao; Liu, Han; Zhang, Xiaotong

2017-09-01

Uncertain data has posed a great challenge to traditional clustering algorithms. Recently, several algorithms have been proposed for clustering uncertain data, and among them density-based techniques seem promising for handling data uncertainty. However, some issues like losing uncertain information, high time complexity and nonadaptive threshold have not been addressed well in the previous density-based algorithm FDBSCAN and hierarchical density-based algorithm FOPTICS. In this paper, we firstly propose a novel density-based algorithm PDBSCAN, which improves the previous FDBSCAN from the following aspects: (1) it employs a more accurate method to compute the probability that the distance between two uncertain objects is less than or equal to a boundary value, instead of the sampling-based method in FDBSCAN; (2) it introduces new definitions of probability neighborhood, support degree, core object probability, direct reachability probability, thus reducing the complexity and solving the issue of nonadaptive threshold (for core object judgement) in FDBSCAN. Then, we modify the algorithm PDBSCAN to an improved version (PDBSCANi), by using a better cluster assignment strategy to ensure that every object will be assigned to the most appropriate cluster, thus solving the issue of nonadaptive threshold (for direct density reachability judgement) in FDBSCAN. Furthermore, as PDBSCAN and PDBSCANi have difficulties for clustering uncertain data with non-uniform cluster density, we propose a novel hierarchical density-based algorithm POPTICS by extending the definitions of PDBSCAN, adding new definitions of fuzzy core distance and fuzzy reachability distance, and employing a new clustering framework. POPTICS can reveal the cluster structures of the datasets with different local densities in different regions better than PDBSCAN and PDBSCANi, and it addresses the issues in FOPTICS. Experimental results demonstrate the superiority of our proposed algorithms over the existing

Robust LS-SVM-based adaptive constrained control for a class of uncertain nonlinear systems with time-varying predefined performance