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.

Method and system for training dynamic nonlinear adaptive filters which have embedded memory

Science.gov (United States)

Rabinowitz, Matthew (Inventor)

2002-01-01

Described herein is a method and system for training nonlinear adaptive filters (or neural networks) which have embedded memory. Such memory can arise in a multi-layer finite impulse response (FIR) architecture, or an infinite impulse response (IIR) architecture. We focus on filter architectures with separate linear dynamic components and static nonlinear components. Such filters can be structured so as to restrict their degrees of computational freedom based on a priori knowledge about the dynamic operation to be emulated. The method is detailed for an FIR architecture which consists of linear FIR filters together with nonlinear generalized single layer subnets. For the IIR case, we extend the methodology to a general nonlinear architecture which uses feedback. For these dynamic architectures, we describe how one can apply optimization techniques which make updates closer to the Newton direction than those of a steepest descent method, such as backpropagation. We detail a novel adaptive modified Gauss-Newton optimization technique, which uses an adaptive learning rate to determine both the magnitude and direction of update steps. For a wide range of adaptive filtering applications, the new training algorithm converges faster and to a smaller value of cost than both steepest-descent methods such as backpropagation-through-time, and standard quasi-Newton methods. We apply the algorithm to modeling the inverse of a nonlinear dynamic tracking system 5, as well as a nonlinear amplifier 6.

Adaptive synchronization between two different order and topology dynamical systems

International Nuclear Information System (INIS)

Bowong, S.; Moukam Kakmeni, F.M.; Yamapi, R.

2006-07-01

This contribution studies adaptive synchronization between two dynamical systems of different order whose topological structure is also different. By order we mean the number of first order differential equations. The problem is closely related to the synchronization of strictly different systems. The master system is given by a sixth order equation with chaotic behavior whereas the slave system is a fourth-order nonautonomous with rational nonlinear terms. Based on the Lyapunov stability theory, sufficient conditions for the synchronization have been analyzed theoretically and numerically. (author)

Robust Adaptive Stabilization of Linear Time-Invariant Dynamic Systems by Using Fractional-Order Holds and Multirate Sampling Controls

Directory of Open Access Journals (Sweden)

S. Alonso-Quesada

2010-01-01

Full Text Available This paper presents a strategy for designing a robust discrete-time adaptive controller for stabilizing linear time-invariant (LTI continuous-time dynamic systems. Such systems may be unstable and noninversely stable in the worst case. A reduced-order model is considered to design the adaptive controller. The control design is based on the discretization of the system with the use of a multirate sampling device with fast-sampled control signal. A suitable on-line adaptation of the multirate gains guarantees the stability of the inverse of the discretized estimated model, which is used to parameterize the adaptive controller. A dead zone is included in the parameters estimation algorithm for robustness purposes under the presence of unmodeled dynamics in the controlled dynamic system. The adaptive controller guarantees the boundedness of the system measured signal for all time. Some examples illustrate the efficacy of this control strategy.

Adaptive Event-Triggered Control Based on Heuristic Dynamic Programming for Nonlinear Discrete-Time Systems.

Science.gov (United States)

Dong, Lu; Zhong, Xiangnan; Sun, Changyin; He, Haibo

2017-07-01

This paper presents the design of a novel adaptive event-triggered control method based on the heuristic dynamic programming (HDP) technique for nonlinear discrete-time systems with unknown system dynamics. In the proposed method, the control law is only updated when the event-triggered condition is violated. Compared with the periodic updates in the traditional adaptive dynamic programming (ADP) control, the proposed method can reduce the computation and transmission cost. An actor-critic framework is used to learn the optimal event-triggered control law and the value function. Furthermore, a model network is designed to estimate the system state vector. The main contribution of this paper is to design a new trigger threshold for discrete-time systems. A detailed Lyapunov stability analysis shows that our proposed event-triggered controller can asymptotically stabilize the discrete-time systems. Finally, we test our method on two different discrete-time systems, and the simulation results are included.

Adaptive Sliding Mode Control of Dynamic Systems Using Double Loop Recurrent Neural Network Structure.

Science.gov (United States)

Fei, Juntao; Lu, Cheng

2018-04-01

In this paper, an adaptive sliding mode control system using a double loop recurrent neural network (DLRNN) structure is proposed for a class of nonlinear dynamic systems. A new three-layer RNN is proposed to approximate unknown dynamics with two different kinds of feedback loops where the firing weights and output signal calculated in the last step are stored and used as the feedback signals in each feedback loop. Since the new structure has combined the advantages of internal feedback NN and external feedback NN, it can acquire the internal state information while the output signal is also captured, thus the new designed DLRNN can achieve better approximation performance compared with the regular NNs without feedback loops or the regular RNNs with a single feedback loop. The new proposed DLRNN structure is employed in an equivalent controller to approximate the unknown nonlinear system dynamics, and the parameters of the DLRNN are updated online by adaptive laws to get favorable approximation performance. To investigate the effectiveness of the proposed controller, the designed adaptive sliding mode controller with the DLRNN is applied to a -axis microelectromechanical system gyroscope to control the vibrating dynamics of the proof mass. Simulation results demonstrate that the proposed methodology can achieve good tracking property, and the comparisons of the approximation performance between radial basis function NN, RNN, and DLRNN show that the DLRNN can accurately estimate the unknown dynamics with a fast speed while the internal states of DLRNN are more stable.

Quantum Information Biology: From Theory of Open Quantum Systems to Adaptive Dynamics

Science.gov (United States)

Asano, Masanari; Basieva, Irina; Khrennikov, Andrei; Ohya, Masanori; Tanaka, Yoshiharu; Yamato, Ichiro

This chapter reviews quantum(-like) information biology (QIB). Here biology is treated widely as even covering cognition and its derivatives: psychology and decision making, sociology, and behavioral economics and finances. QIB provides an integrative description of information processing by bio-systems at all scales of life: from proteins and cells to cognition, ecological and social systems. Mathematically QIB is based on the theory of adaptive quantum systems (which covers also open quantum systems). Ideologically QIB is based on the quantum-like (QL) paradigm: complex bio-systems process information in accordance with the laws of quantum information and probability. This paradigm is supported by plenty of statistical bio-data collected at all bio-scales. QIB re ects the two fundamental principles: a) adaptivity; and, b) openness (bio-systems are fundamentally open). In addition, quantum adaptive dynamics provides the most generally possible mathematical representation of these principles.

Dynamic Information Encoding With Dynamic Synapses in Neural Adaptation

Science.gov (United States)

Li, Luozheng; Mi, Yuanyuan; Zhang, Wenhao; Wang, Da-Hui; Wu, Si

2018-01-01

Adaptation refers to the general phenomenon that the neural system dynamically adjusts its response property according to the statistics of external inputs. In response to an invariant stimulation, neuronal firing rates first increase dramatically and then decrease gradually to a low level close to the background activity. This prompts a question: during the adaptation, how does the neural system encode the repeated stimulation with attenuated firing rates? It has been suggested that the neural system may employ a dynamical encoding strategy during the adaptation, the information of stimulus is mainly encoded by the strong independent spiking of neurons at the early stage of the adaptation; while the weak but synchronized activity of neurons encodes the stimulus information at the later stage of the adaptation. The previous study demonstrated that short-term facilitation (STF) of electrical synapses, which increases the synchronization between neurons, can provide a mechanism to realize dynamical encoding. In the present study, we further explore whether short-term plasticity (STP) of chemical synapses, an interaction form more common than electrical synapse in the cortex, can support dynamical encoding. We build a large-size network with chemical synapses between neurons. Notably, facilitation of chemical synapses only enhances pair-wise correlations between neurons mildly, but its effect on increasing synchronization of the network can be significant, and hence it can serve as a mechanism to convey the stimulus information. To read-out the stimulus information, we consider that a downstream neuron receives balanced excitatory and inhibitory inputs from the network, so that the downstream neuron only responds to synchronized firings of the network. Therefore, the response of the downstream neuron indicates the presence of the repeated stimulation. Overall, our study demonstrates that STP of chemical synapse can serve as a mechanism to realize dynamical neural

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.

A dynamical system that describes vein graft adaptation and failure.

Science.gov (United States)

Garbey, Marc; Berceli, Scott A

2013-11-07

Adaptation of vein bypass grafts to the mechanical stresses imposed by the arterial circulation is thought to be the primary determinant for lesion development, yet an understanding of how the various forces dictate local wall remodeling is lacking. We develop a dynamical system that summarizes the complex interplay between the mechanical environment and cell/matrix kinetics, ultimately dictating changes in the vein graft architecture. Based on a systematic mapping of the parameter space, three general remodeling response patterns are observed: (1) shear stabilized intimal thickening, (2) tension induced wall thinning and lumen expansion, and (3) tension stabilized wall thickening. Notable is our observation that the integration of multiple feedback mechanisms leads to a variety of non-linear responses that would be unanticipated by an analysis of each system component independently. This dynamic analysis supports the clinical observation that the majority of vein grafts proceed along an adaptive trajectory, where grafts dilate and mildly thicken in response to the increased tension and shear, but a small portion of the grafts demonstrate a maladaptive phenotype, where progressive inward remodeling and accentuated wall thickening lead to graft failure. © 2013 The Authors. Published by Elsevier Ltd. All rights reserved.

Adaptive-network models of collective dynamics

Science.gov (United States)

Zschaler, G.

2012-09-01

Complex systems can often be modelled as networks, in which their basic units are represented by abstract nodes and the interactions among them by abstract links. This network of interactions is the key to understanding emergent collective phenomena in such systems. In most cases, it is an adaptive network, which is defined by a feedback loop between the local dynamics of the individual units and the dynamical changes of the network structure itself. This feedback loop gives rise to many novel phenomena. Adaptive networks are a promising concept for the investigation of collective phenomena in different systems. However, they also present a challenge to existing modelling approaches and analytical descriptions due to the tight coupling between local and topological degrees of freedom. In this work, which is essentially my PhD thesis, I present a simple rule-based framework for the investigation of adaptive networks, using which a wide range of collective phenomena can be modelled and analysed from a common perspective. In this framework, a microscopic model is defined by the local interaction rules of small network motifs, which can be implemented in stochastic simulations straightforwardly. Moreover, an approximate emergent-level description in terms of macroscopic variables can be derived from the microscopic rules, which we use to analyse the system's collective and long-term behaviour by applying tools from dynamical systems theory. We discuss three adaptive-network models for different collective phenomena within our common framework. First, we propose a novel approach to collective motion in insect swarms, in which we consider the insects' adaptive interaction network instead of explicitly tracking their positions and velocities. We capture the experimentally observed onset of collective motion qualitatively in terms of a bifurcation in this non-spatial model. We find that three-body interactions are an essential ingredient for collective motion to emerge

Neural network based adaptive control for nonlinear dynamic regimes

Science.gov (United States)

Shin, Yoonghyun

Adaptive control designs using neural networks (NNs) based on dynamic inversion are investigated for aerospace vehicles which are operated at highly nonlinear dynamic regimes. NNs play a key role as the principal element of adaptation to approximately cancel the effect of inversion error, which subsequently improves robustness to parametric uncertainty and unmodeled dynamics in nonlinear regimes. An adaptive control scheme previously named 'composite model reference adaptive control' is further developed so that it can be applied to multi-input multi-output output feedback dynamic inversion. It can have adaptive elements in both the dynamic compensator (linear controller) part and/or in the conventional adaptive controller part, also utilizing state estimation information for NN adaptation. This methodology has more flexibility and thus hopefully greater potential than conventional adaptive designs for adaptive flight control in highly nonlinear flight regimes. The stability of the control system is proved through Lyapunov theorems, and validated with simulations. The control designs in this thesis also include the use of 'pseudo-control hedging' techniques which are introduced to prevent the NNs from attempting to adapt to various actuation nonlinearities such as actuator position and rate saturations. Control allocation is introduced for the case of redundant control effectors including thrust vectoring nozzles. A thorough comparison study of conventional and NN-based adaptive designs for a system under a limit cycle, wing-rock, is included in this research, and the NN-based adaptive control designs demonstrate their performances for two highly maneuverable aerial vehicles, NASA F-15 ACTIVE and FQM-117B unmanned aerial vehicle (UAV), operated under various nonlinearities and uncertainties.

Adaptive resummation of Markovian quantum dynamics

International Nuclear Information System (INIS)

Lucas, Felix

2014-01-01

In this thesis we derive a highly convergent, nonperturbative expansion of Markovian open quantum dynamics. It is based on a splitting of the incoherent dynamics into periods of continuous evolution and abrupt jumps and attains its favorable convergence properties from an adaptive resummation of this so-called jump expansion. By means of the long-standing problems of spatial particle detection and Landau-Zener tunneling in the presence of dephasing, we show that this adaptive resummation technique facilitates new highly accurate analytic approximations of Markovian open systems. The open Landau-Zener model leads us to propose an efficient and robust incoherent control technique for the isomerization reaction of the visual pigment protein rhodopsin. Besides leading to approximate analytic descriptions of Markovian open quantum dynamics, the adaptive resummation of the jump expansion implies an efficient numerical simulation method. We spell out the corresponding numerical algorithm by means of Monte Carlo integration of the relevant terms in the jump expansion and demonstrate it in a set of paradigmatic open quantum systems.

Adaptivity in Professional Printing Systems

NARCIS (Netherlands)

Verriet, J.H.; Basten, T; Hamberg, R.; Reckers, F.J.; Somers, L.

2013-01-01

There is a constant pressure on developers of embedded systems to simultaneously increase system functionality and to decrease development costs. Aviable way to obtain a better system performance with the same physical hardware is adaptivity: a system should be able to adapt itself to dynamically

Spontaneous formation of dynamical groups in an adaptive networked system

International Nuclear Information System (INIS)

Li Menghui; Guan Shuguang; Lai, C-H

2010-01-01

In this work, we investigate a model of an adaptive networked dynamical system, where the coupling strengths among phase oscillators coevolve with the phase states. It is shown that in this model the oscillators can spontaneously differentiate into two dynamical groups after a long time evolution. Within each group, the oscillators have similar phases, while oscillators in different groups have approximately opposite phases. The network gradually converts from the initial random structure with a uniform distribution of connection strengths into a modular structure that is characterized by strong intra-connections and weak inter-connections. Furthermore, the connection strengths follow a power-law distribution, which is a natural consequence of the coevolution of the network and the dynamics. Interestingly, it is found that if the inter-connections are weaker than a certain threshold, the two dynamical groups will almost decouple and evolve independently. These results are helpful in further understanding the empirical observations in many social and biological networks.

Understanding system dynamics of an adaptive enzyme network from globally profiled kinetic parameters.

Science.gov (United States)

Chiang, Austin W T; Liu, Wei-Chung; Charusanti, Pep; Hwang, Ming-Jing

2014-01-15

A major challenge in mathematical modeling of biological systems is to determine how model parameters contribute to systems dynamics. As biological processes are often complex in nature, it is desirable to address this issue using a systematic approach. Here, we propose a simple methodology that first performs an enrichment test to find patterns in the values of globally profiled kinetic parameters with which a model can produce the required system dynamics; this is then followed by a statistical test to elucidate the association between individual parameters and different parts of the system's dynamics. We demonstrate our methodology on a prototype biological system of perfect adaptation dynamics, namely the chemotaxis model for Escherichia coli. Our results agreed well with those derived from experimental data and theoretical studies in the literature. Using this model system, we showed that there are motifs in kinetic parameters and that these motifs are governed by constraints of the specified system dynamics. A systematic approach based on enrichment statistical tests has been developed to elucidate the relationships between model parameters and the roles they play in affecting system dynamics of a prototype biological network. The proposed approach is generally applicable and therefore can find wide use in systems biology modeling research.

Adaptive learning and complex dynamics

International Nuclear Information System (INIS)

Gomes, Orlando

2009-01-01

In this paper, we explore the dynamic properties of a group of simple deterministic difference equation systems in which the conventional perfect foresight assumption gives place to a mechanism of adaptive learning. These systems have a common feature: under perfect foresight (or rational expectations) they all possess a unique fixed point steady state. This long-term outcome is obtained also under learning if the quality underlying the learning process is high. Otherwise, when the degree of inefficiency of the learning process is relatively strong, nonlinear dynamics (periodic and a-periodic cycles) arise. The specific properties of each one of the proposed systems is explored both in terms of local and global dynamics. One macroeconomic model is used to illustrate how the formation of expectations through learning may eventually lead to awkward long-term outcomes.

Adaptive Dynamic Surface Control for Generator Excitation Control System

Directory of Open Access Journals (Sweden)

Zhang Xiu-yu

2014-01-01

Full Text Available For the generator excitation control system which is equipped with static var compensator (SVC and unknown parameters, a novel adaptive dynamic surface control scheme is proposed based on neural network and tracking error transformed function with the following features: (1 the transformation of the excitation generator model to the linear systems is omitted; (2 the prespecified performance of the tracking error can be guaranteed by combining with the tracking error transformed function; (3 the computational burden is greatly reduced by estimating the norm of the weighted vector of neural network instead of the weighted vector itself; therefore, it is more suitable for the real time control; and (4 the explosion of complicity problem inherent in the backstepping control can be eliminated. It is proved that the new scheme can make the system semiglobally uniformly ultimately bounded. Simulation results show the effectiveness of this control scheme.

Calculation Method for Equilibrium Points in Dynamical Systems Based on Adaptive Sinchronization

Directory of Open Access Journals (Sweden)

Manuel Prian Rodríguez

2017-12-01

Full Text Available In this work, a control system is proposed as an equivalent numerical procedure whose aim is to obtain the natural equilibrium points of a dynamical system. These equilibrium points may be employed later as setpoint signal for different control techniques. The proposed procedure is based on the adaptive synchronization between an oscillator and a reference model driven by the oscillator state variables. A stability analysis is carried out and a simplified algorithm is proposed. Finally, satisfactory simulation results are shown.

Online Adaptive Optimal Control of Vehicle Active Suspension Systems Using Single-Network Approximate Dynamic Programming

Directory of Open Access Journals (Sweden)

Zhi-Jun Fu

2017-01-01

Full Text Available In view of the performance requirements (e.g., ride comfort, road holding, and suspension space limitation for vehicle suspension systems, this paper proposes an adaptive optimal control method for quarter-car active suspension system by using the approximate dynamic programming approach (ADP. Online optimal control law is obtained by using a single adaptive critic NN to approximate the solution of the Hamilton-Jacobi-Bellman (HJB equation. Stability of the closed-loop system is proved by Lyapunov theory. Compared with the classic linear quadratic regulator (LQR approach, the proposed ADP-based adaptive optimal control method demonstrates improved performance in the presence of parametric uncertainties (e.g., sprung mass and unknown road displacement. Numerical simulation results of a sedan suspension system are presented to verify the effectiveness of the proposed control strategy.

Universal dynamics of complex adaptive systems: Gauge theory of things alive

International Nuclear Information System (INIS)

Mack, G.

1994-04-01

A universal dynamics of objects and their relations - a kind of ''universal chemistry'' - is discussed which satisfies general principles of locality and relativity. Einsteins theory of gravitation and the gauge theory of elementary particles are prototypes, but complex adaptive systems - anything that is alive in the widest sense - fall under the same paradigma. Frustration and gauge symmetry arise naturally in this context. Besides a nondissipative deterministic dynamics, which is thought to operate at a fundamental levle, a Thermo-Dynamics in sense of Prigogine is introduced by adding a diffusion process. It introduces irreversibility and entropy production. It equilibrates the chaotic local model of the time development (only) and is designed to be undetectable under continued observation with given finite measuring accuracy. Compositeness and the development of structure can be described in this framework. The existence of a critical equilibrium state may be postulated which is invariant under the dynamics. But it is usually not reached in a finite time from a given starting configuration, because local dynamics suffers from critical slowing down, especially in the presence of frustration. (orig.)

Speed tracking control of pneumatic motor servo systems using observation-based adaptive dynamic sliding-mode control

Science.gov (United States)

Chen, Syuan-Yi; Gong, Sheng-Sian

2017-09-01

This study aims to develop an adaptive high-precision control system for controlling the speed of a vane-type air motor (VAM) pneumatic servo system. In practice, the rotor speed of a VAM depends on the input mass air flow, which can be controlled by the effective orifice area (EOA) of an electronic throttle valve (ETV). As the control variable of a second-order pneumatic system is the integral of the EOA, an observation-based adaptive dynamic sliding-mode control (ADSMC) system is proposed to derive the differential of the control variable, namely, the EOA control signal. In the ADSMC system, a proportional-integral-derivative fuzzy neural network (PIDFNN) observer is used to achieve an ideal dynamic sliding-mode control (DSMC), and a supervisor compensator is designed to eliminate the approximation error. As a result, the ADSMC incorporates the robustness of a DSMC and the online learning ability of a PIDFNN. To ensure the convergence of the tracking error, a Lyapunov-based analytical method is employed to obtain the adaptive algorithms required to tune the control parameters of the online ADSMC system. Finally, our experimental results demonstrate the precision and robustness of the ADSMC system for highly nonlinear and time-varying VAM pneumatic servo systems.

Design of an Adaptive Human-Machine System Based on Dynamical Pattern Recognition of Cognitive Task-Load.

Science.gov (United States)

Zhang, Jianhua; Yin, Zhong; Wang, Rubin

2017-01-01

This paper developed a cognitive task-load (CTL) classification algorithm and allocation strategy to sustain the optimal operator CTL levels over time in safety-critical human-machine integrated systems. An adaptive human-machine system is designed based on a non-linear dynamic CTL classifier, which maps a set of electroencephalogram (EEG) and electrocardiogram (ECG) related features to a few CTL classes. The least-squares support vector machine (LSSVM) is used as dynamic pattern classifier. A series of electrophysiological and performance data acquisition experiments were performed on seven volunteer participants under a simulated process control task environment. The participant-specific dynamic LSSVM model is constructed to classify the instantaneous CTL into five classes at each time instant. The initial feature set, comprising 56 EEG and ECG related features, is reduced to a set of 12 salient features (including 11 EEG-related features) by using the locality preserving projection (LPP) technique. An overall correct classification rate of about 80% is achieved for the 5-class CTL classification problem. Then the predicted CTL is used to adaptively allocate the number of process control tasks between operator and computer-based controller. Simulation results showed that the overall performance of the human-machine system can be improved by using the adaptive automation strategy proposed.

Adaptation in Living Systems

Science.gov (United States)

Tu, Yuhai; Rappel, Wouter-Jan

2018-03-01

Adaptation refers to the biological phenomenon where living systems change their internal states in response to changes in their environments in order to maintain certain key functions critical for their survival and fitness. Adaptation is one of the most ubiquitous and arguably one of the most fundamental properties of living systems. It occurs throughout all biological scales, from adaptation of populations of species over evolutionary time to adaptation of a single cell to different environmental stresses during its life span. In this article, we review some of the recent progress made in understanding molecular mechanisms of cellular-level adaptation. We take the minimalist (or the physicist) approach and study the simplest systems that exhibit generic adaptive behaviors, namely chemotaxis in bacterium cells (Escherichia coli) and eukaryotic cells (Dictyostelium). We focus on understanding the basic biochemical interaction networks that are responsible for adaptation dynamics. By combining theoretical modeling with quantitative experimentation, we demonstrate universal features in adaptation as well as important differences in different cellular systems. Future work in extending the modeling framework to study adaptation in more complex systems such as sensory neurons is also discussed.

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.

STEADY ESTIMATION ALGORITHMS OF THE DYNAMIC SYSTEMS CONDITION ON THE BASIS OF CONCEPTS OF THE ADAPTIVE FILTRATION AND CONTROL

Directory of Open Access Journals (Sweden)

H.Z. Igamberdiyev

2014-07-01

Full Text Available Dynamic systems condition estimation regularization algorithms in the conditions of signals and hindrances statistical characteristics aprioristic uncertainty are offered. Regular iterative algorithms of strengthening matrix factor elements of the Kalman filter, allowing to adapt the filter to changing hindrance-alarm conditions are developed. Steady adaptive estimation algorithms of a condition vector in the aprioristic uncertainty conditions of covariance matrixes of object noise and the measurements hindrances providing a certain roughness of filtration process in relation to changing statistical characteristics of signals information parameters are offered. Offered practical realization results of the dynamic systems condition estimation algorithms are given at the adaptive management systems synthesis problems solution by technological processes of granulation drying of an ammophos pulp and receiving ammonia.

L1 Adaptive Control Augmentation System with Application to the X-29 Lateral/Directional Dynamics: A Multi-Input Multi-Output Approach

Science.gov (United States)

Griffin, Brian Joseph; Burken, John J.; Xargay, Enric

2010-01-01

This paper presents an L(sub 1) adaptive control augmentation system design for multi-input multi-output nonlinear systems in the presence of unmatched uncertainties which may exhibit significant cross-coupling effects. A piecewise continuous adaptive law is adopted and extended for applicability to multi-input multi-output systems that explicitly compensates for dynamic cross-coupling. In addition, explicit use of high-fidelity actuator models are added to the L1 architecture to reduce uncertainties in the system. The L(sub 1) multi-input multi-output adaptive control architecture is applied to the X-29 lateral/directional dynamics and results are evaluated against a similar single-input single-output design approach.

Dynamical adaptation in photoreceptors.

Directory of Open Access Journals (Sweden)

Damon A Clark

Full Text Available Adaptation is at the heart of sensation and nowhere is it more salient than in early visual processing. Light adaptation in photoreceptors is doubly dynamical: it depends upon the temporal structure of the input and it affects the temporal structure of the response. We introduce a non-linear dynamical adaptation model of photoreceptors. It is simple enough that it can be solved exactly and simulated with ease; analytical and numerical approaches combined provide both intuition on the behavior of dynamical adaptation and quantitative results to be compared with data. Yet the model is rich enough to capture intricate phenomenology. First, we show that it reproduces the known phenomenology of light response and short-term adaptation. Second, we present new recordings and demonstrate that the model reproduces cone response with great precision. Third, we derive a number of predictions on the response of photoreceptors to sophisticated stimuli such as periodic inputs, various forms of flickering inputs, and natural inputs. In particular, we demonstrate that photoreceptors undergo rapid adaptation of response gain and time scale, over ∼ 300[Formula: see text] ms-i. e., over the time scale of the response itself-and we confirm this prediction with data. For natural inputs, this fast adaptation can modulate the response gain more than tenfold and is hence physiologically relevant.

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.

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.

Adaptive control of solar energy collector systems

CERN Document Server

Lemos, João M; Igreja, José M

2014-01-01

This book describes methods for adaptive control of distributed-collector solar fields: plants that collect solar energy and deliver it in thermal form. Controller design methods are presented that can overcome difficulties found in these type of plants:they are distributed-parameter systems, i.e., systems with dynamics that depend on space as well as time;their dynamics is nonlinear, with a bilinear structure;there is a significant level of uncertainty in plant knowledge.Adaptive methods form the focus of the text because of the degree of uncertainty in the knowledge of plant dynamics. Parts

Adaptive Neural Output-Feedback Control for a Class of Nonlower Triangular Nonlinear Systems With Unmodeled Dynamics.

Science.gov (United States)

Wang, Huanqing; Liu, Peter Xiaoping; Li, Shuai; Wang, Ding

2017-08-29

This paper presents the development of an adaptive neural controller for a class of nonlinear systems with unmodeled dynamics and immeasurable states. An observer is designed to estimate system states. The structure consistency of virtual control signals and the variable partition technique are combined to overcome the difficulties appearing in a nonlower triangular form. An adaptive neural output-feedback controller is developed based on the backstepping technique and the universal approximation property of the radial basis function (RBF) neural networks. By using the Lyapunov stability analysis, the semiglobally and uniformly ultimate boundedness of all signals within the closed-loop system is guaranteed. The simulation results show that the controlled system converges quickly, and all the signals are bounded. This paper is novel at least in the two aspects: 1) an output-feedback control strategy is developed for a class of nonlower triangular nonlinear systems with unmodeled dynamics and 2) the nonlinear disturbances and their bounds are the functions of all states, which is in a more general form than existing results.

Distributed neural network control for adaptive synchronization of uncertain dynamical multiagent systems.

Science.gov (United States)

Peng, Zhouhua; Wang, Dan; Zhang, Hongwei; Sun, Gang

2014-08-01

This paper addresses the leader-follower synchronization problem of uncertain dynamical multiagent systems with nonlinear dynamics. Distributed adaptive synchronization controllers are proposed based on the state information of neighboring agents. The control design is developed for both undirected and directed communication topologies without requiring the accurate model of each agent. This result is further extended to the output feedback case where a neighborhood observer is proposed based on relative output information of neighboring agents. Then, distributed observer-based synchronization controllers are derived and a parameter-dependent Riccati inequality is employed to prove the stability. This design has a favorable decouple property between the observer and the controller designs for nonlinear multiagent systems. For both cases, the developed controllers guarantee that the state of each agent synchronizes to that of the leader with bounded residual errors. Two illustrative examples validate the efficacy of the proposed methods.

Multipoint dynamically reconfigure adaptive distributed fiber optic acoustic emission sensor (FAESense) system for condition based maintenance

Science.gov (United States)

Mendoza, Edgar; Prohaska, John; Kempen, Connie; Esterkin, Yan; Sun, Sunjian; Krishnaswamy, Sridhar

2010-09-01

This paper describes preliminary results obtained under a Navy SBIR contract by Redondo Optics Inc. (ROI), in collaboration with Northwestern University towards the development and demonstration of a next generation, stand-alone and fully integrated, dynamically reconfigurable, adaptive fiber optic acoustic emission sensor (FAESense™) system for the in-situ unattended detection and localization of shock events, impact damage, cracks, voids, and delaminations in new and aging critical infrastructures found in ships, submarines, aircraft, and in next generation weapon systems. ROI's FAESense™ system is based on the integration of proven state-of-the-art technologies: 1) distributed array of in-line fiber Bragg gratings (FBGs) sensors sensitive to strain, vibration, and acoustic emissions, 2) adaptive spectral demodulation of FBG sensor dynamic signals using two-wave mixing interferometry on photorefractive semiconductors, and 3) integration of all the sensor system passive and active optoelectronic components within a 0.5-cm x 1-cm photonic integrated circuit microchip. The adaptive TWM demodulation methodology allows the measurement of dynamic high frequnency acoustic emission events, while compensating for passive quasi-static strain and temperature drifts. It features a compact, low power, environmentally robust 1-inch x 1-inch x 4-inch small form factor (SFF) package with no moving parts. The FAESense™ interrogation system is microprocessor-controlled using high data rate signal processing electronics for the FBG sensors calibration, temperature compensation and the detection and analysis of acoustic emission signals. Its miniaturized package, low power operation, state-of-the-art data communications, and low cost makes it a very attractive solution for a large number of applications in naval and maritime industries, aerospace, civil structures, the oil and chemical industry, and for homeland security applications.

DySOA : Making service systems self-adaptive

NARCIS (Netherlands)

Siljee, J; Bosloper, [No Value; Nijhuis, J; Hammer, D; Benatallah, B; Casati, F; Traverso, P

2005-01-01

Service-centric systems exist in a very dynamic environment. This requires these systems to adapt at runtime in order to keep fulfilling their QoS. In order to create self-adaptive service systems, developers should not only design the service architecture, but also need to design the

Macroscopic description of complex adaptive networks coevolving with dynamic node states

Science.gov (United States)

Wiedermann, Marc; Donges, Jonathan F.; Heitzig, Jobst; Lucht, Wolfgang; Kurths, Jürgen

2015-05-01

In many real-world complex systems, the time evolution of the network's structure and the dynamic state of its nodes are closely entangled. Here we study opinion formation and imitation on an adaptive complex network which is dependent on the individual dynamic state of each node and vice versa to model the coevolution of renewable resources with the dynamics of harvesting agents on a social network. The adaptive voter model is coupled to a set of identical logistic growth models and we mainly find that, in such systems, the rate of interactions between nodes as well as the adaptive rewiring probability are crucial parameters for controlling the sustainability of the system's equilibrium state. We derive a macroscopic description of the system in terms of ordinary differential equations which provides a general framework to model and quantify the influence of single node dynamics on the macroscopic state of the network. The thus obtained framework is applicable to many fields of study, such as epidemic spreading, opinion formation, or socioecological modeling.

Model-Based Design of Adaptive Embedded Systems

NARCIS (Netherlands)

Basten, T.; Hamberg, R.; Reckers, F.; Verriet, J.

2013-01-01

Today’s embedded systems have to operate in a wide variety of dynamically changing environmental circumstances. Adaptivity, the ability of a system to autonomously adapt itself, is a means to optimise a system’s behaviour to accommodate changes in its environment. It involves making in-product

Adaptive neural network output feedback control for stochastic nonlinear systems with unknown dead-zone and unmodeled dynamics.

Science.gov (United States)

Tong, Shaocheng; Wang, Tong; Li, Yongming; Zhang, Huaguang

2014-06-01

This paper discusses the problem of adaptive neural network output feedback control for a class of stochastic nonlinear strict-feedback systems. The concerned systems have certain characteristics, such as unknown nonlinear uncertainties, unknown dead-zones, unmodeled dynamics and without the direct measurements of state variables. In this paper, the neural networks (NNs) are employed to approximate the unknown nonlinear uncertainties, and then by representing the dead-zone as a time-varying system with a bounded disturbance. An NN state observer is designed to estimate the unmeasured states. Based on both backstepping design technique and a stochastic small-gain theorem, a robust adaptive NN output feedback control scheme is developed. It is proved that all the variables involved in the closed-loop system are input-state-practically stable in probability, and also have robustness to the unmodeled dynamics. Meanwhile, the observer errors and the output of the system can be regulated to a small neighborhood of the origin by selecting appropriate design parameters. Simulation examples are also provided to illustrate the effectiveness of the proposed approach.

Adaptive dynamic programming with applications in optimal control

CERN Document Server

Liu, Derong; Wang, Ding; Yang, Xiong; Li, Hongliang

2017-01-01

This book covers the most recent developments in adaptive dynamic programming (ADP). The text begins with a thorough background review of ADP making sure that readers are sufficiently familiar with the fundamentals. In the core of the book, the authors address first discrete- and then continuous-time systems. Coverage of discrete-time systems starts with a more general form of value iteration to demonstrate its convergence, optimality, and stability with complete and thorough theoretical analysis. A more realistic form of value iteration is studied where value function approximations are assumed to have finite errors. Adaptive Dynamic Programming also details another avenue of the ADP approach: policy iteration. Both basic and generalized forms of policy-iteration-based ADP are studied with complete and thorough theoretical analysis in terms of convergence, optimality, stability, and error bounds. Among continuous-time systems, the control of affine and nonaffine nonlinear systems is studied using the ADP app...

Adaptive Dynamic Programming for Control Algorithms and Stability

CERN Document Server

Zhang, Huaguang; Luo, Yanhong; Wang, Ding

2013-01-01

There are many methods of stable controller design for nonlinear systems. In seeking to go beyond the minimum requirement of stability, Adaptive Dynamic Programming for Control approaches the challenging topic of optimal control for nonlinear systems using the tools of  adaptive dynamic programming (ADP). The range of systems treated is extensive; affine, switched, singularly perturbed and time-delay nonlinear systems are discussed as are the uses of neural networks and techniques of value and policy iteration. The text features three main aspects of ADP in which the methods proposed for stabilization and for tracking and games benefit from the incorporation of optimal control methods: • infinite-horizon control for which the difficulty of solving partial differential Hamilton–Jacobi–Bellman equations directly is overcome, and  proof provided that the iterative value function updating sequence converges to the infimum of all the value functions obtained by admissible control law sequences; • finite-...

Rotor-bearing system integrated with shape memory alloy springs for ensuring adaptable dynamics and damping enhancement-Theory and experiment

DEFF Research Database (Denmark)

Enemark, Søren; Santos, Ilmar F.

2016-01-01

nonlinear coupled dynamics of the rotor-bearing system. The nonlinear forces from the thermomechanical shape memory alloy springs and from the passive magnetic bearings are coupled to the rotor and bearing housing dynamics. The equations of motion describing rotor tilt and bearing housing lateral motion......Helical pseudoelastic shape memory alloy (SMA) springs are integrated into a dynamic system consisting of a rigid rotor supported by passive magnetic bearings. The aim is to determine the utility of SMAs for vibration attenuation via their mechanical hysteresis, and for adaptation of the dynamic...

Model-based design of adaptive embedded systems

CERN Document Server

Hamberg, Roelof; Reckers, Frans; Verriet, Jacques

2013-01-01

Today’s embedded systems have to operate in a wide variety of dynamically changing environmental circumstances. Adaptivity, the ability of a system to autonomously adapt itself, is a means to optimise a system’s behaviour to accommodate changes in its environment. It involves making in-product trade-offs between system qualities at system level. The main challenge in the development of adaptive systems is keeping control of the intrinsic complexity of such systems while working with multi-disciplinary teams to create different parts of the system. Model-Based Development of Adaptive Embedded Systems focuses on the development of adaptive embedded systems both from an architectural and methodological point of view. It describes architectural solution patterns for adaptive systems and state-of-the-art model-based methods and techniques to support adaptive system development. In particular, the book describes the outcome of the Octopus project, a cooperation of a multi-disciplinary team of academic and indus...

Neural-network-observer-based optimal control for unknown nonlinear systems using adaptive dynamic programming

Science.gov (United States)

Liu, Derong; Huang, Yuzhu; Wang, Ding; Wei, Qinglai

2013-09-01

In this paper, an observer-based optimal control scheme is developed for unknown nonlinear systems using adaptive dynamic programming (ADP) algorithm. First, a neural-network (NN) observer is designed to estimate system states. Then, based on the observed states, a neuro-controller is constructed via ADP method to obtain the optimal control. In this design, two NN structures are used: a three-layer NN is used to construct the observer which can be applied to systems with higher degrees of nonlinearity and without a priori knowledge of system dynamics, and a critic NN is employed to approximate the value function. The optimal control law is computed using the critic NN and the observer NN. Uniform ultimate boundedness of the closed-loop system is guaranteed. The actor, critic, and observer structures are all implemented in real-time, continuously and simultaneously. Finally, simulation results are presented to demonstrate the effectiveness of the proposed control scheme.

Modulation of neuronal dynamic range using two different adaptation mechanisms

Directory of Open Access Journals (Sweden)

Lei Wang

2017-01-01

Full Text Available The capability of neurons to discriminate between intensity of external stimulus is measured by its dynamic range. A larger dynamic range indicates a greater probability of neuronal survival. In this study, the potential roles of adaptation mechanisms (ion currents in modulating neuronal dynamic range were numerically investigated. Based on the adaptive exponential integrate-and-fire model, which includes two different adaptation mechanisms, i.e. subthreshold and suprathreshold (spike-triggered adaptation, our results reveal that the two adaptation mechanisms exhibit rather different roles in regulating neuronal dynamic range. Specifically, subthreshold adaptation acts as a negative factor that observably decreases the neuronal dynamic range, while suprathreshold adaptation has little influence on the neuronal dynamic range. Moreover, when stochastic noise was introduced into the adaptation mechanisms, the dynamic range was apparently enhanced, regardless of what state the neuron was in, e.g. adaptive or non-adaptive. Our model results suggested that the neuronal dynamic range can be differentially modulated by different adaptation mechanisms. Additionally, noise was a non-ignorable factor, which could effectively modulate the neuronal dynamic range.

Farming System Evolution and Adaptive Capacity: Insights for Adaptation Support

Directory of Open Access Journals (Sweden)

Jami L. Dixon

2014-02-01

Full Text Available Studies of climate impacts on agriculture and adaptation often provide current or future assessments, ignoring the historical contexts farming systems are situated within. We investigate how historical trends have influenced farming system adaptive capacity in Uganda using data from household surveys, semi-structured interviews, focus-group discussions and observations. By comparing two farming systems, we note three major findings: (1 similar trends in farming system evolution have had differential impacts on the diversity of farming systems; (2 trends have contributed to the erosion of informal social and cultural institutions and an increasing dependence on formal institutions; and (3 trade-offs between components of adaptive capacity are made at the farm-scale, thus influencing farming system adaptive capacity. To identify the actual impacts of future climate change and variability, it is important to recognize the dynamic nature of adaptation. In practice, areas identified for further adaptation support include: shift away from one-size-fits-all approach the identification and integration of appropriate modern farming method; a greater focus on building inclusive formal and informal institutions; and a more nuanced understanding regarding the roles and decision-making processes of influential, but external, actors. More research is needed to understand farm-scale trade-offs and the resulting impacts across spatial and temporal scales.

Adaptive Dynamic Process Scheduling on Distributed Memory Parallel Computers

Directory of Open Access Journals (Sweden)

Wei Shu

1994-01-01

Full Text Available One of the challenges in programming distributed memory parallel machines is deciding how to allocate work to processors. This problem is particularly important for computations with unpredictable dynamic behaviors or irregular structures. We present a scheme for dynamic scheduling of medium-grained processes that is useful in this context. The adaptive contracting within neighborhood (ACWN is a dynamic, distributed, load-dependent, and scalable scheme. It deals with dynamic and unpredictable creation of processes and adapts to different systems. The scheme is described and contrasted with two other schemes that have been proposed in this context, namely the randomized allocation and the gradient model. The performance of the three schemes on an Intel iPSC/2 hypercube is presented and analyzed. The experimental results show that even though the ACWN algorithm incurs somewhat larger overhead than the randomized allocation, it achieves better performance in most cases due to its adaptiveness. Its feature of quickly spreading the work helps it outperform the gradient model in performance and scalability.

A Dynamically Adaptable Impedance-Matching System for Midrange Wireless Power Transfer with Misalignment

Directory of Open Access Journals (Sweden)

Thuc Phi Duong

2015-07-01

Full Text Available To enable the geometrical freedom envisioned for wireless power transfer (WPT, fast dynamic adaptation to unpredictable changes in receiver position is needed. In this paper, we propose an adaptive impedance-searching system that achieves good impedance matching quickly. For fast and robust operation, the proposed method consists of three steps: system calibration, coarse search, and fine search. The proposed WPT system is characterized using distance variation and lateral and angular misalignment between coils. The measured results indicate that the proposed method significantly reduces searching time from a few minutes to approximately one second. Furthermore, the proposed system achieves impedance matching with good accuracy. The robust impedance-searching capability of the proposed system significantly improves power transfer efficiency. At 6.78 MHz, we achieve a maximum efficiency of 89.7% and a high efficiency of >80% up to a distance of 50 cm. When the center-to-center misalignment is 35 cm, the efficiency is improved from 48.4% to 74.1% with the proposed method. At a distance of 40 cm, the efficiency is higher than 74% for up to 60° of angular rotation. These results agree well with the simulated results obtained using a lumped-element circuit model.

Complexity and network dynamics in physiological adaptation: An integrated view

OpenAIRE

Baffy, Gyorgy; Loscalzo, Joseph

2014-01-01

Living organisms constantly interact with their surroundings and sustain internal stability against perturbations. This dynamic process follows three fundamental strategies (restore, explore, and abandon) articulated in historical concepts of physiological adaptation such as homeostasis, allostasis, and the general adaptation syndrome. These strategies correspond to elementary forms of behavior (ordered, chaotic, and static) in complex adaptive systems and invite a network-based analysis of t...

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.

A double-loop adaptive sampling approach for sensitivity-free dynamic reliability analysis

International Nuclear Information System (INIS)

Wang, Zequn; Wang, Pingfeng

2015-01-01

Dynamic reliability measures reliability of an engineered system considering time-variant operation condition and component deterioration. Due to high computational costs, conducting dynamic reliability analysis at an early system design stage remains challenging. This paper presents a confidence-based meta-modeling approach, referred to as double-loop adaptive sampling (DLAS), for efficient sensitivity-free dynamic reliability analysis. The DLAS builds a Gaussian process (GP) model sequentially to approximate extreme system responses over time, so that Monte Carlo simulation (MCS) can be employed directly to estimate dynamic reliability. A generic confidence measure is developed to evaluate the accuracy of dynamic reliability estimation while using the MCS approach based on developed GP models. A double-loop adaptive sampling scheme is developed to efficiently update the GP model in a sequential manner, by considering system input variables and time concurrently in two sampling loops. The model updating process using the developed sampling scheme can be terminated once the user defined confidence target is satisfied. The developed DLAS approach eliminates computationally expensive sensitivity analysis process, thus substantially improves the efficiency of dynamic reliability analysis. Three case studies are used to demonstrate the efficacy of DLAS for dynamic reliability analysis. - Highlights: • Developed a novel adaptive sampling approach for dynamic reliability analysis. • POD Developed a new metric to quantify the accuracy of dynamic reliability estimation. • Developed a new sequential sampling scheme to efficiently update surrogate models. • Three case studies were used to demonstrate the efficacy of the new approach. • Case study results showed substantially enhanced efficiency with high accuracy

Performance enhanced design of chaos controller for the mechanical centrifugal flywheel governor system via adaptive dynamic surface control

Directory of Open Access Journals (Sweden)

Shaohua Luo

2016-09-01

Full Text Available This paper addresses chaos suppression of the mechanical centrifugal flywheel governor system with output constraint and fully unknown parameters via adaptive dynamic surface control. To have a certain understanding of chaotic nature of the mechanical centrifugal flywheel governor system and subsequently design its controller, the useful tools like the phase diagrams and corresponding time histories are employed. By using tangent barrier Lyapunov function, a dynamic surface control scheme with neural network and tracking differentiator is developed to transform chaos oscillation into regular motion and the output constraint rule is not broken in whole process. Plugging second-order tracking differentiator into chaos controller tackles the “explosion of complexity” of backstepping and improves the accuracy in contrast with the first-order filter. Meanwhile, Chebyshev neural network with adaptive law whose input only depends on a subset of Chebyshev polynomials is derived to learn the behavior of unknown dynamics. The boundedness of all signals of the closed-loop system is verified in stability analysis. Finally, the results of numerical simulations illustrate effectiveness and exhibit the superior performance of the proposed scheme by comparing with the existing ADSC method.

Observer-based distributed adaptive fault-tolerant containment control of multi-agent systems with general linear dynamics.

Science.gov (United States)

Ye, Dan; Chen, Mengmeng; Li, Kui

2017-11-01

In this paper, we consider the distributed containment control problem of multi-agent systems with actuator bias faults based on observer method. The objective is to drive the followers into the convex hull spanned by the dynamic leaders, where the input is unknown but bounded. By constructing an observer to estimate the states and bias faults, an effective distributed adaptive fault-tolerant controller is developed. Different from the traditional method, an auxiliary controller gain is designed to deal with the unknown inputs and bias faults together. Moreover, the coupling gain can be adjusted online through the adaptive mechanism without using the global information. Furthermore, the proposed control protocol can guarantee that all the signals of the closed-loop systems are bounded and all the followers converge to the convex hull with bounded residual errors formed by the dynamic leaders. Finally, a decoupled linearized longitudinal motion model of the F-18 aircraft is used to demonstrate the effectiveness. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Robust Adaptive Dynamic Programming of Two-Player Zero-Sum Games for Continuous-Time Linear Systems.

Science.gov (United States)

Fu, Yue; Fu, Jun; Chai, Tianyou

2015-12-01

In this brief, an online robust adaptive dynamic programming algorithm is proposed for two-player zero-sum games of continuous-time unknown linear systems with matched uncertainties, which are functions of system outputs and states of a completely unknown exosystem. The online algorithm is developed using the policy iteration (PI) scheme with only one iteration loop. A new analytical method is proposed for convergence proof of the PI scheme. The sufficient conditions are given to guarantee globally asymptotic stability and suboptimal property of the closed-loop system. Simulation studies are conducted to illustrate the effectiveness of the proposed method.

Adaptive mechanism-based congestion control for networked systems

Science.gov (United States)

Liu, Zhi; Zhang, Yun; Chen, C. L. Philip

2013-03-01

In order to assure the communication quality in network systems with heavy traffic and limited bandwidth, a new ATRED (adaptive thresholds random early detection) congestion control algorithm is proposed for the congestion avoidance and resource management of network systems. Different to the traditional AQM (active queue management) algorithms, the control parameters of ATRED are not configured statically, but dynamically adjusted by the adaptive mechanism. By integrating with the adaptive strategy, ATRED alleviates the tuning difficulty of RED (random early detection) and shows a better control on the queue management, and achieve a more robust performance than RED under varying network conditions. Furthermore, a dynamic transmission control protocol-AQM control system using ATRED controller is introduced for the systematic analysis. It is proved that the stability of the network system can be guaranteed when the adaptive mechanism is finely designed. Simulation studies show the proposed ATRED algorithm achieves a good performance in varying network environments, which is superior to the RED and Gentle-RED algorithm, and providing more reliable service under varying network conditions.

Adaptive Hypermedia Educational System Based on XML Technologies.

Science.gov (United States)

Baek, Yeongtae; Wang, Changjong; Lee, Sehoon

This paper proposes an adaptive hypermedia educational system using XML technologies, such as XML, XSL, XSLT, and XLink. Adaptive systems are capable of altering the presentation of the content of the hypermedia on the basis of a dynamic understanding of the individual user. The user profile can be collected in a user model, while the knowledge…

Nanostructural self-organization and dynamic adaptation of metal-polymer tribosystems

Science.gov (United States)

Mashkov, Yu. K.

2017-02-01

The results of investigating the effect of nanosize modifiers of a polymer matrix on the nanostructural self-organization of polymer composites and dynamic adaptation of metal-polymer tribosystems, which considerably affect the wear resistance of polymer composite materials, have been analyzed. It has been shown that the physicochemical nanostructural self-organization processes are developed in metal-polymer tribosystems with the formation of thermotropic liquid-crystal structures of the polymer matrix, followed by the transition of the system to the stationary state with a negative feedback that ensures dynamic adaptation of the tribosystem to given operating conditions.

Adaptive Disturbance Rejection Control for Automatic Carrier Landing System

Directory of Open Access Journals (Sweden)

Xin Wang

2016-01-01

Full Text Available An adaptive disturbance rejection algorithm is proposed for carrier landing system in the final-approach. The carrier-based aircraft dynamics and the linearized longitudinal model under turbulence conditions in the final-approach are analyzed. A stable adaptive control scheme is developed based on LDU decomposition of the high-frequency gain matrix, which ensures closed-loop stability and asymptotic output tracking. Finally, simulation studies of a linearized longitudinal-directional dynamics model are conducted to demonstrate the performance of the adaptive scheme.

The self-adaptation to dynamic failures for efficient virtual organization formations in grid computing context

International Nuclear Information System (INIS)

Han Liangxiu

2009-01-01

Grid computing aims to enable 'resource sharing and coordinated problem solving in dynamic, multi-institutional virtual organizations (VOs)'. However, due to the nature of heterogeneous and dynamic resources, dynamic failures in the distributed grid environment usually occur more than in traditional computation platforms, which cause failed VO formations. In this paper, we develop a novel self-adaptive mechanism to dynamic failures during VO formations. Such a self-adaptive scheme allows an individual and member of VOs to automatically find other available or replaceable one once a failure happens and therefore makes systems automatically recover from dynamic failures. We define dynamic failure situations of a system by using two standard indicators: mean time between failures (MTBF) and mean time to recover (MTTR). We model both MTBF and MTTR as Poisson distributions. We investigate and analyze the efficiency of the proposed self-adaptation mechanism to dynamic failures by comparing the success probability of VO formations before and after adopting it in three different cases: (1) different failure situations; (2) different organizational structures and scales; (3) different task complexities. The experimental results show that the proposed scheme can automatically adapt to dynamic failures and effectively improve the dynamic VO formation performance in the event of node failures, which provide a valuable addition to the field.

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

The adaptive synchronization of fractional-order Liu chaotic system ...

Indian Academy of Sciences (India)

In this paper, the chaos control and the synchronization of two fractional-order Liu chaotic systems with unknown parameters are studied. According to the Lyapunov stabilization theory and the adaptive control theorem, the adaptive control rule is obtained for the described error dynamic stabilization. Using the adaptive rule ...

Towards Trustworthy Adaptive Case Management with Dynamic Condition Response Graphs

DEFF Research Database (Denmark)

Mukkamala, Raghava Rao; Hildebrandt, Thomas; Slaats, Tijs

2013-01-01

We describe how the declarative Dynamic Condition Response (DCR) Graphs process model can be used for trustworthy adaptive case management by leveraging the flexible execution, dynamic composition and adaptation supported by DCR Graphs. The dynamically composed and adapted graphs are verified for...

Time course of dynamic range adaptation in the auditory nerve

Science.gov (United States)

Wang, Grace I.; Dean, Isabel; Delgutte, Bertrand

2012-01-01

Auditory adaptation to sound-level statistics occurs as early as in the auditory nerve (AN), the first stage of neural auditory processing. In addition to firing rate adaptation characterized by a rate decrement dependent on previous spike activity, AN fibers show dynamic range adaptation, which is characterized by a shift of the rate-level function or dynamic range toward the most frequently occurring levels in a dynamic stimulus, thereby improving the precision of coding of the most common sound levels (Wen B, Wang GI, Dean I, Delgutte B. J Neurosci 29: 13797–13808, 2009). We investigated the time course of dynamic range adaptation by recording from AN fibers with a stimulus in which the sound levels periodically switch from one nonuniform level distribution to another (Dean I, Robinson BL, Harper NS, McAlpine D. J Neurosci 28: 6430–6438, 2008). Dynamic range adaptation occurred rapidly, but its exact time course was difficult to determine directly from the data because of the concomitant firing rate adaptation. To characterize the time course of dynamic range adaptation without the confound of firing rate adaptation, we developed a phenomenological “dual adaptation” model that accounts for both forms of AN adaptation. When fitted to the data, the model predicts that dynamic range adaptation occurs as rapidly as firing rate adaptation, over 100–400 ms, and the time constants of the two forms of adaptation are correlated. These findings suggest that adaptive processing in the auditory periphery in response to changes in mean sound level occurs rapidly enough to have significant impact on the coding of natural sounds. PMID:22457465

Cortical microcircuit dynamics mediating Binocular Rivalry: The role of adaptation in inhibition

Directory of Open Access Journals (Sweden)

Panagiota eTheodoni

2011-11-01

Full Text Available Perceptual bistability arises when two conflicting interpretations of an ambiguous stimulus or images in binocular rivalry (BR compete for perceptual dominance. From a computational point of view competition models based on cross-inhibition and adaptation have shown that noise is a crucial force for rivalry and operates in balance with adaptation in order to explain the observed alternations in perception. In particular, noise-driven transitions and adaptation-driven oscillations define two dynamical regimes and the system operates near its boundary. In order to gain insights into the microcircuit dynamics mediating spontaneous perceptual alternations we used a reduced recurrent attractor-based biophysically realistic spiking network well known for working memory, attention and decision-making, where a spike-frequency adaptation mechanism is implemented to account for perceptual bistability. We, thus, derived a consistently reduced four-variable population rate model using mean-field techniques and tested it on BR data collected from human subjects. Our model accounts for experimental data parameters such as time dominance, coefficient of variation and gamma distribution. In addition, we show that our model also operates on the boundary between noise and adaptation and agrees with Levelt’s second revised and fourth propositions. These results show for the first time that a consistent reduction of a biophysically realistic spiking network of integrate and fire neurons with spike frequency adaptation could account for BR. Moreover, we demonstrate that BR can be explained only through the dynamics of the competing neuronal pools, without taking into account the adaptation of inhibitory interneurons..However, adaptation of interneurons affects the optimal parametric space of the system, by decreasing the overall adaptation necessary for the bifurcation to occur.

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.

Complexity and network dynamics in physiological adaptation: an integrated view.

Science.gov (United States)

Baffy, György; Loscalzo, Joseph

2014-05-28

Living organisms constantly interact with their surroundings and sustain internal stability against perturbations. This dynamic process follows three fundamental strategies (restore, explore, and abandon) articulated in historical concepts of physiological adaptation such as homeostasis, allostasis, and the general adaptation syndrome. These strategies correspond to elementary forms of behavior (ordered, chaotic, and static) in complex adaptive systems and invite a network-based analysis of the operational characteristics, allowing us to propose an integrated framework of physiological adaptation from a complex network perspective. Applicability of this concept is illustrated by analyzing molecular and cellular mechanisms of adaptation in response to the pervasive challenge of obesity, a chronic condition resulting from sustained nutrient excess that prompts chaotic exploration for system stability associated with tradeoffs and a risk of adverse outcomes such as diabetes, cardiovascular disease, and cancer. Deconstruction of this complexity holds the promise of gaining novel insights into physiological adaptation in health and disease. Published by Elsevier Inc.

Integration of the immune system: a complex adaptive supersystem

Science.gov (United States)

Crisman, Mark V.

2001-10-01

Immunity to pathogenic organisms is a complex process involving interacting factors within the immune system including circulating cells, tissues and soluble chemical mediators. Both the efficiency and adaptive responses of the immune system in a dynamic, often hostile, environment are essential for maintaining our health and homeostasis. This paper will present a brief review of one of nature's most elegant, complex adaptive systems.

Policy Gradient Adaptive Dynamic Programming for Data-Based Optimal Control.

Science.gov (United States)

Luo, Biao; Liu, Derong; Wu, Huai-Ning; Wang, Ding; Lewis, Frank L

2017-10-01

The model-free optimal control problem of general discrete-time nonlinear systems is considered in this paper, and a data-based policy gradient adaptive dynamic programming (PGADP) algorithm is developed to design an adaptive optimal controller method. By using offline and online data rather than the mathematical system model, the PGADP algorithm improves control policy with a gradient descent scheme. The convergence of the PGADP algorithm is proved by demonstrating that the constructed Q -function sequence converges to the optimal Q -function. Based on the PGADP algorithm, the adaptive control method is developed with an actor-critic structure and the method of weighted residuals. Its convergence properties are analyzed, where the approximate Q -function converges to its optimum. Computer simulation results demonstrate the effectiveness of the PGADP-based adaptive control method.

Automated adaptive inference of phenomenological dynamical models

Science.gov (United States)

Daniels, Bryan

Understanding the dynamics of biochemical systems can seem impossibly complicated at the microscopic level: detailed properties of every molecular species, including those that have not yet been discovered, could be important for producing macroscopic behavior. The profusion of data in this area has raised the hope that microscopic dynamics might be recovered in an automated search over possible models, yet the combinatorial growth of this space has limited these techniques to systems that contain only a few interacting species. We take a different approach inspired by coarse-grained, phenomenological models in physics. Akin to a Taylor series producing Hooke's Law, forgoing microscopic accuracy allows us to constrain the search over dynamical models to a single dimension. This makes it feasible to infer dynamics with very limited data, including cases in which important dynamical variables are unobserved. We name our method Sir Isaac after its ability to infer the dynamical structure of the law of gravitation given simulated planetary motion data. Applying the method to output from a microscopically complicated but macroscopically simple biological signaling model, it is able to adapt the level of detail to the amount of available data. Finally, using nematode behavioral time series data, the method discovers an effective switch between behavioral attractors after the application of a painful stimulus.

Indirect fuzzy adaptive control of a class of SISO nonlinear systems

International Nuclear Information System (INIS)

Laboid, S.; Boucherit, M.S.

2006-01-01

This paper presents an adaptive fuzzy control scheme for a class of continuous-time single-input single-output nonlinear systems with unknown dynamics and disturbance. Within this scheme, the fuzzy systems are employed to approximate the unknown system's dynamics. The proposed controller is composed of a well-defined adaptive fuzzy control term that uses the adaptive fuzzy approximation errors and disturbance. Based on a Lyapunov synthesis method, it is shown that the proposed adaptive control scheme guarantees the convergence of the tracking error to zero and the global boundedness of all signals in the closed-loop system. Moreover, the proposed controller allows initialization by zero of all adjusted parameters in the fuzzy approximators, and does not require the knowledge of the lower bound of the control gain and upper bounds of the approximation errors and disturbance. Simulation results performed on an inverted pendulum system are given to point out the good performance of the developed adaptive controller. (author)

Generalization in adaptation to stable and unstable dynamics.

Directory of Open Access Journals (Sweden)

Abdelhamid Kadiallah

Full Text Available Humans skillfully manipulate objects and tools despite the inherent instability. In order to succeed at these tasks, the sensorimotor control system must build an internal representation of both the force and mechanical impedance. As it is not practical to either learn or store motor commands for every possible future action, the sensorimotor control system generalizes a control strategy for a range of movements based on learning performed over a set of movements. Here, we introduce a computational model for this learning and generalization, which specifies how to learn feedforward muscle activity in a function of the state space. Specifically, by incorporating co-activation as a function of error into the feedback command, we are able to derive an algorithm from a gradient descent minimization of motion error and effort, subject to maintaining a stability margin. This algorithm can be used to learn to coordinate any of a variety of motor primitives such as force fields, muscle synergies, physical models or artificial neural networks. This model for human learning and generalization is able to adapt to both stable and unstable dynamics, and provides a controller for generating efficient adaptive motor behavior in robots. Simulation results exhibit predictions consistent with all experiments on learning of novel dynamics requiring adaptation of force and impedance, and enable us to re-examine some of the previous interpretations of experiments on generalization.

Failure of large transformation projects from the viewpoint of complex adaptive systems: Management principles for dealing with project dynamics

NARCIS (Netherlands)

Janssen, M.; Voort, H. van der; Veenstra, A.F.E. van

2015-01-01

Many large transformation projects do not result in the outcomes desired or envisioned by the stakeholders. This type of project is characterised by dynamics which are both caused by and result of uncertainties and unexpected behaviour. In this paper a complex adaptive system (CAS) view was adopted

Dynamic and adaptive data-management in ATLAS

CERN Document Server

Lassnig, M; Branco, M; Molfetas, A

2010-01-01

Distributed data-management on the grid is subject to huge uncertainties yet static policies govern its usage. Due to the unpredictability of user behaviour, the high-latency and the heterogeneous nature of the environment, distributed data-management on the grid is challenging. In this paper we present the first steps towards a future dynamic data-management system that adapts to the changing conditions and environment. Such a system would eliminate the number of manual interventions and remove unnecessary software layers, thereby providing a higher quality of service to the collaboration.

Adaptive Synchronization of Memristor-based Chaotic Neural Systems

Directory of Open Access Journals (Sweden)

Xiaofang Hu

2014-11-01

Full Text Available Chaotic neural networks consisting of a great number of chaotic neurons are able to reproduce the rich dynamics observed in biological nervous systems. In recent years, the memristor has attracted much interest in the efficient implementation of artificial synapses and neurons. This work addresses adaptive synchronization of a class of memristor-based neural chaotic systems using a novel adaptive backstepping approach. A systematic design procedure is presented. Simulation results have demonstrated the effectiveness of the proposed adaptive synchronization method and its potential in practical application of memristive chaotic oscillators in secure communication.

Harm reduction as a complex adaptive system: A dynamic framework for analyzing Tanzanian policies concerning heroin use.

Science.gov (United States)

Ratliff, Eric A; Kaduri, Pamela; Masao, Frank; Mbwambo, Jessie K K; McCurdy, Sheryl A

2016-04-01

Contrary to popular belief, policies on drug use are not always based on scientific evidence or composed in a rational manner. Rather, decisions concerning drug policies reflect the negotiation of actors' ambitions, values, and facts as they organize in different ways around the perceived problems associated with illicit drug use. Drug policy is thus best represented as a complex adaptive system (CAS) that is dynamic, self-organizing, and coevolving. In this analysis, we use a CAS framework to examine how harm reduction emerged around heroin trafficking and use in Tanzania over the past thirty years (1985-present). This account is an organizational ethnography based on of the observant participation of the authors as actors within this system. We review the dynamic history and self-organizing nature of harm reduction, noting how interactions among system actors and components have coevolved with patterns of heroin us, policing, and treatment activities over time. Using a CAS framework, we describe harm reduction as a complex process where ambitions, values, facts, and technologies interact in the Tanzanian sociopolitical environment. We review the dynamic history and self-organizing nature of heroin policies, noting how the interactions within and between competing prohibitionist and harm reduction policies have changed with patterns of heroin use, policing, and treatment activities over time. Actors learn from their experiences to organize with other actors, align their values and facts, and implement new policies. Using a CAS approach provides researchers and policy actors a better understanding of patterns and intricacies in drug policy. This knowledge of how the system works can help improve the policy process through adaptive action to introduce new actors, different ideas, and avenues for communication into the system. Copyright © 2015 Elsevier B.V. All rights reserved.

Flatness-based adaptive fuzzy control of chaotic finance dynamics

Science.gov (United States)

Rigatos, G.; Siano, P.; Loia, V.; Tommasetti, A.; Troisi, O.

2017-11-01

A flatness-based adaptive fuzzy control is applied to the problem of stabilization of the dynamics of a chaotic finance system, describing interaction between the interest rate, the investment demand and the price exponent. By proving that the system is differentially flat and by applying differential flatness diffeomorphisms, its transformation to the linear canonical (Brunovsky) is performed. For the latter description of the system, the design of a stabilizing state feedback controller becomes possible. A first problem in the design of such a controller is that the dynamic model of the finance system is unknown and thus it has to be identified with the use neurofuzzy approximators. The estimated dynamics provided by the approximators is used in the computation of the control input, thus establishing an indirect adaptive control scheme. The learning rate of the approximators is chosen from the requirement the system's Lyapunov function to have always a negative first-order derivative. Another problem that has to be dealt with is that the control loop is implemented only with the use of output feedback. To estimate the non-measurable state vector elements of the finance system, a state observer is implemented in the control loop. The computation of the feedback control signal requires the solution of two algebraic Riccati equations at each iteration of the control algorithm. Lyapunov stability analysis demonstrates first that an H-infinity tracking performance criterion is satisfied. This signifies elevated robustness against modelling errors and external perturbations. Moreover, the global asymptotic stability is proven for the control loop.

Stock market modeling and forecasting a system adaptation approach

CERN Document Server

Zheng, Xiaolian

2013-01-01

Stock Market Modeling translates experience in system adaptation gained in an engineering context to the modeling of financial markets with a view to improving the capture and understanding of market dynamics. The modeling process is considered as identifying a dynamic system in which a real stock market is treated as an unknown plant and the identification model proposed is tuned by feedback of the matching error. Like a physical system, a stock market exhibits fast and slow dynamics corresponding to internal (such as company value and profitability) and external forces (such as investor sentiment and commodity prices) respectively. The framework presented here, consisting of an internal model and an adaptive filter, is successful at considering both fast and slow market dynamics. A double selection method is efficacious in identifying input factors influential in market movements, revealing them to be both frequency- and market-dependent.   The authors present work on both developed and developing markets ...

Adaptive Embedded Systems – Challenges of Run-Time Resource Management

DEFF Research Database (Denmark)

Understanding and efficiently controlling the dynamic behavior of adaptive embedded systems is a challenging endavor. The challenges come from the often very complicated interplay between the application, the application mapping, and the underlying hardware architecture. With MPSoC, we have...... the technology to design and fabricate dynamically reconfigurable hardware platforms. However, such platforms will pose new challenges to tools and methods to efficiently explore these platforms at run-time. This talk will address some of the challenges of run-time resource management in adaptive embedded...... systems....

Opinion dynamics on an adaptive random network

Science.gov (United States)

Benczik, I. J.; Benczik, S. Z.; Schmittmann, B.; Zia, R. K. P.

2009-04-01

We revisit the classical model for voter dynamics in a two-party system with two basic modifications. In contrast to the original voter model studied in regular lattices, we implement the opinion formation process in a random network of agents in which interactions are no longer restricted by geographical distance. In addition, we incorporate the rapidly changing nature of the interpersonal relations in the model. At each time step, agents can update their relationships. This update is determined by their own opinion, and by their preference to make connections with individuals sharing the same opinion, or rather with opponents. In this way, the network is built in an adaptive manner, in the sense that its structure is correlated and evolves with the dynamics of the agents. The simplicity of the model allows us to examine several issues analytically. We establish criteria to determine whether consensus or polarization will be the outcome of the dynamics and on what time scales these states will be reached. In finite systems consensus is typical, while in infinite systems a disordered metastable state can emerge and persist for infinitely long time before consensus is reached.

A review of human factors challenges of complex adaptive systems: discovering and understanding chaos in human performance.

Science.gov (United States)

Karwowski, Waldemar

2012-12-01

In this paper, the author explores a need for a greater understanding of the true nature of human-system interactions from the perspective of the theory of complex adaptive systems, including the essence of complexity, emergent properties of system behavior, nonlinear systems dynamics, and deterministic chaos. Human performance, more often than not, constitutes complex adaptive phenomena with emergent properties that exhibit nonlinear dynamical (chaotic) behaviors. The complexity challenges in the design and management of contemporary work systems, including service systems, are explored. Examples of selected applications of the concepts of nonlinear dynamics to the study of human physical performance are provided. Understanding and applications of the concepts of theory of complex adaptive and dynamical systems should significantly improve the effectiveness of human-centered design efforts of a large system of systems. Performance of many contemporary work systems and environments may be sensitive to the initial conditions and may exhibit dynamic nonlinear properties and chaotic system behaviors. Human-centered design of emergent human-system interactions requires application of the theories of nonlinear dynamics and complex adaptive system. The success of future human-systems integration efforts requires the fusion of paradigms, knowledge, design principles, and methodologies of human factors and ergonomics with those of the science of complex adaptive systems as well as modern systems engineering.

An extension of the classification of evolutionary singular strategies in Adaptive Dynamics

NARCIS (Netherlands)

Boldin, Barbara; Diekmann, Odo

2014-01-01

The existing classification of evolutionarily singular strategies in Adaptive Dynamics (Geritz et al. in Evol Ecol 12:35–57, 1998; Metz et al. in Stochastic and spatial structures of dynamical systems, pp 183–231, 1996) assumes an invasion exponent that is differentiable twice as a function of both

Adaptive sampling strategies with high-throughput molecular dynamics

Science.gov (United States)

Clementi, Cecilia

Despite recent significant hardware and software developments, the complete thermodynamic and kinetic characterization of large macromolecular complexes by molecular simulations still presents significant challenges. The high dimensionality of these systems and the complexity of the associated potential energy surfaces (creating multiple metastable regions connected by high free energy barriers) does not usually allow to adequately sample the relevant regions of their configurational space by means of a single, long Molecular Dynamics (MD) trajectory. Several different approaches have been proposed to tackle this sampling problem. We focus on the development of ensemble simulation strategies, where data from a large number of weakly coupled simulations are integrated to explore the configurational landscape of a complex system more efficiently. Ensemble methods are of increasing interest as the hardware roadmap is now mostly based on increasing core counts, rather than clock speeds. The main challenge in the development of an ensemble approach for efficient sampling is in the design of strategies to adaptively distribute the trajectories over the relevant regions of the systems' configurational space, without using any a priori information on the system global properties. We will discuss the definition of smart adaptive sampling approaches that can redirect computational resources towards unexplored yet relevant regions. Our approaches are based on new developments in dimensionality reduction for high dimensional dynamical systems, and optimal redistribution of resources. NSF CHE-1152344, NSF CHE-1265929, Welch Foundation C-1570.

Constitutional dynamic chemistry: bridge from supramolecular chemistry to adaptive chemistry.

Science.gov (United States)

Lehn, Jean-Marie

2012-01-01

Supramolecular chemistry aims at implementing highly complex chemical systems from molecular components held together by non-covalent intermolecular forces and effecting molecular recognition, catalysis and transport processes. A further step consists in the investigation of chemical systems undergoing self-organization, i.e. systems capable of spontaneously generating well-defined functional supramolecular architectures by self-assembly from their components, thus behaving as programmed chemical systems. Supramolecular chemistry is intrinsically a dynamic chemistry in view of the lability of the interactions connecting the molecular components of a supramolecular entity and the resulting ability of supramolecular species to exchange their constituents. The same holds for molecular chemistry when the molecular entity contains covalent bonds that may form and break reversibility, so as to allow a continuous change in constitution by reorganization and exchange of building blocks. These features define a Constitutional Dynamic Chemistry (CDC) on both the molecular and supramolecular levels.CDC introduces a paradigm shift with respect to constitutionally static chemistry. The latter relies on design for the generation of a target entity, whereas CDC takes advantage of dynamic diversity to allow variation and selection. The implementation of selection in chemistry introduces a fundamental change in outlook. Whereas self-organization by design strives to achieve full control over the output molecular or supramolecular entity by explicit programming, self-organization with selection operates on dynamic constitutional diversity in response to either internal or external factors to achieve adaptation.The merging of the features: -information and programmability, -dynamics and reversibility, -constitution and structural diversity, points to the emergence of adaptive and evolutive chemistry, towards a chemistry of complex matter.

Full Quantum Dynamics Simulation of a Realistic Molecular System Using the Adaptive Time-Dependent Density Matrix Renormalization Group Method.

Science.gov (United States)

Yao, Yao; Sun, Ke-Wei; Luo, Zhen; Ma, Haibo

2018-01-18

The accurate theoretical interpretation of ultrafast time-resolved spectroscopy experiments relies on full quantum dynamics simulations for the investigated system, which is nevertheless computationally prohibitive for realistic molecular systems with a large number of electronic and/or vibrational degrees of freedom. In this work, we propose a unitary transformation approach for realistic vibronic Hamiltonians, which can be coped with using the adaptive time-dependent density matrix renormalization group (t-DMRG) method to efficiently evolve the nonadiabatic dynamics of a large molecular system. We demonstrate the accuracy and efficiency of this approach with an example of simulating the exciton dissociation process within an oligothiophene/fullerene heterojunction, indicating that t-DMRG can be a promising method for full quantum dynamics simulation in large chemical systems. Moreover, it is also shown that the proper vibronic features in the ultrafast electronic process can be obtained by simulating the two-dimensional (2D) electronic spectrum by virtue of the high computational efficiency of the t-DMRG method.

Relational adaptivity - enacting human-centric systems design

DEFF Research Database (Denmark)

Petersen, Kjell Yngve

2016-01-01

Human centered design approaches places the experiencing human at the center of concern, situated in relation to the dynamics of the environmental condition and the variables of the system of control and sensing. Taking the approach of enacted design methods to enforce the experience...... of the inhabitant as core in human-centered design solutions, the intelligence of the connected sensors is suggested to be developed as an actual learning and self-adjusting adaptive environment, where the adaptive system is part of a negotiation with users on the qualities of the environment. We will present...... a fully functional sketching environment for adaptive sensor-control systems, which enable integration of the complex situation of everyday activities and human well-being. The proposed sketching environment allows for the development of sensor systems related to lighting conditions and human occupancy...

Adaptive Management of Computing and Network Resources for Spacecraft Systems

Science.gov (United States)

Pfarr, Barbara; Welch, Lonnie R.; Detter, Ryan; Tjaden, Brett; Huh, Eui-Nam; Szczur, Martha R. (Technical Monitor)

2000-01-01

It is likely that NASA's future spacecraft systems will consist of distributed processes which will handle dynamically varying workloads in response to perceived scientific events, the spacecraft environment, spacecraft anomalies and user commands. Since all situations and possible uses of sensors cannot be anticipated during pre-deployment phases, an approach for dynamically adapting the allocation of distributed computational and communication resources is needed. To address this, we are evolving the DeSiDeRaTa adaptive resource management approach to enable reconfigurable ground and space information systems. The DeSiDeRaTa approach embodies a set of middleware mechanisms for adapting resource allocations, and a framework for reasoning about the real-time performance of distributed application systems. The framework and middleware will be extended to accommodate (1) the dynamic aspects of intra-constellation network topologies, and (2) the complete real-time path from the instrument to the user. We are developing a ground-based testbed that will enable NASA to perform early evaluation of adaptive resource management techniques without the expense of first deploying them in space. The benefits of the proposed effort are numerous, including the ability to use sensors in new ways not anticipated at design time; the production of information technology that ties the sensor web together; the accommodation of greater numbers of missions with fewer resources; and the opportunity to leverage the DeSiDeRaTa project's expertise, infrastructure and models for adaptive resource management for distributed real-time 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.

Dynamic Surface Adaptive Robust Control of Unmanned Marine Vehicles with Disturbance Observer

Directory of Open Access Journals (Sweden)

Pengchao Zhang

2018-01-01

Full Text Available This paper presents a dynamic surface adaptive robust control method with disturbance observer for unmanned marine vehicles (UMV. It uses adaptive law to estimate and compensate the disturbance observer error. Dynamic surface is introduced to solve the “differential explosion” caused by the virtual control derivation in traditional backstepping method. The final controlled system is proved to be globally uniformly bounded based on Lyapunov stability theory. Simulation results illustrate the effectiveness of the proposed controller, which can realize the three-dimensional trajectory tracking for UMV with the systematic uncertainty and time-varying disturbances.

Indirect adaptive control of discrete chaotic systems

International Nuclear Information System (INIS)

Salarieh, Hassan; Shahrokhi, Mohammad

2007-01-01

In this paper an indirect adaptive control algorithm is proposed to stabilize the fixed points of discrete chaotic systems. It is assumed that the functionality of the chaotic dynamics is known but the system parameters are unknown. This assumption is usually applicable to many chaotic systems, such as the Henon map, logistic and many other nonlinear maps. Using the recursive-least squares technique, the system parameters are identified and based on the feedback linearization method an adaptive controller is designed for stabilizing the fixed points, or unstable periodic orbits of the chaotic maps. The stability of the proposed scheme has been shown and the effectiveness of the control algorithm has been demonstrated through computer simulations

An Adaptable Neuromorphic Model of Orientation Selectivity Based On Floating Gate Dynamics

Directory of Open Access Journals (Sweden)

Priti eGupta

2014-04-01

Full Text Available The biggest challenge that the neuromorphic community faces today is to build systems that can be considered truly cognitive. Adaptation and self-organization are the two basic principles that underlie any cognitive function that the brain performs. If we can replicate this behavior in hardware, we move a step closer to our goal of having cognitive neuromorphic systems. Adaptive feature selectivity is a mechanism by which nature optimizes resources so as to have greater acuity for more abundant features. Developing neuromorphic feature maps can help design generic machines that can emulate this adaptive behavior. Most neuromorphic models that have attempted to build self-organizing systems, follow the approach of modeling abstract theoretical frameworks in hardware. While this is good from a modeling and analysis perspective, it may not lead to the most efficient hardware. On the other hand, exploiting hardware dynamics to build adaptive systems rather than forcing the hardware to behave like mathematical equations, seems to be a more robust methodology when it comes to developing actual hardware for real world applications. In this paper we use a novel time-staggered Winner Take All circuit, that exploits the adaptation dynamics of floating gate transistors, to model an adaptive cortical cell that demonstrates Orientation Selectivity, a well-known biological phenomenon observed in the visual cortex. The cell performs competitive learning, refining its weights in response to input patterns resembling different oriented bars, becoming selective to a particular oriented pattern. Different analysis performed on the cell such as orientation tuning, application of abnormal inputs, response to spatial frequency and periodic patterns reveal close similarity between our cell and its biological counterpart. Embedded in a RC grid, these cells interact diffusively exhibiting cluster formation, making way for adaptively building orientation selective maps

Finite-horizon differential games for missile-target interception system using adaptive dynamic programming with input constraints

Science.gov (United States)

Sun, Jingliang; Liu, Chunsheng

2018-01-01

In this paper, the problem of intercepting a manoeuvring target within a fixed final time is posed in a non-linear constrained zero-sum differential game framework. The Nash equilibrium solution is found by solving the finite-horizon constrained differential game problem via adaptive dynamic programming technique. Besides, a suitable non-quadratic functional is utilised to encode the control constraints into a differential game problem. The single critic network with constant weights and time-varying activation functions is constructed to approximate the solution of associated time-varying Hamilton-Jacobi-Isaacs equation online. To properly satisfy the terminal constraint, an additional error term is incorporated in a novel weight-updating law such that the terminal constraint error is also minimised over time. By utilising Lyapunov's direct method, the closed-loop differential game system and the estimation weight error of the critic network are proved to be uniformly ultimately bounded. Finally, the effectiveness of the proposed method is demonstrated by using a simple non-linear system and a non-linear missile-target interception system, assuming first-order dynamics for the interceptor and target.

An adaptive cubature formula for efficient reliability assessment of nonlinear structural dynamic systems

Science.gov (United States)

Xu, Jun; Kong, Fan

2018-05-01

Extreme value distribution (EVD) evaluation is a critical topic in reliability analysis of nonlinear structural dynamic systems. In this paper, a new method is proposed to obtain the EVD. The maximum entropy method (MEM) with fractional moments as constraints is employed to derive the entire range of EVD. Then, an adaptive cubature formula is proposed for fractional moments assessment involved in MEM, which is closely related to the efficiency and accuracy for reliability analysis. Three point sets, which include a total of 2d2 + 1 integration points in the dimension d, are generated in the proposed formula. In this regard, the efficiency of the proposed formula is ensured. Besides, a "free" parameter is introduced, which makes the proposed formula adaptive with the dimension. The "free" parameter is determined by arranging one point set adjacent to the boundary of the hyper-sphere which contains the bulk of total probability. In this regard, the tail distribution may be better reproduced and the fractional moments could be evaluated with accuracy. Finally, the proposed method is applied to a ten-storey shear frame structure under seismic excitations, which exhibits strong nonlinearity. The numerical results demonstrate the efficacy of the proposed method.

Methodology for Simulation and Analysis of Complex Adaptive Supply Network Structure and Dynamics Using Information Theory

Directory of Open Access Journals (Sweden)

Joshua Rodewald

2016-10-01

Full Text Available Supply networks existing today in many industries can behave as complex adaptive systems making them more difficult to analyze and assess. Being able to fully understand both the complex static and dynamic structures of a complex adaptive supply network (CASN are key to being able to make more informed management decisions and prioritize resources and production throughout the network. Previous efforts to model and analyze CASN have been impeded by the complex, dynamic nature of the systems. However, drawing from other complex adaptive systems sciences, information theory provides a model-free methodology removing many of those barriers, especially concerning complex network structure and dynamics. With minimal information about the network nodes, transfer entropy can be used to reverse engineer the network structure while local transfer entropy can be used to analyze the network structure’s dynamics. Both simulated and real-world networks were analyzed using this methodology. Applying the methodology to CASNs allows the practitioner to capitalize on observations from the highly multidisciplinary field of information theory which provides insights into CASN’s self-organization, emergence, stability/instability, and distributed computation. This not only provides managers with a more thorough understanding of a system’s structure and dynamics for management purposes, but also opens up research opportunities into eventual strategies to monitor and manage emergence and adaption within the environment.

Discrete Model Reference Adaptive Control System for Automatic Profiling Machine

Directory of Open Access Journals (Sweden)

Peng Song

2012-01-01

Full Text Available Automatic profiling machine is a movement system that has a high degree of parameter variation and high frequency of transient process, and it requires an accurate control in time. In this paper, the discrete model reference adaptive control system of automatic profiling machine is discussed. Firstly, the model of automatic profiling machine is presented according to the parameters of DC motor. Then the design of the discrete model reference adaptive control is proposed, and the control rules are proven. The results of simulation show that adaptive control system has favorable dynamic performances.

Chemotactic response and adaptation dynamics in Escherichia coli.

Directory of Open Access Journals (Sweden)

Diana Clausznitzer

2010-05-01

Full Text Available Adaptation of the chemotaxis sensory pathway of the bacterium Escherichia coli is integral for detecting chemicals over a wide range of background concentrations, ultimately allowing cells to swim towards sources of attractant and away from repellents. Its biochemical mechanism based on methylation and demethylation of chemoreceptors has long been known. Despite the importance of adaptation for cell memory and behavior, the dynamics of adaptation are difficult to reconcile with current models of precise adaptation. Here, we follow time courses of signaling in response to concentration step changes of attractant using in vivo fluorescence resonance energy transfer measurements. Specifically, we use a condensed representation of adaptation time courses for efficient evaluation of different adaptation models. To quantitatively explain the data, we finally develop a dynamic model for signaling and adaptation based on the attractant flow in the experiment, signaling by cooperative receptor complexes, and multiple layers of feedback regulation for adaptation. We experimentally confirm the predicted effects of changing the enzyme-expression level and bypassing the negative feedback for demethylation. Our data analysis suggests significant imprecision in adaptation for large additions. Furthermore, our model predicts highly regulated, ultrafast adaptation in response to removal of attractant, which may be useful for fast reorientation of the cell and noise reduction in adaptation.

Learning-Based Adaptive Optimal Tracking Control of Strict-Feedback Nonlinear Systems.

Science.gov (United States)

Gao, Weinan; Jiang, Zhong-Ping; Weinan Gao; Zhong-Ping Jiang; Gao, Weinan; Jiang, Zhong-Ping

2018-06-01

This paper proposes a novel data-driven control approach to address the problem of adaptive optimal tracking for a class of nonlinear systems taking the strict-feedback form. Adaptive dynamic programming (ADP) and nonlinear output regulation theories are integrated for the first time to compute an adaptive near-optimal tracker without any a priori knowledge of the system dynamics. Fundamentally different from adaptive optimal stabilization problems, the solution to a Hamilton-Jacobi-Bellman (HJB) equation, not necessarily a positive definite function, cannot be approximated through the existing iterative methods. This paper proposes a novel policy iteration technique for solving positive semidefinite HJB equations with rigorous convergence analysis. A two-phase data-driven learning method is developed and implemented online by ADP. The efficacy of the proposed adaptive optimal tracking control methodology is demonstrated via a Van der Pol oscillator with time-varying exogenous signals.

Adaptive numerical modeling of dynamic crack propagation

International Nuclear Information System (INIS)

Adouani, H.; Tie, B.; Berdin, C.; Aubry, D.

2006-01-01

We propose an adaptive numerical strategy that aims at developing reliable and efficient numerical tools to model dynamic crack propagation and crack arrest. We use the cohesive zone theory as behavior of interface-type elements to model crack. Since the crack path is generally unknown beforehand, adaptive meshing is proposed to model the dynamic crack propagation. The dynamic study requires the development of specific solvers for time integration. As both geometry and finite element mesh of the studied structure evolve in time during transient analysis, the stability behavior of dynamic solver becomes a major concern. For this purpose, we use the space-time discontinuous Galerkin finite element method, well-known to provide a natural framework to manage meshes that evolve in time. As an important result, we prove that the space-time discontinuous Galerkin solver is unconditionally stable, when the dynamic crack propagation is modeled by the cohesive zone theory, which is highly non-linear. (authors)

Elucidating Microbial Adaptation Dynamics via Autonomous Exposure and Sampling

Science.gov (United States)

Grace, Joseph M.; Verseux, Cyprien; Gentry, Diana; Moffet, Amy; Thayabaran, Ramanen; Wong, Nathan; Rothschild, Lynn

2013-01-01

The adaptation of micro-organisms to their environments is a complex process of interaction between the pressures of the environment and of competition. Reducing this multifactorial process to environmental exposure in the laboratory is a common tool for elucidating individual mechanisms of evolution, such as mutation rates. Although such studies inform fundamental questions about the way adaptation and even speciation occur, they are often limited by labor-intensive manual techniques. Current methods for controlled study of microbial adaptation limit the length of time, the depth of collected data, and the breadth of applied environmental conditions. Small idiosyncrasies in manual techniques can have large effects on outcomes; for example, there are significant variations in induced radiation resistances following similar repeated exposure protocols. We describe here a project under development to allow rapid cycling of multiple types of microbial environmental exposure. The system allows continuous autonomous monitoring and data collection of both single species and sampled communities, independently and concurrently providing multiple types of controlled environmental pressure (temperature, radiation, chemical presence or absence, and so on) to a microbial community in dynamic response to the ecosystem's current status. When combined with DNA sequencing and extraction, such a controlled environment can cast light on microbial functional development, population dynamics, inter- and intra-species competition, and microbe-environment interaction. The project's goal is to allow rapid, repeatable iteration of studies of both natural and artificial microbial adaptation. As an example, the same system can be used both to increase the pH of a wet soil aliquot over time while periodically sampling it for genetic activity analysis, or to repeatedly expose a culture of bacteria to the presence of a toxic metal, automatically adjusting the level of toxicity based on the

Introduction to State Estimation of High-Rate System Dynamics.

Science.gov (United States)

Hong, Jonathan; Laflamme, Simon; Dodson, Jacob; Joyce, Bryan

2018-01-13

Engineering systems experiencing high-rate dynamic events, including airbags, debris detection, and active blast protection systems, could benefit from real-time observability for enhanced performance. However, the task of high-rate state estimation is challenging, in particular for real-time applications where the rate of the observer's convergence needs to be in the microsecond range. This paper identifies the challenges of state estimation of high-rate systems and discusses the fundamental characteristics of high-rate systems. A survey of applications and methods for estimators that have the potential to produce accurate estimations for a complex system experiencing highly dynamic events is presented. It is argued that adaptive observers are important to this research. In particular, adaptive data-driven observers are advantageous due to their adaptability and lack of dependence on the system model.

Auto-adaptative Robot-aided Therapy based in 3D Virtual Tasks controlled by a Supervised and Dynamic Neuro-Fuzzy System

Directory of Open Access Journals (Sweden)

Luis Daniel Lledó

2015-03-01

Full Text Available This paper presents an application formed by a classification method based on the architecture of ART neural network (Adaptive Resonance Theory and the Fuzzy Set Theory to classify physiological reactions in order to automatically and dynamically adapt a robot-assisted rehabilitation therapy to the patient needs, using a three-dimensional task in a virtual reality system. Firstly, the mathematical and structural model of the neuro-fuzzy classification method is described together with the signal and training data acquisition. Then, the virtual designed task with physics behavior and its development procedure are explained. Finally, the general architecture of the experimentation for the auto-adaptive therapy is presented using the classification method with the virtual reality exercise.

Physiological complexity and system adaptability: evidence from postural control dynamics of older adults.

Science.gov (United States)

Manor, Brad; Costa, Madalena D; Hu, Kun; Newton, Elizabeth; Starobinets, Olga; Kang, Hyun Gu; Peng, C K; Novak, Vera; Lipsitz, Lewis A

2010-12-01

The degree of multiscale complexity in human behavioral regulation, such as that required for postural control, appears to decrease with advanced aging or disease. To help delineate causes and functional consequences of complexity loss, we examined the effects of visual and somatosensory impairment on the complexity of postural sway during quiet standing and its relationship to postural adaptation to cognitive dual tasking. Participants of the MOBILIZE Boston Study were classified into mutually exclusive groups: controls [intact vision and foot somatosensation, n = 299, 76 ± 5 (SD) yr old], visual impairment only (Postural sway (i.e., center-of-pressure) dynamics were assessed during quiet standing and cognitive dual tasking, and a complexity index was quantified using multiscale entropy analysis. Postural sway speed and area, which did not correlate with complexity, were also computed. During quiet standing, the complexity index (mean ± SD) was highest in controls (9.5 ± 1.2) and successively lower in the visual (9.1 ± 1.1), somatosensory (8.6 ± 1.6), and combined (7.8 ± 1.3) impairment groups (P = 0.001). Dual tasking resulted in increased sway speed and area but reduced complexity (P postural sway speed from quiet standing to dual-tasking conditions. Sensory impairments contributed to decreased postural sway complexity, which reflected reduced adaptive capacity of the postural control system. Relatively low baseline complexity may, therefore, indicate control systems that are more vulnerable to cognitive and other stressors.

Distributed Coordination of Fractional Dynamical Systems with Exogenous Disturbances

Directory of Open Access Journals (Sweden)

Hongyong Yang

2014-01-01

Full Text Available Distributed coordination of fractional multiagent systems with external disturbances is studied. The state observer of fractional dynamical system is presented, and an adaptive pinning controller is designed for a little part of agents in multiagent systems without disturbances. This adaptive pinning controller with the state observer can ensure multiple agents' states reaching an expected reference tracking. Based on disturbance observers, the controllers are composited with the pinning controller and the state observer. By applying the stability theory of fractional order dynamical systems, the distributed coordination of fractional multiagent systems with external disturbances can be reached asymptotically.

Adaptive PI Controller for a Nonlinear System

Directory of Open Access Journals (Sweden)

D. Rathikarani

2009-10-01

Full Text Available Most of the industrial processes are inherently nonlinear in their behaviour. Designs of controllers for these nonlinear processes are difficult, as they do not follow superposition theorem. Adaptive controller can change its behaviour in response to changes in the dynamics of the process and disturbances. Hence adaptive controller can be used to control nonlinear processes. Direct Model Reference Adaptive Control is a technique, in which a reference model involving the desired performances is specified. In the present work, a DMRAC is designed and implemented to achieve satisfactory control of a nonlinear system in all its local linear operating regions. The closed loop system is made BIBO stable by proper control techniques. The controller is designed through simulation in Matlab platform and is validated in real time by conducting experiments on the laboratory Air Flow Control System using the dSPACE interface.

Extracting neuronal functional network dynamics via adaptive Granger causality analysis.

Science.gov (United States)

Sheikhattar, Alireza; Miran, Sina; Liu, Ji; Fritz, Jonathan B; Shamma, Shihab A; Kanold, Patrick O; Babadi, Behtash

2018-04-24

Quantifying the functional relations between the nodes in a network based on local observations is a key challenge in studying complex systems. Most existing time series analysis techniques for this purpose provide static estimates of the network properties, pertain to stationary Gaussian data, or do not take into account the ubiquitous sparsity in the underlying functional networks. When applied to spike recordings from neuronal ensembles undergoing rapid task-dependent dynamics, they thus hinder a precise statistical characterization of the dynamic neuronal functional networks underlying adaptive behavior. We develop a dynamic estimation and inference paradigm for extracting functional neuronal network dynamics in the sense of Granger, by integrating techniques from adaptive filtering, compressed sensing, point process theory, and high-dimensional statistics. We demonstrate the utility of our proposed paradigm through theoretical analysis, algorithm development, and application to synthetic and real data. Application of our techniques to two-photon Ca 2+ imaging experiments from the mouse auditory cortex reveals unique features of the functional neuronal network structures underlying spontaneous activity at unprecedented spatiotemporal resolution. Our analysis of simultaneous recordings from the ferret auditory and prefrontal cortical areas suggests evidence for the role of rapid top-down and bottom-up functional dynamics across these areas involved in robust attentive behavior.

Evaluating sustainable adaptation strategies for vulnerable mega-deltas using system dynamics modelling: Rice agriculture in the Mekong Delta's An Giang Province, Vietnam.

Science.gov (United States)

Chapman, Alexander; Darby, Stephen

2016-07-15

Challenging dynamics are unfolding in social-ecological systems around the globe as society attempts to mitigate and adapt to climate change while sustaining rapid local development. The IPCC's 5th assessment suggests these changing systems are susceptible to unforeseen and dangerous 'emergent risks'. An archetypal example is the Vietnamese Mekong Delta (VMD) where the river dyke network has been heightened and extended over the last decade with the dual objectives of (1) adapting the delta's 18 million inhabitants and their livelihoods to increasingly intense river-flooding, and (2) developing rice production through a shift from double to triple-cropping. Negative impacts have been associated with this shift, particularly in relation to its exclusion of fluvial sediment deposition from the floodplain. A deficit in our understanding of the dynamics of the rice-sediment system, which involve unintuitive delays, feedbacks, and tipping points, is addressed here, using a system dynamics (SD) approach to inform sustainable adaptation strategies. Specifically, we develop and test a new SD model which simulates the dynamics between the farmers' economic system and their rice agriculture operations, and uniquely, integrates the role of fluvial sediment deposition within their dyke compartment. We use the model to explore a range of alternative rice cultivation strategies. Our results suggest that the current dominant strategy (triple-cropping) is only optimal for wealthier groups within society and over the short-term (ca. 10years post-implementation). The model suggests that the policy of opening sluice gates and leaving paddies fallow during high-flood years, in order to encourage natural sediment deposition and the nutrient replenishment it supplies, is both a more equitable and a more sustainable policy. But, even with this approach, diminished supplies of sediment-bound nutrients and the consequent need to compensate with artificial fertilisers will mean that smaller

Self-Adaptive On-Chip System Based on Cross-Layer Adaptation Approach

Directory of Open Access Journals (Sweden)

Kais Loukil

2013-01-01

Full Text Available The emergence of mobile and battery operated multimedia systems and the diversity of supported applications mount new challenges in terms of design efficiency of these systems which must provide a maximum application quality of service (QoS in the presence of a dynamically varying environment. These optimization problems cannot be entirely solved at design time and some efficiency gains can be obtained at run-time by means of self-adaptivity. In this paper, we propose a new cross-layer hardware (HW/software (SW adaptation solution for embedded mobile systems. It supports application QoS under real-time and lifetime constraints via coordinated adaptation in the hardware, operating system (OS, and application layers. Our method relies on an original middleware solution used on both global and local managers. The global manager (GM handles large, long-term variations whereas the local manager (LM is used to guarantee real-time constraints. The GM acts in three layers whereas the LM acts in application and OS layers only. The main role of GM is to select the best configuration for each application to meet the constraints of the system and respect the preferences of the user. The proposed approach has been applied to a 3D graphics application and successfully implemented on an Altera FPGA.

HOLD MODE BASED DYNAMIC PRIORITY LOAD ADAPTIVE INTERPICONET SCHEDULING FOR BLUETOOTH SCATTERNETS

Directory of Open Access Journals (Sweden)

G.S. Mahalakshmi

2011-09-01

Full Text Available Scheduling in piconets has emerged as a challenging research area. Interpiconet scheduling focuses on when a bridge is switched among various piconets and how a bridge node communicates with the masters in different piconets. This paper proposes an interpiconet scheduling algorithm named, hold mode based dynamic traffic priority load adaptive scheduling. The bridges are adaptively switched between the piconets according to various traffic loads. The main goal is to maximize the utilization of the bridge by reducing the bridge switch wastes, utilize intelligent decision making algorithm, resolve conflict between the masters, and allow negotiation for bridge utilization in HDPLIS using bridge failure-bridge repair procedure . The Hold mode - dynamic traffic - priority based - load adaptive scheduling reduces the number of bridge switch wastes and hence increases the efficiency of the bridge which results in increased performance of the system.

Adaptive SLICE method: an enhanced method to determine nonlinear dynamic respiratory system mechanics

International Nuclear Information System (INIS)

Zhao, Zhanqi; Möller, Knut; Guttmann, Josef

2012-01-01

The objective of this paper is to introduce and evaluate the adaptive SLICE method (ASM) for continuous determination of intratidal nonlinear dynamic compliance and resistance. The tidal volume is subdivided into a series of volume intervals called slices. For each slice, one compliance and one resistance are calculated by applying a least-squares-fit method. The volume window (width) covered by each slice is determined based on the confidence interval of the parameter estimation. The method was compared to the original SLICE method and evaluated using simulation and animal data. The ASM was also challenged with separate analysis of dynamic compliance during inspiration. If the signal-to-noise ratio (SNR) in the respiratory data decreased from +∞ to 10 dB, the relative errors of compliance increased from 0.1% to 22% for the ASM and from 0.2% to 227% for the SLICE method. Fewer differences were found in resistance. When the SNR was larger than 40 dB, the ASM delivered over 40 parameter estimates (42.2 ± 1.3). When analyzing the compliance during inspiration separately, the estimates calculated with the ASM were more stable. The adaptive determination of slice bounds results in consistent and reliable parameter values. Online analysis of nonlinear respiratory mechanics will profit from such an adaptive selection of interval size. (paper)

Modeling Adaptive Behavior for Systems Design

DEFF Research Database (Denmark)

Rasmussen, Jens

1994-01-01

Field studies in modern work systems and analysis of recent major accidents have pointed to a need for better models of the adaptive behavior of individuals and organizations operating in a dynamic and highly competitive environment. The paper presents a discussion of some key characteristics.......) The basic difference between the models of system functions used in engineering and design and those evolving from basic research within the various academic disciplines and finally 3.) The models and methods required for closed-loop, feedback system design....

Indirect learning control for nonlinear dynamical systems

Science.gov (United States)

Ryu, Yeong Soon; Longman, Richard W.

1993-01-01

In a previous paper, learning control algorithms were developed based on adaptive control ideas for linear time variant systems. The learning control methods were shown to have certain advantages over their adaptive control counterparts, such as the ability to produce zero tracking error in time varying systems, and the ability to eliminate repetitive disturbances. In recent years, certain adaptive control algorithms have been developed for multi-body dynamic systems such as robots, with global guaranteed convergence to zero tracking error for the nonlinear system euations. In this paper we study the relationship between such adaptive control methods designed for this specific class of nonlinear systems, and the learning control problem for such systems, seeking to converge to zero tracking error in following a specific command repeatedly, starting from the same initial conditions each time. The extension of these methods from the adaptive control problem to the learning control problem is seen to be trivial. The advantages and disadvantages of using learning control based on such adaptive control concepts for nonlinear systems, and the use of other currently available learning control algorithms are discussed.

Adaptive fuzzy bilinear observer based synchronization design for generalized Lorenz system

International Nuclear Information System (INIS)

Baek, Jaeho; Lee, Heejin; Kim, Seungwoo; Park, Mignon

2009-01-01

This Letter proposes an adaptive fuzzy bilinear observer (FBO) based synchronization design for generalized Lorenz system (GLS). The GLS can be described to TS fuzzy bilinear generalized Lorenz model (FBGLM) with their states immeasurable and their parameters unknown. We design an adaptive FBO based on TS FBGLM for synchronization. Lyapunov theory is employed to guarantee the stability of error dynamic system via linear matrix equalities (LMIs) and to derive the adaptive laws to estimate unknown parameters. Numerical example is given to demonstrate the validity of our proposed adaptive FBO approach for synchronization.

Atomic switch networks as complex adaptive systems

Science.gov (United States)

Scharnhorst, Kelsey S.; Carbajal, Juan P.; Aguilera, Renato C.; Sandouk, Eric J.; Aono, Masakazu; Stieg, Adam Z.; Gimzewski, James K.

2018-03-01

Complexity is an increasingly crucial aspect of societal, environmental and biological phenomena. Using a dense unorganized network of synthetic synapses it is shown that a complex adaptive system can be physically created on a microchip built especially for complex problems. These neuro-inspired atomic switch networks (ASNs) are a dynamic system with inherent and distributed memory, recurrent pathways, and up to a billion interacting elements. We demonstrate key parameters describing self-organized behavior such as non-linearity, power law dynamics, and multistate switching regimes. Device dynamics are then investigated using a feedback loop which provides control over current and voltage power-law behavior. Wide ranging prospective applications include understanding and eventually predicting future events that display complex emergent behavior in the critical regime.

Contraction theory based adaptive synchronization of chaotic systems

International Nuclear Information System (INIS)

Sharma, B.B.; Kar, I.N.

2009-01-01

Contraction theory based stability analysis exploits the incremental behavior of trajectories of a system with respect to each other. Application of contraction theory provides an alternative way for stability analysis of nonlinear systems. This paper considers the design of a control law for synchronization of certain class of chaotic systems based on backstepping technique. The controller is selected so as to make the error dynamics between the two systems contracting. Synchronization problem with and without uncertainty in system parameters is discussed and necessary stability proofs are worked out using contraction theory. Suitable adaptation laws for unknown parameters are proposed based on the contraction principle. The numerical simulations verify the synchronization of the chaotic systems. Also parameter estimates converge to their true values with the proposed adaptation laws.

The importance of socio-ecological system dynamics in understanding adaptation to global change in the forestry sector.

Science.gov (United States)

Blanco, Victor; Brown, Calum; Holzhauer, Sascha; Vulturius, Gregor; Rounsevell, Mark D A

2017-07-01

Adaptation is necessary to cope with or take advantage of the effects of climate change on socio-ecological systems. This is especially important in the forestry sector, which is sensitive to the ecological and economic impacts of climate change, and where the adaptive decisions of owners play out over long periods of time. Relatively little is known about how successful these decisions are likely to be in meeting demands for ecosystem services in an uncertain future. We explore adaptation to global change in the forestry sector using CRAFTY-Sweden; an agent-based model that represents large-scale land-use dynamics, based on the demand and supply of ecosystem services. Future impacts and adaptation within the Swedish forestry sector were simulated for scenarios of socio-economic change (Shared Socio-economic Pathways) and climatic change (Representative Concentration Pathways, for three climate models), between 2010 and 2100. Substantial differences were found in the competitiveness and coping ability of land owners implementing different management strategies through time. Generally, multi-objective management was found to provide the best basis for adaptation. Across large regions, however, a combination of management strategies was better at meeting ecosystem service demands. Results also show that adaptive capacity evolves through time in response to external (global) drivers and interactions between individual actors. This suggests that process-based models are more appropriate for the study of autonomous adaptation and future adaptive and coping capacities than models based on indicators, discrete time snapshots or exogenous proxies. Nevertheless, a combination of planned and autonomous adaptation by institutions and forest owners is likely to be more successful than either group acting alone. Copyright © 2017 Elsevier Ltd. All rights reserved.

Fuzzy model-based adaptive synchronization of time-delayed chaotic systems

International Nuclear Information System (INIS)

Vasegh, Nastaran; Majd, Vahid Johari

2009-01-01

In this paper, fuzzy model-based synchronization of a class of first order chaotic systems described by delayed-differential equations is addressed. To design the fuzzy controller, the chaotic system is modeled by Takagi-Sugeno fuzzy system considering the properties of the nonlinear part of the system. Assuming that the parameters of the chaotic system are unknown, an adaptive law is derived to estimate these unknown parameters, and the stability of error dynamics is guaranteed by Lyapunov theory. Numerical examples are given to demonstrate the validity of the proposed adaptive synchronization approach.

Adaptive filtering prediction and control

CERN Document Server

Goodwin, Graham C

2009-01-01

Preface1. Introduction to Adaptive TechniquesPart 1. Deterministic Systems2. Models for Deterministic Dynamical Systems3. Parameter Estimation for Deterministic Systems4. Deterministic Adaptive Prediction5. Control of Linear Deterministic Systems6. Adaptive Control of Linear Deterministic SystemsPart 2. Stochastic Systems7. Optimal Filtering and Prediction8. Parameter Estimation for Stochastic Dynamic Systems9. Adaptive Filtering and Prediction10. Control of Stochastic Systems11. Adaptive Control of Stochastic SystemsAppendicesA. A Brief Review of Some Results from Systems TheoryB. A Summary o

Application of Non-Kolmogorovian Probability and Quantum Adaptive Dynamics to Unconscious Inference in Visual Perception Process

Science.gov (United States)

Accardi, Luigi; Khrennikov, Andrei; Ohya, Masanori; Tanaka, Yoshiharu; Yamato, Ichiro

2016-07-01

Recently a novel quantum information formalism — quantum adaptive dynamics — was developed and applied to modelling of information processing by bio-systems including cognitive phenomena: from molecular biology (glucose-lactose metabolism for E.coli bacteria, epigenetic evolution) to cognition, psychology. From the foundational point of view quantum adaptive dynamics describes mutual adapting of the information states of two interacting systems (physical or biological) as well as adapting of co-observations performed by the systems. In this paper we apply this formalism to model unconscious inference: the process of transition from sensation to perception. The paper combines theory and experiment. Statistical data collected in an experimental study on recognition of a particular ambiguous figure, the Schröder stairs, support the viability of the quantum(-like) model of unconscious inference including modelling of biases generated by rotation-contexts. From the probabilistic point of view, we study (for concrete experimental data) the problem of contextuality of probability, its dependence on experimental contexts. Mathematically contextuality leads to non-Komogorovness: probability distributions generated by various rotation contexts cannot be treated in the Kolmogorovian framework. At the same time they can be embedded in a “big Kolmogorov space” as conditional probabilities. However, such a Kolmogorov space has too complex structure and the operational quantum formalism in the form of quantum adaptive dynamics simplifies the modelling essentially.

Modeling of memristor-based chaotic systems using nonlinear Wiener adaptive filters based on backslash operator

International Nuclear Information System (INIS)

Zhao, Yibo; Jiang, Yi; Feng, Jiuchao; Wu, Lifu

2016-01-01

Highlights: • A novel nonlinear Wiener adaptive filters based on the backslash operator are proposed. • The identification approach to the memristor-based chaotic systems using the proposed adaptive filters. • The weight update algorithm and convergence characteristics for the proposed adaptive filters are derived. - Abstract: Memristor-based chaotic systems have complex dynamical behaviors, which are characterized as nonlinear and hysteresis characteristics. Modeling and identification of their nonlinear model is an important premise for analyzing the dynamical behavior of the memristor-based chaotic systems. This paper presents a novel nonlinear Wiener adaptive filtering identification approach to the memristor-based chaotic systems. The linear part of Wiener model consists of the linear transversal adaptive filters, the nonlinear part consists of nonlinear adaptive filters based on the backslash operator for the hysteresis characteristics of the memristor. The weight update algorithms for the linear and nonlinear adaptive filters are derived. Final computer simulation results show the effectiveness as well as fast convergence characteristics. Comparing with the adaptive nonlinear polynomial filters, the proposed nonlinear adaptive filters have less identification error.

Dynamic Adaptation in Child-Adult Language Interaction

Science.gov (United States)

van Dijk, Marijn; van Geert, Paul; Korecky-Kröll, Katharina; Maillochon, Isabelle; Laaha, Sabine; Dressler, Wolfgang U.; Bassano, Dominique

2013-01-01

When speaking to young children, adults adapt their language to that of the child. In this article, we suggest that this child-directed speech (CDS) is the result of a transactional process of dynamic adaptation between the child and the adult. The study compares developmental trajectories of three children to those of the CDS of their caregivers.…

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.

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

A Secure, Scalable and Elastic Autonomic Computing Systems Paradigm: Supporting Dynamic Adaptation of Self-* Services from an Autonomic Cloud

Directory of Open Access Journals (Sweden)

Abdul Jaleel

2018-05-01

Full Text Available Autonomic computing embeds self-management features in software systems using external feedback control loops, i.e., autonomic managers. In existing models of autonomic computing, adaptive behaviors are defined at the design time, autonomic managers are statically configured, and the running system has a fixed set of self-* capabilities. An autonomic computing design should accommodate autonomic capability growth by allowing the dynamic configuration of self-* services, but this causes security and integrity issues. A secure, scalable and elastic autonomic computing system (SSE-ACS paradigm is proposed to address the runtime inclusion of autonomic managers, ensuring secure communication between autonomic managers and managed resources. Applying the SSE-ACS concept, a layered approach for the dynamic adaptation of self-* services is presented with an online ‘Autonomic_Cloud’ working as the middleware between Autonomic Managers (offering the self-* services and Autonomic Computing System (requiring the self-* services. A stock trading and forecasting system is used for simulation purposes. The security impact of the SSE-ACS paradigm is verified by testing possible attack cases over the autonomic computing system with single and multiple autonomic managers running on the same and different machines. The common vulnerability scoring system (CVSS metric shows a decrease in the vulnerability severity score from high (8.8 for existing ACS to low (3.9 for SSE-ACS. Autonomic managers are introduced into the system at runtime from the Autonomic_Cloud to test the scalability and elasticity. With elastic AMs, the system optimizes the Central Processing Unit (CPU share resulting in an improved execution time for business logic. For computing systems requiring the continuous support of self-management services, the proposed system achieves a significant improvement in security, scalability, elasticity, autonomic efficiency, and issue resolving time

All-Coefficient Adaptive Control of Dual-Motor Driving Servo System

Directory of Open Access Journals (Sweden)

Zhao Haibo

2017-01-01

Full Text Available Backlash nonlinearity and friction nonlinearity exist in dual-motor driving servo system, which reducing system response speed, steady accuracy and anti-interference ability. In order to diminish the adverse effects of backlash and friction nonlinearity to system, we proposed a new all-coefficient adaptive control method. Firstly, we introduced the dynamic model of backlash and friction nonlinearity respectively. Then on this basis, we established the characteristic model when backlash and friction nonlinearity coexist. We used recursive least square method for parameter estimation. Finally we designed the all-coefficient adaptive controller. On the basis of simplex all-coefficient adaptive controller, we designed a feedforward all-coefficient adaptive controller. The simulations of feedforward all-coefficient adaptive control and simplex all-coefficient adaptive control were compared. The results show that the former has quicker response speed, higher steady accuracy, stronger anti-interference performance and better robustness, which validating the efficacy of the proposed control strategy.

Strategic tradeoffs in competitor dynamics on adaptive networks.

Science.gov (United States)

Hébert-Dufresne, Laurent; Allard, Antoine; Noël, Pierre-André; Young, Jean-Gabriel; Libby, Eric

2017-08-08

Recent empirical work highlights the heterogeneity of social competitions such as political campaigns: proponents of some ideologies seek debate and conversation, others create echo chambers. While symmetric and static network structure is typically used as a substrate to study such competitor dynamics, network structure can instead be interpreted as a signature of the competitor strategies, yielding competition dynamics on adaptive networks. Here we demonstrate that tradeoffs between aggressiveness and defensiveness (i.e., targeting adversaries vs. targeting like-minded individuals) creates paradoxical behaviour such as non-transitive dynamics. And while there is an optimal strategy in a two competitor system, three competitor systems have no such solution; the introduction of extreme strategies can easily affect the outcome of a competition, even if the extreme strategies have no chance of winning. Not only are these results reminiscent of classic paradoxical results from evolutionary game theory, but the structure of social networks created by our model can be mapped to particular forms of payoff matrices. Consequently, social structure can act as a measurable metric for social games which in turn allows us to provide a game theoretical perspective on online political debates.

Adaptive modified function projective synchronization of multiple time-delayed chaotic Rossler system

International Nuclear Information System (INIS)

Sudheer, K. Sebastian; Sabir, M.

2011-01-01

In this Letter we consider modified function projective synchronization of unidirectionally coupled multiple time-delayed Rossler chaotic systems using adaptive controls. Recently, delay differential equations have attracted much attention in the field of nonlinear dynamics. The high complexity of the multiple time-delayed systems can provide a new architecture for enhancing message security in chaos based encryption systems. Adaptive control can be used for synchronization when the parameters of the system are unknown. Based on Lyapunov stability theory, the adaptive control law and the parameter update law are derived to make the state of two chaotic systems are function projective synchronized. Numerical simulations are presented to demonstrate the effectiveness of the proposed adaptive controllers.

An Adaptive Damping Network Designed for Strapdown Fiber Optic Gyrocompass System for Ships

Directory of Open Access Journals (Sweden)

Jin Sun

2017-03-01

Full Text Available The strapdown fiber optic gyrocompass (strapdown FOGC system for ships primarily works on external horizontal damping and undamping statuses. When there are large sea condition changes, the system will switch frequently between the external horizontal damping status and the undamping status. This means that the system is always in an adjustment status and influences the dynamic accuracy of the system. Aiming at the limitations of the conventional damping method, a new design idea is proposed, where the adaptive control method is used to design the horizontal damping network of the strapdown FOGC system. According to the size of acceleration, the parameters of the damping network are changed to make the system error caused by the ship’s maneuvering to a minimum. Furthermore, the jump in damping coefficient was transformed into gradual change to make a smooth system status switch. The adaptive damping network was applied for strapdown FOGC under the static and dynamic condition, and its performance was compared with the conventional damping, and undamping means. Experimental results showed that the adaptive damping network was effective in improving the dynamic performance of the strapdown FOGC.

Towards Self-adaptation for Dependable Service-Oriented Systems

Science.gov (United States)

Cardellini, Valeria; Casalicchio, Emiliano; Grassi, Vincenzo; Lo Presti, Francesco; Mirandola, Raffaela

Increasingly complex information systems operating in dynamic environments ask for management policies able to deal intelligently and autonomously with problems and tasks. An attempt to deal with these aspects can be found in the Service-Oriented Architecture (SOA) paradigm that foresees the creation of business applications from independently developed services, where services and applications build up complex dependencies. Therefore the dependability of SOA systems strongly depends on their ability to self-manage and adapt themselves to cope with changes in the operating conditions and to meet the required dependability with a minimum of resources. In this paper we propose a model-based approach to the realization of self-adaptable SOA systems, aimed at the fulfillment of dependability requirements. Specifically, we provide a methodology driving the system adaptation and we discuss the architectural issues related to its implementation. To bring this approach to fruition, we developed a prototype tool and we show the results that can be achieved with a simple example.

Identification of chaotic memristor systems based on piecewise adaptive Legendre filters

International Nuclear Information System (INIS)

Zhao, Yibo; Zhang, Xiuzai; Xu, Jin; Guo, Yecai

2015-01-01

Memristor is a nonlinear device, which plays an important role in the design and implementation of chaotic systems. In order to be able to understand in-depth the complex nonlinear dynamic behaviors in chaotic memristor systems, modeling or identification of its nonlinear model is very important premise. This paper presents a chaotic memristor system identification method based on piecewise adaptive Legendre filters. The threshold decomposition is carried out for the input vector, and also the input signal subintervals via decomposition satisfy the convergence condition of the adaptive Legendre filters. Then the adaptive Legendre filter structure and adaptive weight update algorithm are derived. Final computer simulation results show the effectiveness as well as fast convergence characteristics.

Understanding the dynamics of the Seguro Popular de Salud policy implementation in Mexico from a complex adaptive systems perspective.

Science.gov (United States)

Nigenda, Gustavo; González-Robledo, Luz María; Juárez-Ramírez, Clara; Adam, Taghreed

2016-05-13

In 2003, Mexico's Seguro Popular de Salud (SPS), was launched as an innovative financial mechanism implemented to channel new funds to provide health insurance to 50 million Mexicans and to reduce systemic financial inequities. The objective of this article is to understand the complexity and dynamics that contributed to the adaptation of the policy in the implementation stage, how these changes occurred, and why, from a complex and adaptive systems perspective. A complex adaptive systems (CAS) framework was used to carry out a secondary analysis of data obtained from four SPS's implementation evaluations. We first identified key actors, their roles, incentives and power, and their responses to the policy and guidelines. We then developed a causal loop diagram to disentangle the feedback dynamics associated with the modifications of the policy implementation which we then analyzed using a CAS perspective. Implementation variations were identified in seven core design features during the first 10 years of implementation period, and in each case, the SPS's central coordination introduced modifications in response to the reactions of the different actors. We identified several CAS phenomena associated with these changes including phase transitions, network emergence, resistance to change, history dependence, and feedback loops. Our findings generate valuable lessons to policy implementation processes, especially those involving a monetary component, where the emergence of coping mechanisms and other CAS phenomena inevitably lead to modifications of policies and their interpretation by those who implement them. These include the difficulty of implementing strategies that aim to pool funds through solidarity among beneficiaries where the rich support the poor when there are no incentives for the rich to do so. Also, how resistance to change and history dependence can pose significant challenges to implementing changes, where the local actors use their significant power

On Sustaining Dynamic Adaptation of Context-Aware Services

Directory of Open Access Journals (Sweden)

Boudjemaa Boudaa

2015-03-01

Full Text Available The modern human is getting more and more mobile having access to online services by using mobile cutting-edge computational devices. In the last decade, the field of context-aware services had led to emerge several works. However, most of the proposed approaches have not provided clear adaptation strategies in case of unforeseen contexts. Dealing with this last at runtime is also another crucial need that has been ignored in their proposals. This paper aims to propose a generic dynamic adaptation process as a phase in a model-driven development life-cycle for context-aware services using the MAPE-K control loop to meet the runtime adaptation. This process is validated by implementing an illustrative application on FraSCAti platform. The main benefit of the proposed process is to sustain the self-reconfiguration of such services at model and code levels by enabling successive dynamic adaptations depending on the changing context.

Adaptive control of dynamic balance in human gait on a split-belt treadmill.

Science.gov (United States)

Buurke, Tom J W; Lamoth, Claudine J C; Vervoort, Danique; van der Woude, Lucas H V; den Otter, Rob

2018-05-17

Human bipedal gait is inherently unstable and staying upright requires adaptive control of dynamic balance. Little is known about adaptive control of dynamic balance in reaction to long-term, continuous perturbations. We examined how dynamic balance control adapts to a continuous perturbation in gait, by letting people walk faster with one leg than the other on a treadmill with two belts (i.e. split-belt walking). In addition, we assessed whether changes in mediolateral dynamic balance control coincide with changes in energy use during split-belt adaptation. In nine minutes of split-belt gait, mediolateral margins of stability and mediolateral foot roll-off changed during adaptation to the imposed gait asymmetry, especially on the fast side, and returned to baseline during washout. Interestingly, no changes in mediolateral foot placement (i.e. step width) were found during split-belt adaptation. Furthermore, the initial margin of stability and subsequent mediolateral foot roll-off were strongly coupled to maintain mediolateral dynamic balance throughout the gait cycle. Consistent with previous results net metabolic power was reduced during split-belt adaptation, but changes in mediolateral dynamic balance control were not correlated with the reduction of net metabolic power during split-belt adaptation. Overall, this study has shown that a complementary mechanism of relative foot positioning and mediolateral foot roll-off adapts to continuously imposed gait asymmetry to maintain dynamic balance in human bipedal gait. © 2018. Published by The Company of Biologists Ltd.

Modelling the dynamics of traits involved in fighting-predators-prey system.

Science.gov (United States)

Kooi, B W

2015-12-01

We study the dynamics of a predator-prey system where predators fight for captured prey besides searching for and handling (and digestion) of the prey. Fighting for prey is modelled by a continuous time hawk-dove game dynamics where the gain depends on the amount of disputed prey while the costs for fighting is constant per fighting event. The strategy of the predator-population is quantified by a trait being the proportion of the number of predator-individuals playing hawk tactics. The dynamics of the trait is described by two models of adaptation: the replicator dynamics (RD) and the adaptive dynamics (AD). In the RD-approach a variant individual with an adapted trait value changes the population's strategy, and consequently its trait value, only when its payoff is larger than the population average. In the AD-approach successful replacement of the resident population after invasion of a rare variant population with an adapted trait value is a step in a sequence changing the population's strategy, and hence its trait value. The main aim is to compare the consequences of the two adaptation models. In an equilibrium predator-prey system this will lead to convergence to a neutral singular strategy, while in the oscillatory system to a continuous singular strategy where in this endpoint the resident population is not invasible by any variant population. In equilibrium (low prey carrying capacity) RD and AD-approach give the same results, however not always in a periodically oscillating system (high prey carrying-capacity) where the trait is density-dependent. For low costs the predator population is monomorphic (only hawks) while for high costs dimorphic (hawks and doves). These results illustrate that intra-specific trait dynamics matters in predator-prey dynamics.

Nonlinear adaptive control system design with asymptotically stable parameter estimation error

Science.gov (United States)

Mishkov, Rumen; Darmonski, Stanislav

2018-01-01

The paper presents a new general method for nonlinear adaptive system design with asymptotic stability of the parameter estimation error. The advantages of the approach include asymptotic unknown parameter estimation without persistent excitation and capability to directly control the estimates transient response time. The method proposed modifies the basic parameter estimation dynamics designed via a known nonlinear adaptive control approach. The modification is based on the generalised prediction error, a priori constraints with a hierarchical parameter projection algorithm, and the stable data accumulation concepts. The data accumulation principle is the main tool for achieving asymptotic unknown parameter estimation. It relies on the parametric identifiability system property introduced. Necessary and sufficient conditions for exponential stability of the data accumulation dynamics are derived. The approach is applied in a nonlinear adaptive speed tracking vector control of a three-phase induction motor.

Chaos Synchronization Using Adaptive Dynamic Neural Network Controller with Variable Learning Rates

Directory of Open Access Journals (Sweden)

Chih-Hong Kao

2011-01-01

Full Text Available This paper addresses the synchronization of chaotic gyros with unknown parameters and external disturbance via an adaptive dynamic neural network control (ADNNC system. The proposed ADNNC system is composed of a neural controller and a smooth compensator. The neural controller uses a dynamic RBF (DRBF network to online approximate an ideal controller. The DRBF network can create new hidden neurons online if the input data falls outside the hidden layer and prune the insignificant hidden neurons online if the hidden neuron is inappropriate. The smooth compensator is designed to compensate for the approximation error between the neural controller and the ideal controller. Moreover, the variable learning rates of the parameter adaptation laws are derived based on a discrete-type Lyapunov function to speed up the convergence rate of the tracking error. Finally, the simulation results which verified the chaotic behavior of two nonlinear identical chaotic gyros can be synchronized using the proposed ADNNC scheme.

Behavioral and neural Darwinism: selectionist function and mechanism in adaptive behavior dynamics.

Science.gov (United States)

McDowell, J J

2010-05-01

An evolutionary theory of behavior dynamics and a theory of neuronal group selection share a common selectionist framework. The theory of behavior dynamics instantiates abstractly the idea that behavior is selected by its consequences. It implements Darwinian principles of selection, reproduction, and mutation to generate adaptive behavior in virtual organisms. The behavior generated by the theory has been shown to be quantitatively indistinguishable from that of live organisms. The theory of neuronal group selection suggests a mechanism whereby the abstract principles of the evolutionary theory may be implemented in the nervous systems of biological organisms. According to this theory, groups of neurons subserving behavior may be selected by synaptic modifications that occur when the consequences of behavior activate value systems in the brain. Together, these theories constitute a framework for a comprehensive account of adaptive behavior that extends from brain function to the behavior of whole organisms in quantitative detail. Copyright (c) 2009 Elsevier B.V. All rights reserved.

Hydrodynamics in adaptive resolution particle simulations: Multiparticle collision dynamics

Energy Technology Data Exchange (ETDEWEB)

Alekseeva, Uliana, E-mail: Alekseeva@itc.rwth-aachen.de [Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation (IAS), Forschungszentrum Jülich, D-52425 Jülich (Germany); German Research School for Simulation Sciences (GRS), Forschungszentrum Jülich, D-52425 Jülich (Germany); Winkler, Roland G., E-mail: r.winkler@fz-juelich.de [Theoretical Soft Matter and Biophysics, Institute for Advanced Simulation (IAS), Forschungszentrum Jülich, D-52425 Jülich (Germany); Sutmann, Godehard, E-mail: g.sutmann@fz-juelich.de [Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation (IAS), Forschungszentrum Jülich, D-52425 Jülich (Germany); ICAMS, Ruhr-University Bochum, D-44801 Bochum (Germany)

2016-06-01

A new adaptive resolution technique for particle-based multi-level simulations of fluids is presented. In the approach, the representation of fluid and solvent particles is changed on the fly between an atomistic and a coarse-grained description. The present approach is based on a hybrid coupling of the multiparticle collision dynamics (MPC) method and molecular dynamics (MD), thereby coupling stochastic and deterministic particle-based methods. Hydrodynamics is examined by calculating velocity and current correlation functions for various mixed and coupled systems. We demonstrate that hydrodynamic properties of the mixed fluid are conserved by a suitable coupling of the two particle methods, and that the simulation results agree well with theoretical expectations.

Computational identification of adaptive mutants using the VERT system

Directory of Open Access Journals (Sweden)

Winkler James

2012-04-01

Full Text Available Background Evolutionary dynamics of microbial organisms can now be visualized using the Visualizing Evolution in Real Time (VERT system, in which several isogenic strains expressing different fluorescent proteins compete during adaptive evolution and are tracked using fluorescent cell sorting to construct a population history over time. Mutations conferring enhanced growth rates can be detected by observing changes in the fluorescent population proportions. Results Using data obtained from several VERT experiments, we construct a hidden Markov-derived model to detect these adaptive events in VERT experiments without external intervention beyond initial training. Analysis of annotated data revealed that the model achieves consensus with human annotation for 85-93% of the data points when detecting adaptive events. A method to determine the optimal time point to isolate adaptive mutants is also introduced. Conclusions The developed model offers a new way to monitor adaptive evolution experiments without the need for external intervention, thereby simplifying adaptive evolution efforts relying on population tracking. Future efforts to construct a fully automated system to isolate adaptive mutants may find the algorithm a useful tool.

On dynamical systems approaches and methods in f ( R ) cosmology

Energy Technology Data Exchange (ETDEWEB)

Alho, Artur [Center for Mathematical Analysis, Geometry and Dynamical Systems, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisboa (Portugal); Carloni, Sante [Centro Multidisciplinar de Astrofisica – CENTRA, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisboa (Portugal); Uggla, Claes, E-mail: aalho@math.ist.utl.pt, E-mail: sante.carloni@tecnico.ulisboa.pt, E-mail: claes.uggla@kau.se [Department of Physics, Karlstad University, S-65188 Karlstad (Sweden)

2016-08-01

We discuss dynamical systems approaches and methods applied to flat Robertson-Walker models in f ( R )-gravity. We argue that a complete description of the solution space of a model requires a global state space analysis that motivates globally covering state space adapted variables. This is shown explicitly by an illustrative example, f ( R ) = R + α R {sup 2}, α > 0, for which we introduce new regular dynamical systems on global compactly extended state spaces for the Jordan and Einstein frames. This example also allows us to illustrate several local and global dynamical systems techniques involving, e.g., blow ups of nilpotent fixed points, center manifold analysis, averaging, and use of monotone functions. As a result of applying dynamical systems methods to globally state space adapted dynamical systems formulations, we obtain pictures of the entire solution spaces in both the Jordan and the Einstein frames. This shows, e.g., that due to the domain of the conformal transformation between the Jordan and Einstein frames, not all the solutions in the Jordan frame are completely contained in the Einstein frame. We also make comparisons with previous dynamical systems approaches to f ( R ) cosmology and discuss their advantages and disadvantages.

Diagonal recurrent neural network based adaptive control of nonlinear dynamical systems using lyapunov stability criterion.

Science.gov (United States)

Kumar, Rajesh; Srivastava, Smriti; Gupta, J R P

2017-03-01

In this paper adaptive control of nonlinear dynamical systems using diagonal recurrent neural network (DRNN) is proposed. The structure of DRNN is a modification of fully connected recurrent neural network (FCRNN). Presence of self-recurrent neurons in the hidden layer of DRNN gives it an ability to capture the dynamic behaviour of the nonlinear plant under consideration (to be controlled). To ensure stability, update rules are developed using lyapunov stability criterion. These rules are then used for adjusting the various parameters of DRNN. The responses of plants obtained with DRNN are compared with those obtained when multi-layer feed forward neural network (MLFFNN) is used as a controller. Also, in example 4, FCRNN is also investigated and compared with DRNN and MLFFNN. Robustness of the proposed control scheme is also tested against parameter variations and disturbance signals. Four simulation examples including one-link robotic manipulator and inverted pendulum are considered on which the proposed controller is applied. The results so obtained show the superiority of DRNN over MLFFNN as a controller. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Synchronization of a modified Chua's circuit system via adaptive sliding mode control

International Nuclear Information System (INIS)

Yan, J.-J.; Lin, J.-S.; Liao, T.-L.

2008-01-01

This study addresses the adaptive synchronization of a modified Chua's circuit system with both unknown system parameters and the nonlinearity in the control input. An adaptive switching surface is newly adopted such that it becomes easy to ensure the stability of the error dynamics in the sliding mode. Based on this adaptive switching surface, an adaptive sliding mode controller (ASMC) is derived to guarantee the occurrence of the sliding motion, even when the system is undergoing input nonlinearity. This method can also be easily extended to a general class of Chua's circuits. An illustrative example is given to show the applicability of the proposed ASMC design

Robust adaptive synchronization of general dynamical networks ...

Indian Academy of Sciences (India)

Robust adaptive synchronization; dynamical network; multiple delays; multiple uncertainties. ... Networks such as neural networks, communication transmission networks, social rela- tionship networks etc. ..... a very good effect. Pramana – J.

Neural network-based adaptive dynamic surface control for permanent magnet synchronous motors.

Science.gov (United States)

Yu, Jinpeng; Shi, Peng; Dong, Wenjie; Chen, Bing; Lin, Chong

2015-03-01

This brief considers the problem of neural networks (NNs)-based adaptive dynamic surface control (DSC) for permanent magnet synchronous motors (PMSMs) with parameter uncertainties and load torque disturbance. First, NNs are used to approximate the unknown and nonlinear functions of PMSM drive system and a novel adaptive DSC is constructed to avoid the explosion of complexity in the backstepping design. Next, under the proposed adaptive neural DSC, the number of adaptive parameters required is reduced to only one, and the designed neural controllers structure is much simpler than some existing results in literature, which can guarantee that the tracking error converges to a small neighborhood of the origin. Then, simulations are given to illustrate the effectiveness and potential of the new design technique.

Online Dynamic Balance Technology for High Speed Spindle Based on Gain Parameter Adaption and Scheduling Control

Directory of Open Access Journals (Sweden)

Shihai Zhang

2018-06-01

Full Text Available Unbalance vibration is one of the main vibration forms of a high speed machine tool spindle. The overlarge unbalance vibration will have some adverse effects on the working life of the spindle system and the surface quality of the work-piece. In order to reduce the unbalance of a high speed spindle system, a pneumatic online dynamic balance device and its control system are presented in the paper. To improve the balance accuracy and adaptation of the balance system, the gain parameter adaption and scheduling control method are proposed first, and then the different balance effects of the influence coefficient method and the gain scheduling control method are compared through many dynamic balance experiments of the high speed spindle. The experimental results indicate that the gain parameters can be changed timely according to the transformation of the speed and kinetic parameters of the spindle system. The balance accuracy can be improved for a high speed spindle with time-varying characteristics, based on the adaptive gain scheduling control method.

Mathematical model for adaptive control system of ASEA robot at Kennedy Space Center

Science.gov (United States)

Zia, Omar

1989-01-01

The dynamic properties and the mathematical model for the adaptive control of the robotic system presently under investigation at Robotic Application and Development Laboratory at Kennedy Space Center are discussed. NASA is currently investigating the use of robotic manipulators for mating and demating of fuel lines to the Space Shuttle Vehicle prior to launch. The Robotic system used as a testbed for this purpose is an ASEA IRB-90 industrial robot with adaptive control capabilities. The system was tested and it's performance with respect to stability was improved by using an analogue force controller. The objective of this research project is to determine the mathematical model of the system operating under force feedback control with varying dynamic internal perturbation in order to provide continuous stable operation under variable load conditions. A series of lumped parameter models are developed. The models include some effects of robot structural dynamics, sensor compliance, and workpiece dynamics.

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 single-rate context-dependent learning process underlies rapid adaptation to familiar object dynamics.

Science.gov (United States)

Ingram, James N; Howard, Ian S; Flanagan, J Randall; Wolpert, Daniel M

2011-09-01

Motor learning has been extensively studied using dynamic (force-field) perturbations. These induce movement errors that result in adaptive changes to the motor commands. Several state-space models have been developed to explain how trial-by-trial errors drive the progressive adaptation observed in such studies. These models have been applied to adaptation involving novel dynamics, which typically occurs over tens to hundreds of trials, and which appears to be mediated by a dual-rate adaptation process. In contrast, when manipulating objects with familiar dynamics, subjects adapt rapidly within a few trials. Here, we apply state-space models to familiar dynamics, asking whether adaptation is mediated by a single-rate or dual-rate process. Previously, we reported a task in which subjects rotate an object with known dynamics. By presenting the object at different visual orientations, adaptation was shown to be context-specific, with limited generalization to novel orientations. Here we show that a multiple-context state-space model, with a generalization function tuned to visual object orientation, can reproduce the time-course of adaptation and de-adaptation as well as the observed context-dependent behavior. In contrast to the dual-rate process associated with novel dynamics, we show that a single-rate process mediates adaptation to familiar object dynamics. The model predicts that during exposure to the object across multiple orientations, there will be a degree of independence for adaptation and de-adaptation within each context, and that the states associated with all contexts will slowly de-adapt during exposure in one particular context. We confirm these predictions in two new experiments. Results of the current study thus highlight similarities and differences in the processes engaged during exposure to novel versus familiar dynamics. In both cases, adaptation is mediated by multiple context-specific representations. In the case of familiar object dynamics

A single-rate context-dependent learning process underlies rapid adaptation to familiar object dynamics.

Directory of Open Access Journals (Sweden)

James N Ingram

2011-09-01

Full Text Available Motor learning has been extensively studied using dynamic (force-field perturbations. These induce movement errors that result in adaptive changes to the motor commands. Several state-space models have been developed to explain how trial-by-trial errors drive the progressive adaptation observed in such studies. These models have been applied to adaptation involving novel dynamics, which typically occurs over tens to hundreds of trials, and which appears to be mediated by a dual-rate adaptation process. In contrast, when manipulating objects with familiar dynamics, subjects adapt rapidly within a few trials. Here, we apply state-space models to familiar dynamics, asking whether adaptation is mediated by a single-rate or dual-rate process. Previously, we reported a task in which subjects rotate an object with known dynamics. By presenting the object at different visual orientations, adaptation was shown to be context-specific, with limited generalization to novel orientations. Here we show that a multiple-context state-space model, with a generalization function tuned to visual object orientation, can reproduce the time-course of adaptation and de-adaptation as well as the observed context-dependent behavior. In contrast to the dual-rate process associated with novel dynamics, we show that a single-rate process mediates adaptation to familiar object dynamics. The model predicts that during exposure to the object across multiple orientations, there will be a degree of independence for adaptation and de-adaptation within each context, and that the states associated with all contexts will slowly de-adapt during exposure in one particular context. We confirm these predictions in two new experiments. Results of the current study thus highlight similarities and differences in the processes engaged during exposure to novel versus familiar dynamics. In both cases, adaptation is mediated by multiple context-specific representations. In the case of familiar

Dynamic adjustments of cognitive control: oscillatory correlates of the conflict adaptation effect.

Science.gov (United States)

Pastötter, Bernhard; Dreisbach, Gesine; Bäuml, Karl-Heinz T

2013-12-01

It is a prominent idea that cognitive control mediates conflict adaptation, in that response conflict in a previous trial triggers control adjustments that reduce conflict in a current trial. In the present EEG study, we investigated the dynamics of cognitive control in a response-priming task by examining the effects of previous trial conflict on intertrial and current trial oscillatory brain activities, both on the electrode and the source level. Behavioral results showed conflict adaptation effects for RTs and response accuracy. Physiological results showed sustained intertrial effects in left parietal theta power, originating in the left inferior parietal cortex, and midcentral beta power, originating in the left and right (pre)motor cortex. Moreover, physiological analysis revealed a current trial conflict adaptation effect in midfrontal theta power, originating in the ACC. Correlational analyses showed that intertrial effects predicted conflict-induced midfrontal theta power in currently incongruent trials. In addition, conflict adaptation effects in midfrontal theta power and RTs were positively related. Together, these findings point to a dynamic cognitive control system that, as a function of previous trial type, up- and down-regulates attention and preparatory motor activities in anticipation of the next trial.

Adaptive Control of Nonlinear Discrete-Time Systems by Using OS-ELM Neural Networks

Directory of Open Access Journals (Sweden)

Xiao-Li Li

2014-01-01

Full Text Available As a kind of novel feedforward neural network with single hidden layer, ELM (extreme learning machine neural networks are studied for the identification and control of nonlinear dynamic systems. The property of simple structure and fast convergence of ELM can be shown clearly. In this paper, we are interested in adaptive control of nonlinear dynamic plants by using OS-ELM (online sequential extreme learning machine neural networks. Based on data scope division, the problem that training process of ELM neural network is sensitive to the initial training data is also solved. According to the output range of the controlled plant, the data corresponding to this range will be used to initialize ELM. Furthermore, due to the drawback of conventional adaptive control, when the OS-ELM neural network is used for adaptive control of the system with jumping parameters, the topological structure of the neural network can be adjusted dynamically by using multiple model switching strategy, and an MMAC (multiple model adaptive control will be used to improve the control performance. Simulation results are included to complement the theoretical results.

Optimal spectral tracking--adapting to dynamic regime change.

Science.gov (United States)

Brittain, John-Stuart; Halliday, David M

2011-01-30

Real world data do not always obey the statistical restraints imposed upon them by sophisticated analysis techniques. In spectral analysis for instance, an ergodic process--the interchangeability of temporal for spatial averaging--is assumed for a repeat-trial design. Many evolutionary scenarios, such as learning and motor consolidation, do not conform to such linear behaviour and should be approached from a more flexible perspective. To this end we previously introduced the method of optimal spectral tracking (OST) in the study of trial-varying parameters. In this extension to our work we modify the OST routines to provide an adaptive implementation capable of reacting to dynamic transitions in the underlying system state. In so doing, we generalise our approach to characterise both slow-varying and rapid fluctuations in time-series, simultaneously providing a metric of system stability. The approach is first applied to a surrogate dataset and compared to both our original non-adaptive solution and spectrogram approaches. The adaptive OST is seen to display fast convergence and desirable statistical properties. All three approaches are then applied to a neurophysiological recording obtained during a study on anaesthetic monitoring. Local field potentials acquired from the posterior hypothalamic region of a deep brain stimulation patient undergoing anaesthesia were analysed. The characterisation of features such as response delay, time-to-peak and modulation brevity are considered. Copyright © 2010 Elsevier B.V. All rights reserved.

Towards Static Analysis of Policy-Based Self-adaptive Computing Systems

DEFF Research Database (Denmark)

Margheri, Andrea; Nielson, Hanne Riis; Nielson, Flemming

2016-01-01

For supporting the design of self-adaptive computing systems, the PSCEL language offers a principled approach that relies on declarative definitions of adaptation and authorisation policies enforced at runtime. Policies permit managing system components by regulating their interactions...... and by dynamically introducing new actions to accomplish task-oriented goals. However, the runtime evaluation of policies and their effects on system components make the prediction of system behaviour challenging. In this paper, we introduce the construction of a flow graph that statically points out the policy...... evaluations that can take place at runtime and exploit it to analyse the effects of policy evaluations on the progress of system components....

Dynamical Adaptation in Terrorist Cells/Networks

DEFF Research Database (Denmark)

Hussain, Dil Muhammad Akbar; Ahmed, Zaki

2010-01-01

Typical terrorist cells/networks have dynamical structure as they evolve or adapt to changes which may occur due to capturing or killing of a member of the cell/network. Analytical measures in graph theory like degree centrality, betweenness and closeness centralities are very common and have long...

Improving the Adaptability of Simulated Evolutionary Swarm Robots in Dynamically Changing Environments

Science.gov (United States)

Yao, Yao; Marchal, Kathleen; Van de Peer, Yves

2014-01-01

One of the important challenges in the field of evolutionary robotics is the development of systems that can adapt to a changing environment. However, the ability to adapt to unknown and fluctuating environments is not straightforward. Here, we explore the adaptive potential of simulated swarm robots that contain a genomic encoding of a bio-inspired gene regulatory network (GRN). An artificial genome is combined with a flexible agent-based system, representing the activated part of the regulatory network that transduces environmental cues into phenotypic behaviour. Using an artificial life simulation framework that mimics a dynamically changing environment, we show that separating the static from the conditionally active part of the network contributes to a better adaptive behaviour. Furthermore, in contrast with most hitherto developed ANN-based systems that need to re-optimize their complete controller network from scratch each time they are subjected to novel conditions, our system uses its genome to store GRNs whose performance was optimized under a particular environmental condition for a sufficiently long time. When subjected to a new environment, the previous condition-specific GRN might become inactivated, but remains present. This ability to store ‘good behaviour’ and to disconnect it from the novel rewiring that is essential under a new condition allows faster re-adaptation if any of the previously observed environmental conditions is reencountered. As we show here, applying these evolutionary-based principles leads to accelerated and improved adaptive evolution in a non-stable environment. PMID:24599485

Improving the adaptability of simulated evolutionary swarm robots in dynamically changing environments.

Directory of Open Access Journals (Sweden)

Yao Yao

Full Text Available One of the important challenges in the field of evolutionary robotics is the development of systems that can adapt to a changing environment. However, the ability to adapt to unknown and fluctuating environments is not straightforward. Here, we explore the adaptive potential of simulated swarm robots that contain a genomic encoding of a bio-inspired gene regulatory network (GRN. An artificial genome is combined with a flexible agent-based system, representing the activated part of the regulatory network that transduces environmental cues into phenotypic behaviour. Using an artificial life simulation framework that mimics a dynamically changing environment, we show that separating the static from the conditionally active part of the network contributes to a better adaptive behaviour. Furthermore, in contrast with most hitherto developed ANN-based systems that need to re-optimize their complete controller network from scratch each time they are subjected to novel conditions, our system uses its genome to store GRNs whose performance was optimized under a particular environmental condition for a sufficiently long time. When subjected to a new environment, the previous condition-specific GRN might become inactivated, but remains present. This ability to store 'good behaviour' and to disconnect it from the novel rewiring that is essential under a new condition allows faster re-adaptation if any of the previously observed environmental conditions is reencountered. As we show here, applying these evolutionary-based principles leads to accelerated and improved adaptive evolution in a non-stable environment.

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.

Adaptive fuzzy sliding-mode control for multi-input multi-output chaotic systems

International Nuclear Information System (INIS)

Poursamad, Amir; Markazi, Amir H.D.

2009-01-01

This paper describes an adaptive fuzzy sliding-mode control algorithm for controlling unknown or uncertain, multi-input multi-output (MIMO), possibly chaotic, dynamical systems. The control approach encompasses a fuzzy system and a robust controller. The fuzzy system is designed to mimic an ideal sliding-mode controller, and the robust controller compensates the difference between the fuzzy controller and the ideal one. The parameters of the fuzzy system, as well as the uncertainty bound of the robust controller, are tuned adaptively. The adaptive laws are derived in the Lyapunov sense to guarantee the asymptotic stability and tracking of the controlled system. The effectiveness of the proposed method is shown by applying it to some well-known chaotic systems.

Water Governance and Adaptation to Disturbances in Irrigated Semi-Arid Agricultural Systems

Science.gov (United States)

Evans, T. P.; McCord, P. F.; McBride, L.; Gower, D.; Caylor, K. K.

2013-12-01

Climate and other physical drivers of environmental systems are modifying the global availability of water for irrigation. At the same time population growth is placing an increased demand on water resources as local municipalities promote agricultural production as a mechanism to support human welfare and development. Substantial has research focused on household-level agricultural decision-making and adaptation. But equally important are institutional dynamics, or the rules implemented to allocate water resources across different user groups. Previous work has identified design principles for common-pool resource systems that tend to lead to sustained governance regimes. Likewise, past research has addressed the issue of "institutional fit", or locally adapted governance arrangements characterized through governance structure. However, much of the complexity behind institutional dynamics and adaptive capacity lies in the translation of data to information to knowledge, and how this sequence contributes to effective cross-scale water management and decision-making - an arena that has arguably received less attention in the water management literature. We investigate the interplay between governance regimes, data/information and institutional dynamics in irrigation systems in semi-arid regions of Kenya. In particular, we articulate the role of knowledge and data in institutional dynamics at multiple levels of analysis. How do users at different decision-making levels incorporate social and hydrological information in water governance? What data is needed to develop the information and knowledge users need for effective management? While governance structure is certainly a critical component of water management systems - we emphasize the interplay between the data-information-knowledge sequence and institutional dynamics. We present findings from household and manager-level surveys examining irrigation practices and the institutions designed to equitably allocate

Adaptive control of dynamical synchronization on evolving networks with noise disturbances

Science.gov (United States)

Yuan, Wu-Jie; Zhou, Jian-Fang; Sendiña-Nadal, Irene; Boccaletti, Stefano; Wang, Zhen

2018-02-01

In real-world networked systems, the underlying structure is often affected by external and internal unforeseen factors, making its evolution typically inaccessible. An adaptive strategy was introduced for maintaining synchronization on unpredictably evolving networks [Sorrentino and Ott, Phys. Rev. Lett. 100, 114101 (2008), 10.1103/PhysRevLett.100.114101], which yet does not consider the noise disturbances widely existing in networks' environments. We provide here strategies to control dynamical synchronization on slowly and unpredictably evolving networks subjected to noise disturbances which are observed at the node and at the communication channel level. With our strategy, the nodes' coupling strength is adaptively adjusted with the aim of controlling synchronization, and according only to their received signal and noise disturbances. We first provide a theoretical analysis of the control scheme by introducing an error potential function to seek for the minimization of the synchronization error. Then, we show numerical experiments which verify our theoretical results. In particular, it is found that our adaptive strategy is effective even for the case in which the dynamics of the uncontrolled network would be explosive (i.e., the states of all the nodes would diverge to infinity).

Adaptation dynamics of the quasispecies model

Indian Academy of Sciences (India)

We study the adaptation dynamics of an initially maladapted population evolving via the elementary processes of mutation and selection. The evolution occurs on rugged fitness landscapes which are defined on the multi-dimensional genotypic space and have many local peaks separated by low fitness valleys. We mainly ...

Organization of an optimal adaptive immune system

Science.gov (United States)

Walczak, Aleksandra; Mayer, Andreas; Balasubramanian, Vijay; Mora, Thierry

The repertoire of lymphocyte receptors in the adaptive immune system protects organisms from a diverse set of pathogens. A well-adapted repertoire should be tuned to the pathogenic environment to reduce the cost of infections. I will discuss a general framework for predicting the optimal repertoire that minimizes the cost of infections contracted from a given distribution of pathogens. The theory predicts that the immune system will have more receptors for rare antigens than expected from the frequency of encounters and individuals exposed to the same infections will have sparse repertoires that are largely different, but nevertheless exploit cross-reactivity to provide the same coverage of antigens. I will show that the optimal repertoires can be reached by dynamics that describes the competitive binding of antigens by receptors, and selective amplification of stimulated receptors.

Verification and Validation Challenges for Adaptive Flight Control of Complex Autonomous Systems

Science.gov (United States)

Nguyen, Nhan T.

2018-01-01

Autonomy of aerospace systems requires the ability for flight control systems to be able to adapt to complex uncertain dynamic environment. In spite of the five decades of research in adaptive control, the fact still remains that currently no adaptive control system has ever been deployed on any safety-critical or human-rated production systems such as passenger transport aircraft. The problem lies in the difficulty with the certification of adaptive control systems since existing certification methods cannot readily be used for nonlinear adaptive control systems. Research to address the notion of metrics for adaptive control began to appear in the recent years. These metrics, if accepted, could pave a path towards certification that would potentially lead to the adoption of adaptive control as a future control technology for safety-critical and human-rated production systems. Development of certifiable adaptive control systems represents a major challenge to overcome. Adaptive control systems with learning algorithms will never become part of the future unless it can be proven that they are highly safe and reliable. Rigorous methods for adaptive control software verification and validation must therefore be developed to ensure that adaptive control system software failures will not occur, to verify that the adaptive control system functions as required, to eliminate unintended functionality, and to demonstrate that certification requirements imposed by regulatory bodies such as the Federal Aviation Administration (FAA) can be satisfied. This presentation will discuss some of the technical issues with adaptive flight control and related V&V challenges.

Supervision and prognosis architecture based on dynamical classification method for the predictive maintenance of dynamical evolving systems

International Nuclear Information System (INIS)

Traore, M.; Chammas, A.; Duviella, E.

2015-01-01

In this paper, we are concerned by the improvement of the safety, availability and reliability of dynamical systems’ components subjected to slow degradations (slow drifts). We propose an architecture for efficient Predictive Maintenance (PM) according to the real time estimate of the future state of the components. The architecture is built on supervision and prognosis tools. The prognosis method is based on an appropriated supervision technique that consists in drift tracking of the dynamical systems using AUDyC (AUto-adaptive and Dynamical Clustering), that is an auto-adaptive dynamical classifier. Thus, due to the complexity and the dynamical of the considered systems, the Failure Mode Effect and Criticity Analysis (FMECA) is used to identify the key components of the systems. A component is defined as an element of the system that can be impacted by only one failure. A failure of a key component causes a long downtime of the system. From the FMECA, a Fault Tree Analysis (FTA) of the system are built to determine the propagation laws of a failure on the system by using a deductive method. The proposed architecture is implemented for the PM of a thermoregulator. The application on this real system highlights the interests and the performances of the proposed architecture

Brain-wide neuronal dynamics during motor adaptation in zebrafish.

Science.gov (United States)

Ahrens, Misha B; Li, Jennifer M; Orger, Michael B; Robson, Drew N; Schier, Alexander F; Engert, Florian; Portugues, Ruben

2012-05-09

A fundamental question in neuroscience is how entire neural circuits generate behaviour and adapt it to changes in sensory feedback. Here we use two-photon calcium imaging to record the activity of large populations of neurons at the cellular level, throughout the brain of larval zebrafish expressing a genetically encoded calcium sensor, while the paralysed animals interact fictively with a virtual environment and rapidly adapt their motor output to changes in visual feedback. We decompose the network dynamics involved in adaptive locomotion into four types of neuronal response properties, and provide anatomical maps of the corresponding sites. A subset of these signals occurred during behavioural adjustments and are candidates for the functional elements that drive motor learning. Lesions to the inferior olive indicate a specific functional role for olivocerebellar circuitry in adaptive locomotion. This study enables the analysis of brain-wide dynamics at single-cell resolution during behaviour.

The Use of Complex Adaptive Systems as a Generative Metaphor in an Action Research Study of an Organisation

Science.gov (United States)

Brown, Callum

2008-01-01

Understanding the dynamic behaviour of organisations is challenging and this study uses a model of complex adaptive systems as a generative metaphor to address this challenge. The research question addressed is: How might a conceptual model of complex adaptive systems be used to assist in understanding the dynamic nature of organisations? Using an…

Understanding global health governance as a complex adaptive system.

Science.gov (United States)

Hill, Peter S

2011-01-01

The transition from international to global health reflects the rapid growth in the numbers and nature of stakeholders in health, as well as the constant change embodied in the process of globalisation itself. This paper argues that global health governance shares the characteristics of complex adaptive systems, with its multiple and diverse players, and their polyvalent and constantly evolving relationships, and rich and dynamic interactions. The sheer quantum of initiatives, the multiple networks through which stakeholders (re)configure their influence, the range of contexts in which development for health is played out - all compound the complexity of this system. This paper maps out the characteristics of complex adaptive systems as they apply to global health governance, linking them to developments in the past two decades, and the multiple responses to these changes. Examining global health governance through the frame of complexity theory offers insight into the current dynamics of governance, and while providing a framework for making meaning of the whole, opens up ways of accessing this complexity through local points of engagement.

Facial Expression Aftereffect Revealed by Adaption to Emotion-Invisible Dynamic Bubbled Faces

Science.gov (United States)

Luo, Chengwen; Wang, Qingyun; Schyns, Philippe G.; Kingdom, Frederick A. A.; Xu, Hong

2015-01-01

Visual adaptation is a powerful tool to probe the short-term plasticity of the visual system. Adapting to local features such as the oriented lines can distort our judgment of subsequently presented lines, the tilt aftereffect. The tilt aftereffect is believed to be processed at the low-level of the visual cortex, such as V1. Adaptation to faces, on the other hand, can produce significant aftereffects in high-level traits such as identity, expression, and ethnicity. However, whether face adaptation necessitate awareness of face features is debatable. In the current study, we investigated whether facial expression aftereffects (FEAE) can be generated by partially visible faces. We first generated partially visible faces using the bubbles technique, in which the face was seen through randomly positioned circular apertures, and selected the bubbled faces for which the subjects were unable to identify happy or sad expressions. When the subjects adapted to static displays of these partial faces, no significant FEAE was found. However, when the subjects adapted to a dynamic video display of a series of different partial faces, a significant FEAE was observed. In both conditions, subjects could not identify facial expression in the individual adapting faces. These results suggest that our visual system is able to integrate unrecognizable partial faces over a short period of time and that the integrated percept affects our judgment on subsequently presented faces. We conclude that FEAE can be generated by partial face with little facial expression cues, implying that our cognitive system fills-in the missing parts during adaptation, or the subcortical structures are activated by the bubbled faces without conscious recognition of emotion during adaptation. PMID:26717572

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.

Adaptive Lighting

DEFF Research Database (Denmark)

Petersen, Kjell Yngve; Søndergaard, Karin; Kongshaug, Jesper

2015-01-01

Adaptive Lighting Adaptive lighting is based on a partial automation of the possibilities to adjust the colour tone and brightness levels of light in order to adapt to people’s needs and desires. IT support is key to the technical developments that afford adaptive control systems. The possibilities...... offered by adaptive lighting control are created by the ways that the system components, the network and data flow can be coordinated through software so that the dynamic variations are controlled in ways that meaningfully adapt according to people’s situations and design intentions. This book discusses...... differently into an architectural body. We also examine what might occur when light is dynamic and able to change colour, intensity and direction, and when it is adaptive and can be brought into interaction with its surroundings. In short, what happens to an architectural space when artificial lighting ceases...

Adaptive control of chaotic systems with stochastic time varying unknown parameters

Energy Technology Data Exchange (ETDEWEB)

Salarieh, Hassan [Center of Excellence in Design, Robotics and Automation, Department of Mechanical Engineering, Sharif University of Technology, P.O. Box 11365-9567, Azadi Avenue, Tehran (Iran, Islamic Republic of)], E-mail: salarieh@mech.sharif.edu; Alasty, Aria [Center of Excellence in Design, Robotics and Automation, Department of Mechanical Engineering, Sharif University of Technology, P.O. Box 11365-9567, Azadi Avenue, Tehran (Iran, Islamic Republic of)], E-mail: aalasti@sharif.edu

2008-10-15

In this paper based on the Lyapunov stability theorem, an adaptive control scheme is proposed for stabilizing the unstable periodic orbits (UPO) of chaotic systems. It is assumed that the chaotic system has some linearly dependent unknown parameters which are stochastically time varying. The stochastic parameters are modeled through the Weiner process derivative. To demonstrate the effectiveness of the proposed technique it has been applied to the Lorenz, Chen and Rossler dynamical systems, as some case studies. Simulation results indicate that the proposed adaptive controller has a high performance in stabilizing the UPO of chaotic systems in noisy environment.

Adaptive Sliding Control for a Class of Fractional Commensurate Order Chaotic Systems

Directory of Open Access Journals (Sweden)

Jian Yuan

2015-01-01

Full Text Available This paper proposes adaptive sliding mode control design for a class of fractional commensurate order chaotic systems. We firstly introduce a fractional integral sliding manifold for the nominal systems. Secondly we prove the stability of the corresponding fractional sliding dynamics. Then, by introducing a Lyapunov candidate function and using the Mittag-Leffler stability theory we derive the desired sliding control law. Furthermore, we prove that the proposed sliding manifold is also adapted for the fractional systems in the presence of uncertainties and external disturbances. At last, we design a fractional adaptation law for the perturbed fractional systems. To verify the viability and efficiency of the proposed fractional controllers, numerical simulations of fractional Lorenz’s system and Chen’s system are presented.

Force adaptation transfers to untrained workspace regions in children: evidence for developing inverse dynamic motor models.

Science.gov (United States)

Jansen-Osmann, Petra; Richter, Stefanie; Konczak, Jürgen; Kalveram, Karl-Theodor

2002-03-01

When humans perform goal-directed arm movements under the influence of an external damping force, they learn to adapt to these external dynamics. After removal of the external force field, they reveal kinematic aftereffects that are indicative of a neural controller that still compensates the no longer existing force. Such behavior suggests that the adult human nervous system uses a neural representation of inverse arm dynamics to control upper-extremity motion. Central to the notion of an inverse dynamic model (IDM) is that learning generalizes. Consequently, aftereffects should be observable even in untrained workspace regions. Adults have shown such behavior, but the ontogenetic development of this process remains unclear. This study examines the adaptive behavior of children and investigates whether learning a force field in one hemifield of the right arm workspace has an effect on force adaptation in the other hemifield. Thirty children (aged 6-10 years) and ten adults performed 30 degrees elbow flexion movements under two conditions of external damping (negative and null). We found that learning to compensate an external damping force transferred to the opposite hemifield, which indicates that a model of the limb dynamics rather than an association of visited space and experienced force was acquired. Aftereffects were more pronounced in the younger children and readaptation to a null-force condition was prolonged. This finding is consistent with the view that IDMs in children are imprecise neural representations of the actual arm dynamics. It indicates that the acquisition of IDMs is a developmental achievement and that the human motor system is inherently flexible enough to adapt to any novel force within the limits of the organism's biomechanics.

Simulated climate adaptation in storm-water systems: Evaluating the efficiency of within-system flexibility

Directory of Open Access Journals (Sweden)

Adam D. McCurdy

Full Text Available Changes in regional temperature and precipitation patterns resulting from global climate change may adversely affect the performance of long-lived infrastructure. Adaptation may be necessary to ensure that infrastructure offers consistent service and remains cost effective. But long service times and deep uncertainty associated with future climate projections make adaptation decisions especially challenging for managers. Incorporating flexibility into systems can increase their effectiveness across different climate futures but can also add significant costs. In this paper we review existing work on flexibility in climate change adaptation of infrastructure, such as robust decision-making and dynamic adaptive pathways, apply a basic typology of flexibility, and test alternative strategies for flexibility in distributed infrastructure systems comprised of multiple emplacements of a common, long-lived element: roadway culverts. Rather than treating a system of dispersed infrastructure elements as monolithic, we simulate “options flexibility” in which inherent differences in individual elements is incorporated into adaptation decisions. We use a virtual testbed of highway drainage crossing structures to examine the performance under different climate scenarios of policies that allow for multiple adaptation strategies with varying timing based on individual emplacement characteristics. Results indicate that a strategy with options flexibility informed by crossing characteristics offers a more efficient method of adaptation than do monolithic policies. In some cases this results in more cost-effective adaptation for agencies building long-lived, climate-sensitive infrastructure, even where detailed system data and analytical capacity is limited. Keywords: Climate adaptation, Stormwater management, Adaptation pathways

Wavelet and adaptive methods for time dependent problems and applications in aerosol dynamics

Science.gov (United States)

Guo, Qiang

Time dependent partial differential equations (PDEs) are widely used as mathematical models of environmental problems. Aerosols are now clearly identified as an important factor in many environmental aspects of climate and radiative forcing processes, as well as in the health effects of air quality. The mathematical models for the aerosol dynamics with respect to size distribution are nonlinear partial differential and integral equations, which describe processes of condensation, coagulation and deposition. Simulating the general aerosol dynamic equations on time, particle size and space exhibits serious difficulties because the size dimension ranges from a few nanometer to several micrometer while the spatial dimension is usually described with kilometers. Therefore, it is an important and challenging task to develop efficient techniques for solving time dependent dynamic equations. In this thesis, we develop and analyze efficient wavelet and adaptive methods for the time dependent dynamic equations on particle size and further apply them to the spatial aerosol dynamic systems. Wavelet Galerkin method is proposed to solve the aerosol dynamic equations on time and particle size due to the fact that aerosol distribution changes strongly along size direction and the wavelet technique can solve it very efficiently. Daubechies' wavelets are considered in the study due to the fact that they possess useful properties like orthogonality, compact support, exact representation of polynomials to a certain degree. Another problem encountered in the solution of the aerosol dynamic equations results from the hyperbolic form due to the condensation growth term. We propose a new characteristic-based fully adaptive multiresolution numerical scheme for solving the aerosol dynamic equation, which combines the attractive advantages of adaptive multiresolution technique and the characteristics method. On the aspect of theoretical analysis, the global existence and uniqueness of

Adaptive Robust Online Constructive Fuzzy Control of a Complex Surface Vehicle System.

Science.gov (United States)

Wang, Ning; Er, Meng Joo; Sun, Jing-Chao; Liu, Yan-Cheng

2016-07-01

In this paper, a novel adaptive robust online constructive fuzzy control (AR-OCFC) scheme, employing an online constructive fuzzy approximator (OCFA), to deal with tracking surface vehicles with uncertainties and unknown disturbances is proposed. Significant contributions of this paper are as follows: 1) unlike previous self-organizing fuzzy neural networks, the OCFA employs decoupled distance measure to dynamically allocate discriminable and sparse fuzzy sets in each dimension and is able to parsimoniously self-construct high interpretable T-S fuzzy rules; 2) an OCFA-based dominant adaptive controller (DAC) is designed by employing the improved projection-based adaptive laws derived from the Lyapunov synthesis which can guarantee reasonable fuzzy partitions; 3) closed-loop system stability and robustness are ensured by stable cancelation and decoupled adaptive compensation, respectively, thereby contributing to an auxiliary robust controller (ARC); and 4) global asymptotic closed-loop system can be guaranteed by AR-OCFC consisting of DAC and ARC and all signals are bounded. Simulation studies and comprehensive comparisons with state-of-the-arts fixed- and dynamic-structure adaptive control schemes demonstrate superior performance of the AR-OCFC in terms of tracking and approximation accuracy.

Modeling and Control of Magnetic Fluid Deformable Mirrors for Adaptive Optics Systems

CERN Document Server

Wu, Zhizheng; Ben Amara, Foued

2013-01-01

Modeling and Control of Magnetic Fluid Deformable Mirrors for Adaptive Optics Systems presents a novel design of wavefront correctors based on magnetic fluid deformable mirrors (MFDM) as well as corresponding control algorithms. The presented wavefront correctors are characterized by their linear, dynamic response. Various mirror surface shape control algorithms are presented along with experimental evaluations of the performance of the resulting adaptive optics systems. Adaptive optics (AO) systems are used in various fields of application to enhance the performance of optical systems, such as imaging, laser, free space optical communication systems, etc. This book is intended for undergraduate and graduate students, professors, engineers, scientists and researchers working on the design of adaptive optics systems and their various emerging fields of application. Zhizheng Wu is an associate professor at Shanghai University, China. Azhar Iqbal is a research associate at the University of Toronto, Canada. Foue...

Value Iteration Adaptive Dynamic Programming for Optimal Control of Discrete-Time Nonlinear Systems.

Science.gov (United States)

Wei, Qinglai; Liu, Derong; Lin, Hanquan

2016-03-01

In this paper, a value iteration adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon undiscounted optimal control problems for discrete-time nonlinear systems. The present value iteration ADP algorithm permits an arbitrary positive semi-definite function to initialize the algorithm. A novel convergence analysis is developed to guarantee that the iterative value function converges to the optimal performance index function. Initialized by different initial functions, it is proven that the iterative value function will be monotonically nonincreasing, monotonically nondecreasing, or nonmonotonic and will converge to the optimum. In this paper, for the first time, the admissibility properties of the iterative control laws are developed for value iteration algorithms. It is emphasized that new termination criteria are established to guarantee the effectiveness of the iterative control laws. Neural networks are used to approximate the iterative value function and compute the iterative control law, respectively, for facilitating the implementation of the iterative ADP algorithm. Finally, two simulation examples are given to illustrate the performance of the present method.

Algebraic and adaptive learning in neural control systems

Science.gov (United States)

Ferrari, Silvia

A systematic approach is developed for designing adaptive and reconfigurable nonlinear control systems that are applicable to plants modeled by ordinary differential equations. The nonlinear controller comprising a network of neural networks is taught using a two-phase learning procedure realized through novel techniques for initialization, on-line training, and adaptive critic design. A critical observation is that the gradients of the functions defined by the neural networks must equal corresponding linear gain matrices at chosen operating points. On-line training is based on a dual heuristic adaptive critic architecture that improves control for large, coupled motions by accounting for actual plant dynamics and nonlinear effects. An action network computes the optimal control law; a critic network predicts the derivative of the cost-to-go with respect to the state. Both networks are algebraically initialized based on prior knowledge of satisfactory pointwise linear controllers and continue to adapt on line during full-scale simulations of the plant. On-line training takes place sequentially over discrete periods of time and involves several numerical procedures. A backpropagating algorithm called Resilient Backpropagation is modified and successfully implemented to meet these objectives, without excessive computational expense. This adaptive controller is as conservative as the linear designs and as effective as a global nonlinear controller. The method is successfully implemented for the full-envelope control of a six-degree-of-freedom aircraft simulation. The results show that the on-line adaptation brings about improved performance with respect to the initialization phase during aircraft maneuvers that involve large-angle and coupled dynamics, and parameter variations.

Development of fault tolerant adaptive control laws for aerospace systems

Science.gov (United States)

Perez Rocha, Andres E.

The main topic of this dissertation is the design, development and implementation of intelligent adaptive control techniques designed to maintain healthy performance of aerospace systems subjected to malfunctions, external parameter changes and/or unmodeled dynamics. The dissertation is focused on the development of novel adaptive control configurations that rely on non-linear functions that appear in the immune system of living organisms as main source of adaptation. One of the main goals of this dissertation is to demonstrate that these novel adaptive control architectures are able to improve overall performance and protect the system while reducing control effort and maintaining adequate operation outside bounds of nominal design. This research effort explores several phases, ranging from theoretical stability analysis, simulation and hardware implementation on different types of aerospace systems including spacecraft, aircraft and quadrotor vehicles. The results presented in this dissertation are focused on two main adaptivity approaches, the first one is intended for aerospace systems that do not attain large angles and use exact feedback linearization of Euler angle kinematics. A proof of stability is presented by means of the circle Criterion and Lyapunov's direct method. The second approach is intended for aerospace systems that can attain large attitude angles (e.g. space systems in gravity-less environments), the adaptation is incorporated on a baseline architecture that uses partial feedback linearization of quaternions kinematics. In this case, the closed loop stability was analyzed using Lyapunov's direct method and Barbalat's Lemma. It is expected that some results presented in this dissertation can contribute towards the validation and certification of direct adaptive controllers.

Adaptive Nonsingular Terminal Sliding Model Control and Its Application to Permanent Magnet Synchronous Motor Drive System

Directory of Open Access Journals (Sweden)

Liu Yue

2016-01-01

Full Text Available To improve the dynamic performance of permanent magnet synchronous motor(PMSM drive system, a adaptive nonsingular terminal sliding model control((NTSMC strategy was proposed. The proposed control strategy presents an adaptive variable-rated exponential reaching law which the L1 norm of state variables is introduced. Exponential and constant approach speed can adaptively adjust according to the state variables’ distance to the equilibrium position.The proposed scheme can shorten the reaching time and weaken system chatting. The method was applied to the PMSM speed servo system, and compared with the traditional terminal-sliding-mode regulator and PI regulator. Simulation results show that the proposed control strategy can improve dynamic, steady performance and robustness.

Precision position control of servo systems using adaptive back-stepping and recurrent fuzzy neural networks

International Nuclear Information System (INIS)

Kim, Han Me; Kim, Jong Shik; Han, Seong Ik

2009-01-01

To improve position tracking performance of servo systems, a position tracking control using adaptive back-stepping control(ABSC) scheme and recurrent fuzzy neural networks(RFNN) is proposed. An adaptive rule of the ABSC based on system dynamics and dynamic friction model is also suggested to compensate nonlinear dynamic friction characteristics. However, it is difficult to reduce the position tracking error of servo systems by using only the ABSC scheme because of the system uncertainties which cannot be exactly identified during the modeling of servo systems. Therefore, in order to overcome system uncertainties and then to improve position tracking performance of servo systems, the RFNN technique is additionally applied to the servo system. The feasibility of the proposed control scheme for a servo system is validated through experiments. Experimental results show that the servo system with ABS controller based on the dual friction observer and RFNN including the reconstruction error estimator can achieve desired tracking performance and robustness

Dynamics of immune system vulnerabilities

Science.gov (United States)

Stromberg, Sean P.

The adaptive immune system can be viewed as a complex system, which adapts, over time, to reflect the history of infections experienced by the organism. Understanding its operation requires viewing it in terms of tradeoffs under constraints and evolutionary history. It typically displays "robust, yet fragile" behavior, meaning common tasks are robust to small changes but novel threats or changes in environment can have dire consequences. In this dissertation we use mechanistic models to study several biological processes: the immune response, the homeostasis of cells in the lymphatic system, and the process that normally prevents autoreactive cells from entering the lymphatic system. Using these models we then study the effects of these processes interacting. We show that the mechanisms that regulate the numbers of cells in the immune system, in conjunction with the immune response, can act to suppress autoreactive cells from proliferating, thus showing quantitatively how pathogenic infections can suppress autoimmune disease. We also show that over long periods of time this same effect can thin the repertoire of cells that defend against novel threats, leading to an age correlated vulnerability. This vulnerability is shown to be a consequence of system dynamics, not due to degradation of immune system components with age. Finally, modeling a specific tolerance mechanism that normally prevents autoimmune disease, in conjunction with models of the immune response and homeostasis we look at the consequences of the immune system mistakenly incorporating pathogenic molecules into its tolerizing mechanisms. The signature of this dynamic matches closely that of the dengue virus system.

Impulsive Synchronization and Adaptive-Impulsive Synchronization of a Novel Financial Hyperchaotic System

Directory of Open Access Journals (Sweden)

Xiuli Chai

2013-01-01

Full Text Available The impulsive synchronization and adaptive-impulsive synchronization of a novel financial hyperchaotic system are investigated. Based on comparing principle for impulsive functional differential equations, several sufficient conditions for impulsive synchronization are derived, and the upper bounds of impulsive interval for stable synchronization are estimated. Furthermore, a nonlinear adaptive-impulsive control scheme is designed to synchronize the financial system using invariant principle of impulsive dynamical systems. Moreover, corresponding numerical simulations are presented to illustrate the effectiveness and feasibility of the proposed methods.

Nonlinear attractor dynamics in the fundamental and extended prism adaptation paradigm

International Nuclear Information System (INIS)

Frank, T.D.; Blau, Julia J.C.; Turvey, M.T.

2009-01-01

Adaptation and re-adaptation processes are studied in terms of dynamic attractors that evolve and devolve. In doing so, a theoretical account is given for the fundamental observation that adaptation and re-adaptation processes do not exhibit one-trial learning. Moreover, the emergence of the latent aftereffect in the extended prism paradigm is addressed

Optimal and robust control of a class of nonlinear systems using dynamically re-optimised single network adaptive critic design

Science.gov (United States)

Tiwari, Shivendra N.; Padhi, Radhakant

2018-01-01

Following the philosophy of adaptive optimal control, a neural network-based state feedback optimal control synthesis approach is presented in this paper. First, accounting for a nominal system model, a single network adaptive critic (SNAC) based multi-layered neural network (called as NN1) is synthesised offline. However, another linear-in-weight neural network (called as NN2) is trained online and augmented to NN1 in such a manner that their combined output represent the desired optimal costate for the actual plant. To do this, the nominal model needs to be updated online to adapt to the actual plant, which is done by synthesising yet another linear-in-weight neural network (called as NN3) online. Training of NN3 is done by utilising the error information between the nominal and actual states and carrying out the necessary Lyapunov stability analysis using a Sobolev norm based Lyapunov function. This helps in training NN2 successfully to capture the required optimal relationship. The overall architecture is named as 'Dynamically Re-optimised single network adaptive critic (DR-SNAC)'. Numerical results for two motivating illustrative problems are presented, including comparison studies with closed form solution for one problem, which clearly demonstrate the effectiveness and benefit of the proposed approach.

Managing lifelike behavior in a dynamic self-assembled system

Science.gov (United States)

Ropp, Chad; Bachelard, Nicolas; Wang, Yuan; Zhang, Xiang

Self-organization can arise outside of thermodynamic equilibrium in a process of dynamic self-assembly. This is observed in nature, for example in flocking birds, but can also be created artificially with non-living entities. Such dynamic systems often display lifelike properties, including the ability to self-heal and adapt to environmental changes, which arise due to the collective and often complex interactions between the many individual elements. Such interactions are inherently difficult to predict and control, and limit the development of artificial systems. Here, we report a fundamentally new method to manage dynamic self-assembly through the direct external control of collective phenomena. Our system consists of a waveguide filled with mobile scattering particles. These particles spontaneously self-organize when driven by a coherent field, self-heal when mechanically perturbed, and adapt to changes in the drive wavelength. This behavior is governed by particle interactions that are completely mediated by coherent wave scattering. Compared to hydrodynamic interactions which lead to compact ordered structures, our system displays sinusoidal degeneracy and many different steady-state geometries that can be adjusted using the external field.

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.

Comparing dynamical systems concepts and techniques for biomechanical analysis

Institute of Scientific and Technical Information of China (English)

Richard E.A. van Emmerik; Scott W. Ducharme; Avelino C. Amado; Joseph Hamill

2016-01-01

Traditional biomechanical analyses of human movement are generally derived from linear mathematics. While these methods can be useful in many situations, they do not describe behaviors in human systems that are predominately nonlinear. For this reason, nonlinear analysis methods based on a dynamical systems approach have become more prevalent in recent literature. These analysis techniques have provided new insights into how systems (1) maintain pattern stability, (2) transition into new states, and (3) are governed by short-and long-term (fractal) correlational processes at different spatio-temporal scales. These different aspects of system dynamics are typically investigated using concepts related to variability, stability, complexity, and adaptability. The purpose of this paper is to compare and contrast these different concepts and demonstrate that, although related, these terms represent fundamentally different aspects of system dynamics. In particular, we argue that variability should not uniformly be equated with stability or complexity of movement. In addition, current dynamic stability measures based on nonlinear analysis methods (such as the finite maximal Lyapunov exponent) can reveal local instabilities in movement dynamics, but the degree to which these local instabilities relate to global postural and gait stability and the ability to resist external perturbations remains to be explored. Finally, systematic studies are needed to relate observed reductions in complexity with aging and disease to the adaptive capabilities of the movement system and how complexity changes as a function of different task constraints.

Comparing dynamical systems concepts and techniques for biomechanical analysis

Directory of Open Access Journals (Sweden)

Richard E.A. van Emmerik

2016-03-01

Full Text Available Traditional biomechanical analyses of human movement are generally derived from linear mathematics. While these methods can be useful in many situations, they do not describe behaviors in human systems that are predominately nonlinear. For this reason, nonlinear analysis methods based on a dynamical systems approach have become more prevalent in recent literature. These analysis techniques have provided new insights into how systems (1 maintain pattern stability, (2 transition into new states, and (3 are governed by short- and long-term (fractal correlational processes at different spatio-temporal scales. These different aspects of system dynamics are typically investigated using concepts related to variability, stability, complexity, and adaptability. The purpose of this paper is to compare and contrast these different concepts and demonstrate that, although related, these terms represent fundamentally different aspects of system dynamics. In particular, we argue that variability should not uniformly be equated with stability or complexity of movement. In addition, current dynamic stability measures based on nonlinear analysis methods (such as the finite maximal Lyapunov exponent can reveal local instabilities in movement dynamics, but the degree to which these local instabilities relate to global postural and gait stability and the ability to resist external perturbations remains to be explored. Finally, systematic studies are needed to relate observed reductions in complexity with aging and disease to the adaptive capabilities of the movement system and how complexity changes as a function of different task constraints.

Quantifying the Adaptive Cycle.

Directory of Open Access Journals (Sweden)

David G Angeler

Full Text Available The adaptive cycle was proposed as a conceptual model to portray patterns of change in complex systems. Despite the model having potential for elucidating change across systems, it has been used mainly as a metaphor, describing system dynamics qualitatively. We use a quantitative approach for testing premises (reorganisation, conservatism, adaptation in the adaptive cycle, using Baltic Sea phytoplankton communities as an example of such complex system dynamics. Phytoplankton organizes in recurring spring and summer blooms, a well-established paradigm in planktology and succession theory, with characteristic temporal trajectories during blooms that may be consistent with adaptive cycle phases. We used long-term (1994-2011 data and multivariate analysis of community structure to assess key components of the adaptive cycle. Specifically, we tested predictions about: reorganisation: spring and summer blooms comprise distinct community states; conservatism: community trajectories during individual adaptive cycles are conservative; and adaptation: phytoplankton species during blooms change in the long term. All predictions were supported by our analyses. Results suggest that traditional ecological paradigms such as phytoplankton successional models have potential for moving the adaptive cycle from a metaphor to a framework that can improve our understanding how complex systems organize and reorganize following collapse. Quantifying reorganization, conservatism and adaptation provides opportunities to cope with the intricacies and uncertainties associated with fast ecological change, driven by shifting system controls. Ultimately, combining traditional ecological paradigms with heuristics of complex system dynamics using quantitative approaches may help refine ecological theory and improve our understanding of the resilience of ecosystems.

Nonlinear Dynamics of Controlled Synchronizations of Manipulator System

Directory of Open Access Journals (Sweden)

Qingkai Han

2014-01-01

Full Text Available The nonlinear dynamics of the manipulator system which is controlled to achieve the synchronization motions is investigated in the paper. Firstly, the control strategies and modeling approaches of the manipulator system are given, in which the synchronization goal is defined by both synchronization errors and its derivatives. The synchronization controllers applied on the manipulator system include neuron synchronization controller, improved OPCL synchronization controller, and MRAC-PD synchronization controller. Then, an improved adaptive synchronized control strategy is proposed in order to estimate online the unknown structure parameters and state variables of the manipulator system and to realize the needed synchronous compensation. Furthermore, a robust adaptive synchronization controller is also researched to guarantee the dynamic stability of the system. Finally, the stability of motion synchronizations of the manipulator system possessing nonlinear component is discussed, together with the effect of control parameters and joint friction and others. Some typical motions such as motion bifurcations and the loss of synchronization of it are obtained and illustrated as periodic, multiperiodic, and/or chaotic motion patterns.

A two-stage planning and control model toward Economically Adapted Power Distribution Systems using analytical hierarchy processes and fuzzy optimization

Energy Technology Data Exchange (ETDEWEB)

Schweickardt, Gustavo [Instituto de Economia Energetica, Fundacion Bariloche, Centro Atomico Bariloche - Pabellon 7, Av. Bustillo km 9500, 8400 Bariloche (Argentina); Miranda, Vladimiro [INESC Porto, Instituto de Engenharia de Sistemas e Computadores do Porto and FEUP, Faculdade de Engenharia da Universidade do Porto, R. Dr. Roberto Frias, 378, 4200-465 Porto (Portugal)

2009-07-15

This work presents a model to evaluate the Distribution System Dynamic De-adaptation respecting its planning for a given period of Tariff Control. The starting point for modeling is brought about by the results from a multi-criteria method based on Fuzzy Dynamic Programming and on Analytic Hierarchy Processes applied in a mid/short-term horizon (stage 1). Then, the decision-making activities using the Hierarchy Analytical Processes will allow defining, for a Control of System De-adaptation (stage 2), a Vector to evaluate the System Dynamic Adaptation. It is directly associated to an eventual series of inbalances that take place during its evolution. (author)

Normalized value coding explains dynamic adaptation in the human valuation process.

Science.gov (United States)

Khaw, Mel W; Glimcher, Paul W; Louie, Kenway

2017-11-28

The notion of subjective value is central to choice theories in ecology, economics, and psychology, serving as an integrated decision variable by which options are compared. Subjective value is often assumed to be an absolute quantity, determined in a static manner by the properties of an individual option. Recent neurobiological studies, however, have shown that neural value coding dynamically adapts to the statistics of the recent reward environment, introducing an intrinsic temporal context dependence into the neural representation of value. Whether valuation exhibits this kind of dynamic adaptation at the behavioral level is unknown. Here, we show that the valuation process in human subjects adapts to the history of previous values, with current valuations varying inversely with the average value of recently observed items. The dynamics of this adaptive valuation are captured by divisive normalization, linking these temporal context effects to spatial context effects in decision making as well as spatial and temporal context effects in perception. These findings suggest that adaptation is a universal feature of neural information processing and offer a unifying explanation for contextual phenomena in fields ranging from visual psychophysics to economic choice.

Adaptation and inertia in dynamic environments

DEFF Research Database (Denmark)

Stieglitz, Nils; Knudsen, Thorbjørn; Becker, Markus C.

2016-01-01

responses to these dimensions. Our results show how frequent directional changes undermine the value of exploration and decisively shift performance advantages to inert organizations that restrict exploration. In contrast, increased environmental variance rewards exploration. Our results also show that......Research summary: We address conflicting claims and mixed empirical findings about adaptation as a response to increased environmental dynamism. We disentangle distinct dimensions of environmental dynamism—the direction, magnitude, and frequency of change—and identify how selection shapes adaptive...... business environments characterized by persistent trends and by large, infrequently occurring structural shocks reward strategic pursuit of temporary advantage. Thus, exploration and strategic flexibility are preferred strategies. In contrast, the challenge in frequently changing environments with fleeting...

What to change and what to keep? Values and dynamics of adaptation to climate change

Directory of Open Access Journals (Sweden)

Sebastian Wessels

2015-04-01

Full Text Available This paper uses a complex systems theory framework to clarify what adaptation to climate change means in practice, which is to make targeted changes to a society's functioning in order to avoid changes happening to that which is of value to the members of that society. It is shown that the question what is to be changed and what to be preserved is not prescribed by the facts of climate change and technology, but a contingent one to be made by society. Discussing four important domains of adaptation and the respective narratives found in academia and politics, it is investigated how these decisions are formed, giving special consideration to the case of Germany. This leads to the finding that the generally defensive framings that characterizes common notions of adaptation reinforce predominant cultural paradigms and social dynamics that arguably have contributed considerably to the need for adaptation to climate change in the first place and will most likely create further need for adaptation in the future. A paradoxical tendency to accelerate predominant social dynamics in attempts to keep current states of affairs unchanged is identified. It is concluded that the concept of adaptation is a regression behind the concept of sustainability which can easily accommodate adaptation needs but avoids the identified pitfalls of adaptation by its future orientation and oft-criticized openness.

Recurrent-Neural-Network-Based Multivariable Adaptive Control for a Class of Nonlinear Dynamic Systems With Time-Varying Delay.

Science.gov (United States)

Hwang, Chih-Lyang; Jan, Chau

2016-02-01

At the beginning, an approximate nonlinear autoregressive moving average (NARMA) model is employed to represent a class of multivariable nonlinear dynamic systems with time-varying delay. It is known that the disadvantages of robust control for the NARMA model are as follows: 1) suitable control parameters for larger time delay are more sensitive to achieving desirable performance; 2) it only deals with bounded uncertainty; and 3) the nominal NARMA model must be learned in advance. Due to the dynamic feature of the NARMA model, a recurrent neural network (RNN) is online applied to learn it. However, the system performance becomes deteriorated due to the poor learning of the larger variation of system vector functions. In this situation, a simple network is employed to compensate the upper bound of the residue caused by the linear parameterization of the approximation error of RNN. An e -modification learning law with a projection for weight matrix is applied to guarantee its boundedness without persistent excitation. Under suitable conditions, the semiglobally ultimately bounded tracking with the boundedness of estimated weight matrix is obtained by the proposed RNN-based multivariable adaptive control. Finally, simulations are presented to verify the effectiveness and robustness of the proposed control.

Achieving Optimal Self-Adaptivity for Dynamic Tuning of Organic Semiconductors through Resonance Engineering.

Science.gov (United States)

Tao, Ye; Xu, Lijia; Zhang, Zhen; Chen, Runfeng; Li, Huanhuan; Xu, Hui; Zheng, Chao; Huang, Wei

2016-08-03

Current static-state explorations of organic semiconductors for optimal material properties and device performance are hindered by limited insights into the dynamically changed molecular states and charge transport and energy transfer processes upon device operation. Here, we propose a simple yet successful strategy, resonance variation-based dynamic adaptation (RVDA), to realize optimized self-adaptive properties in donor-resonance-acceptor molecules by engineering the resonance variation for dynamic tuning of organic semiconductors. Organic light-emitting diodes hosted by these RVDA materials exhibit remarkably high performance, with external quantum efficiencies up to 21.7% and favorable device stability. Our approach, which supports simultaneous realization of dynamically adapted and selectively enhanced properties via resonance engineering, illustrates a feasible design map for the preparation of smart organic semiconductors capable of dynamic structure and property modulations, promoting the studies of organic electronics from static to dynamic.

Structure identification and adaptive synchronization of uncertain general complex dynamical networks

International Nuclear Information System (INIS)

Xu Yuhua; Zhou Wuneng; Fang Jian'an; Lu Hongqian

2009-01-01

This Letter proposes an approach to identify the topological structure and unknown parameters for uncertain general complex networks simultaneously. By designing effective adaptive controllers, we achieve synchronization between two complex networks. The unknown network topological structure and system parameters of uncertain general complex dynamical networks are identified simultaneously in the process of synchronization. Several useful criteria for synchronization are given. Finally, an illustrative example is presented to demonstrate the application of the theoretical results.

Structure identification and adaptive synchronization of uncertain general complex dynamical networks

Energy Technology Data Exchange (ETDEWEB)

Xu Yuhua, E-mail: yuhuaxu2004@163.co [College of Information Science and Technology, Donghua University, Shanghai 201620 (China) and Department of Maths, Yunyang Teacher' s College, Hubei 442000 (China); Zhou Wuneng, E-mail: wnzhou@163.co [College of Information Science and Technology, Donghua University, Shanghai 201620 (China); Fang Jian' an [College of Information Science and Technology, Donghua University, Shanghai 201620 (China); Lu Hongqian [Shandong Institute of Light Industry, Shandong Jinan 250353 (China)

2009-12-28

This Letter proposes an approach to identify the topological structure and unknown parameters for uncertain general complex networks simultaneously. By designing effective adaptive controllers, we achieve synchronization between two complex networks. The unknown network topological structure and system parameters of uncertain general complex dynamical networks are identified simultaneously in the process of synchronization. Several useful criteria for synchronization are given. Finally, an illustrative example is presented to demonstrate the application of the theoretical results.

Chaos control of the micro-electro-mechanical resonator by using adaptive dynamic surface technology with extended state observer

International Nuclear Information System (INIS)

Luo, Shaohua; Sun, Quanping; Cheng, Wei

2016-01-01

This paper addresses chaos control of the micro-electro- mechanical resonator by using adaptive dynamic surface technology with extended state observer. To reveal the mechanism of the micro- electro-mechanical resonator, the phase diagrams and corresponding time histories are given to research the nonlinear dynamics and chaotic behavior, and Homoclinic and heteroclinic chaos which relate closely with the appearance of chaos are presented based on the potential function. To eliminate the effect of chaos, an adaptive dynamic surface control scheme with extended state observer is designed to convert random motion into regular motion without precise system model parameters and measured variables. Putting tracking differentiator into chaos controller solves the ‘explosion of complexity’ of backstepping and poor precision of the first-order filters. Meanwhile, to obtain high performance, a neural network with adaptive law is employed to approximate unknown nonlinear function in the process of controller design. The boundedness of all the signals of the closed-loop system is proved in theoretical analysis. Finally, numerical simulations are executed and extensive results illustrate effectiveness and robustness of the proposed scheme.

Adaptative synchronization in multi-output fractional-order complex dynamical networks and secure communications

Science.gov (United States)

Mata-Machuca, Juan L.; Aguilar-López, Ricardo

2018-01-01

This work deals with the adaptative synchronization of complex dynamical networks with fractional-order nodes and its application in secure communications employing chaotic parameter modulation. The complex network is composed of multiple fractional-order systems with mismatch parameters and the coupling functions are given to realize the network synchronization. We introduce a fractional algebraic synchronizability condition (FASC) and a fractional algebraic identifiability condition (FAIC) which are used to know if the synchronization and parameters estimation problems can be solved. To overcome these problems, an adaptative synchronization methodology is designed; the strategy consists in proposing multiple receiver systems which tend to follow asymptotically the uncertain transmitters systems. The coupling functions and parameters of the receiver systems are adjusted continually according to a convenient sigmoid-like adaptative controller (SLAC), until the measurable output errors converge to zero, hence, synchronization between transmitter and receivers is achieved and message signals are recovered. Indeed, the stability analysis of the synchronization error is based on the fractional Lyapunov direct method. Finally, numerical results corroborate the satisfactory performance of the proposed scheme by means of the synchronization of a complex network consisting of several fractional-order unified chaotic systems.

Adapting Dynamic Mathematical Models to a Pilot Anaerobic Digestion Reactor

Directory of Open Access Journals (Sweden)

F. Haugen, R. Bakke, and B. Lie

2013-04-01

Full Text Available A dynamic model has been adapted to a pilot anaerobic reactor fed diarymanure. Both steady-state data from online sensors and laboratory analysis anddynamic operational data from online sensors are used in the model adaptation.The model is based on material balances, and comprises four state variables,namely biodegradable volatile solids, volatile fatty acids, acid generatingmicrobes (acidogens, and methane generating microbes (methanogens. The modelcan predict the methane gas flow produced in the reactor. The model may beused for optimal reactor design and operation, state-estimation and control.Also, a dynamic model for the reactor temperature based on energy balance ofthe liquid in the reactor is adapted. This model may be used for optimizationand control when energy and economy are taken into account.

PAQ: Persistent Adaptive Query Middleware for Dynamic Environments

Science.gov (United States)

Rajamani, Vasanth; Julien, Christine; Payton, Jamie; Roman, Gruia-Catalin

Pervasive computing applications often entail continuous monitoring tasks, issuing persistent queries that return continuously updated views of the operational environment. We present PAQ, a middleware that supports applications' needs by approximating a persistent query as a sequence of one-time queries. PAQ introduces an integration strategy abstraction that allows composition of one-time query responses into streams representing sophisticated spatio-temporal phenomena of interest. A distinguishing feature of our middleware is the realization that the suitability of a persistent query's result is a function of the application's tolerance for accuracy weighed against the associated overhead costs. In PAQ, programmers can specify an inquiry strategy that dictates how information is gathered. Since network dynamics impact the suitability of a particular inquiry strategy, PAQ associates an introspection strategy with a persistent query, that evaluates the quality of the query's results. The result of introspection can trigger application-defined adaptation strategies that alter the nature of the query. PAQ's simple API makes developing adaptive querying systems easily realizable. We present the key abstractions, describe their implementations, and demonstrate the middleware's usefulness through application examples and evaluation.

Development of adaptive control applied to chaotic systems

Science.gov (United States)

Rhode, Martin Andreas

1997-12-01

Continuous-time derivative control and adaptive map-based recursive feedback control techniques are used to control chaos in a variety of systems and in situations that are of practical interest. The theoretical part of the research includes the review of fundamental concept of control theory in the context of its applications to deterministic chaotic systems, the development of a new adaptive algorithm to identify the linear system properties necessary for control, and the extension of the recursive proportional feedback control technique, RPF, to high dimensional systems. Chaos control was applied to models of a thermal pulsed combustor, electro-chemical dissolution and the hyperchaotic Rossler system. Important implications for combustion engineering were suggested by successful control of the model of the thermal pulsed combustor. The system was automatically tracked while maintaining control into regions of parameter and state space where no stable attractors exist. In a simulation of the electrochemical dissolution system, application of derivative control to stabilize a steady state, and adaptive RPF to stabilize a period one orbit, was demonstrated. The high dimensional adaptive control algorithm was applied in a simulation using the Rossler hyperchaotic system, where a period-two orbit with two unstable directions was stabilized and tracked over a wide range of a system parameter. In the experimental part, the electrochemical system was studied in parameter space, by scanning the applied potential and the frequency of the rotating copper disk. The automated control algorithm is demonstrated to be effective when applied to stabilize a period-one orbit in the experiment. We show the necessity of small random perturbations applied to the system in order to both learn the dynamics and control the system at the same time. The simultaneous learning and control capability is shown to be an important part of the active feedback control.

Adaptive infrared-reflecting systems inspired by cephalopods

Science.gov (United States)

Xu, Chengyi; Stiubianu, George T.; Gorodetsky, Alon A.

2018-03-01

Materials and systems that statically reflect radiation in the infrared region of the electromagnetic spectrum underpin the performance of many entrenched technologies, including building insulation, energy-conserving windows, spacecraft components, electronics shielding, container packaging, protective clothing, and camouflage platforms. The development of their adaptive variants, in which the infrared-reflecting properties dynamically change in response to external stimuli, has emerged as an important unmet scientific challenge. By drawing inspiration from cephalopod skin, we developed adaptive infrared-reflecting platforms that feature a simple actuation mechanism, low working temperature, tunable spectral range, weak angular dependence, fast response, stability to repeated cycling, amenability to patterning and multiplexing, autonomous operation, robust mechanical properties, and straightforward manufacturability. Our findings may open opportunities for infrared camouflage and other technologies that regulate infrared radiation.

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.

Runtime adaptive multi-processor system-on-chip: RAMPSoC

OpenAIRE

Göhringer, D.; Hübner, M.; Schatz, V.; Becker, J.

2008-01-01

Current trends in high performance computing show, that the usage of multiprocessor systems on chip are one approach for the requirements of computing intensive applications. The multiprocessor system on chip (MPSoC) approaches often provide a static and homogeneous infrastructure of networked microprocessor on the chip die. A novel idea in this research area is to introduce the dynamic adaptivity of reconfigurable hardware in order to provide a flexible heterogeneous set of processing elemen...

Dynamics of epidemic diseases on a growing adaptive network.

Science.gov (United States)

Demirel, Güven; Barter, Edmund; Gross, Thilo

2017-02-10

The study of epidemics on static networks has revealed important effects on disease prevalence of network topological features such as the variance of the degree distribution, i.e. the distribution of the number of neighbors of nodes, and the maximum degree. Here, we analyze an adaptive network where the degree distribution is not independent of epidemics but is shaped through disease-induced dynamics and mortality in a complex interplay. We study the dynamics of a network that grows according to a preferential attachment rule, while nodes are simultaneously removed from the network due to disease-induced mortality. We investigate the prevalence of the disease using individual-based simulations and a heterogeneous node approximation. Our results suggest that in this system in the thermodynamic limit no epidemic thresholds exist, while the interplay between network growth and epidemic spreading leads to exponential networks for any finite rate of infectiousness when the disease persists.

Dynamic balance during walking adaptability tasks in individuals post-stroke.

Science.gov (United States)

Vistamehr, Arian; Balasubramanian, Chitralakshmi K; Clark, David J; Neptune, Richard R; Fox, Emily J

2018-04-24

Maintaining dynamic balance during community ambulation is a major challenge post-stroke. Community ambulation requires performance of steady-state level walking as well as tasks that require walking adaptability. Prior studies on balance control post-stroke have mainly focused on steady-state walking, but walking adaptability tasks have received little attention. The purpose of this study was to quantify and compare dynamic balance requirements during common walking adaptability tasks post-stroke and in healthy adults and identify differences in underlying mechanisms used for maintaining dynamic balance. Kinematic data were collected from fifteen individuals with post-stroke hemiparesis during steady-state forward and backward walking, obstacle negotiation, and step-up tasks. In addition, data from ten healthy adults provided the basis for comparison. Dynamic balance was quantified using the peak-to-peak range of whole-body angular-momentum in each anatomical plane during the paretic, nonparetic and healthy control single-leg-stance phase of the gait cycle. To understand differences in some of the key underlying mechanisms for maintaining dynamic balance, foot placement and plantarflexor muscle activation were examined. Individuals post-stroke had significant dynamic balance deficits in the frontal plane across most tasks, particularly during the paretic single-leg-stance. Frontal plane balance deficits were associated with wider paretic foot placement, elevated body center-of-mass, and lower soleus activity. Further, the obstacle negotiation task imposed a higher balance requirement, particularly during the trailing leg single-stance. Thus, improving paretic foot placement and ankle plantarflexor activity, particularly during obstacle negotiation, may be important rehabilitation targets to enhance dynamic balance during post-stroke community ambulation. Copyright © 2018. Published by Elsevier Ltd.

ℋ- adaptive observer design and parameter identification for a class of nonlinear fractional-order systems

KAUST Repository

Ndoye, Ibrahima

2014-12-01

In this paper, an adaptive observer design with parameter identification for a nonlinear system with external perturbations and unknown parameters is proposed. The states of the nonlinear system are estimated by a nonlinear observer and the unknown parameters are also adapted to their values. Sufficient conditions for the stability of the adaptive observer error dynamics are derived in terms of linear matrix inequalities. Simulation results for chaotic Lorenz systems with unknown parameters in the presence of external perturbations are given to illustrate the effectiveness of our proposed approach. © 2014 IEEE.

An Adaptive Genetic Algorithm with Dynamic Population Size for Optimizing Join Queries

OpenAIRE

Vellev, Stoyan

2008-01-01

The problem of finding the optimal join ordering executing a query to a relational database management system is a combinatorial optimization problem, which makes deterministic exhaustive solution search unacceptable for queries with a great number of joined relations. In this work an adaptive genetic algorithm with dynamic population size is proposed for optimizing large join queries. The performance of the algorithm is compared with that of several classical non-determinis...

Use of sensitivity-information for the adaptive simulation of thermo-hydraulic system codes

International Nuclear Information System (INIS)

Kerner, Alexander M.

2011-01-01

Within the scope of this thesis the development of methods for online-adaptation of dynamical plant simulations of a thermal-hydraulic system code to measurement data is depicted. The described approaches are mainly based on the use of sensitivity-information in different areas: statistical sensitivity measures are used for the identification of the parameters to be adapted and online-sensitivities for the parameter adjustment itself. For the parameter adjustment the method of a ''system-adapted heuristic adaptation with partial separation'' (SAHAT) was developed, which combines certain variants of parameter estimation and control with supporting procedures to solve the basic problems. The applicability of the methods is shown by adaptive simulations of a PKL-III experiment and by selected transients in a nuclear power plant. Finally the main perspectives for the application of a tracking simulator on a system code are identified.

The absence or temporal offset of visual feedback does not influence adaptation to novel movement dynamics.

Science.gov (United States)

McKenna, Erin; Bray, Laurence C Jayet; Zhou, Weiwei; Joiner, Wilsaan M

2017-10-01

Delays in transmitting and processing sensory information require correctly associating delayed feedback to issued motor commands for accurate error compensation. The flexibility of this alignment between motor signals and feedback has been demonstrated for movement recalibration to visual manipulations, but the alignment dependence for adapting movement dynamics is largely unknown. Here we examined the effect of visual feedback manipulations on force-field adaptation. Three subject groups used a manipulandum while experiencing a lag in the corresponding cursor motion (0, 75, or 150 ms). When the offset was applied at the start of the session (continuous condition), adaptation was not significantly different between groups. However, these similarities may be due to acclimation to the offset before motor adaptation. We tested additional subjects who experienced the same delays concurrent with the introduction of the perturbation (abrupt condition). In this case adaptation was statistically indistinguishable from the continuous condition, indicating that acclimation to feedback delay was not a factor. In addition, end-point errors were not significantly different across the delay or onset conditions, but end-point correction (e.g., deceleration duration) was influenced by the temporal offset. As an additional control, we tested a group of subjects who performed without visual feedback and found comparable movement adaptation results. These results suggest that visual feedback manipulation (absence or temporal misalignment) does not affect adaptation to novel dynamics, independent of both acclimation and perceptual awareness. These findings could have implications for modeling how the motor system adjusts to errors despite concurrent delays in sensory feedback information. NEW & NOTEWORTHY A temporal offset between movement and distorted visual feedback (e.g., visuomotor rotation) influences the subsequent motor recalibration, but the effects of this offset for

Active Hearing Mechanisms Inspire Adaptive Amplification in an Acoustic Sensor System.

Science.gov (United States)

Guerreiro, Jose; Reid, Andrew; Jackson, Joseph C; Windmill, James F C

2018-06-01

Over many millions of years of evolution, nature has developed some of the most adaptable sensors and sensory systems possible, capable of sensing, conditioning and processing signals in a very power- and size-effective manner. By looking into biological sensors and systems as a source of inspiration, this paper presents the study of a bioinspired concept of signal processing at the sensor level. By exploiting a feedback control mechanism between a front-end acoustic receiver and back-end neuronal based computation, a nonlinear amplification with hysteretic behavior is created. Moreover, the transient response of the front-end acoustic receiver can also be controlled and enhanced. A theoretical model is proposed and the concept is prototyped experimentally through an embedded system setup that can provide dynamic adaptations of a sensory system comprising a MEMS microphone placed in a closed-loop feedback system. It faithfully mimics the mosquito's active hearing response as a function of the input sound intensity. This is an adaptive acoustic sensor system concept that can be exploited by sensor and system designers within acoustics and ultrasonic engineering fields.

Adaptive typography for dynamic mapping environments

Science.gov (United States)

Bardon, Didier

1991-08-01

When typography moves across a map, it passes over areas of different colors, densities, and textures. In such a dynamic environment, the aspect of typography must be constantly adapted to provide disernibility for every new background. Adaptive typography undergoes two adaptive operations: background control and contrast control. The background control prevents the features of the map (edges, lines, abrupt changes of densities) from destroying the integrity of the letterform. This is achieved by smoothing the features of the map in the area where a text label is displayed. The modified area is limited to the space covered by the characters of the label. Dispositions are taken to insure that the smoothing operation does not introduce any new visual noise. The contrast control assures that there are sufficient lightness differences between the typography and its ever-changing background. For every new situation, background color and foreground color are compared and the foreground color lightness is adjusted according to a chosen contrast value. Criteria and methods of choosing the appropriate contrast value are presented as well as the experiments that led to them.

Dynamic Modeling of a Reformed Methanol Fuel Cell System using Empirical Data and Adaptive Neuro-Fuzzy Inference System Models

DEFF Research Database (Denmark)

Justesen, Kristian Kjær; Andreasen, Søren Juhl; Shaker, Hamid Reza

2013-01-01

In this work, a dynamic MATLAB Simulink model of a H3-350 Reformed Methanol Fuel Cell (RMFC) stand-alone battery charger produced by Serenergy is developed on the basis of theoretical and empirical methods. The advantage of RMFC systems is that they use liquid methanol as a fuel instead of gaseous...... of the reforming process are implemented. Models of the cooling flow of the blowers for the fuel cell and the burner which supplies process heat for the reformer are made. The two blowers have a common exhaust, which means that the two blowers influence each other’s output. The models take this into account using...... an empirical approach. Fin efficiency models for the cooling effect of the air are also developed using empirical methods. A fuel cell model is also implemented based on a standard model which is adapted to fit the measured performance of the H3-350 module. All the individual parts of the model are verified...

Dynamic Modeling of a Reformed Methanol Fuel Cell System using Empirical Data and Adaptive Neuro-Fuzzy Inference System Models

DEFF Research Database (Denmark)

Justesen, Kristian Kjær; Andreasen, Søren Juhl; Shaker, Hamid Reza

2014-01-01

In this work, a dynamic MATLAB Simulink model of a H3-350 Reformed Methanol Fuel Cell (RMFC) stand-alone battery charger produced by Serenergy is developed on the basis of theoretical and empirical methods. The advantage of RMFC systems is that they use liquid methanol as a fuel instead of gaseous...... of the reforming process are implemented. Models of the cooling flow of the blowers for the fuel cell and the burner which supplies process heat for the reformer are made. The two blowers have a common exhaust, which means that the two blowers influence each other’s output. The models take this into account using...... an empirical approach. Fin efficiency models for the cooling effect of the air are also developed using empirical methods. A fuel cell model is also implemented based on a standard model which is adapted to fit the measured performance of the H3-350 module. All the individual parts of the model are verified...

Chaos control of the brushless direct current motor using adaptive dynamic surface control based on neural network with the minimum weights

International Nuclear Information System (INIS)

Luo, Shaohua; Wu, Songli; Gao, Ruizhen

2015-01-01

This paper investigates chaos control for the brushless DC motor (BLDCM) system by adaptive dynamic surface approach based on neural network with the minimum weights. The BLDCM system contains parameter perturbation, chaotic behavior, and uncertainty. With the help of radial basis function (RBF) neural network to approximate the unknown nonlinear functions, the adaptive law is established to overcome uncertainty of the control gain. By introducing the RBF neural network and adaptive technology into the dynamic surface control design, a robust chaos control scheme is developed. It is proved that the proposed control approach can guarantee that all signals in the closed-loop system are globally uniformly bounded, and the tracking error converges to a small neighborhood of the origin. Simulation results are provided to show that the proposed approach works well in suppressing chaos and parameter perturbation

Chaos control of the brushless direct current motor using adaptive dynamic surface control based on neural network with the minimum weights.

Science.gov (United States)

Luo, Shaohua; Wu, Songli; Gao, Ruizhen

2015-07-01

This paper investigates chaos control for the brushless DC motor (BLDCM) system by adaptive dynamic surface approach based on neural network with the minimum weights. The BLDCM system contains parameter perturbation, chaotic behavior, and uncertainty. With the help of radial basis function (RBF) neural network to approximate the unknown nonlinear functions, the adaptive law is established to overcome uncertainty of the control gain. By introducing the RBF neural network and adaptive technology into the dynamic surface control design, a robust chaos control scheme is developed. It is proved that the proposed control approach can guarantee that all signals in the closed-loop system are globally uniformly bounded, and the tracking error converges to a small neighborhood of the origin. Simulation results are provided to show that the proposed approach works well in suppressing chaos and parameter perturbation.

Exploring dynamics of embedded ADC through adapted digital input stimuli

NARCIS (Netherlands)

Sheng, Xiaoqin; Kerkhoff, Hans G.; Zjajo, A.; Gronthoud, G.

2008-01-01

This paper reports an evaluation of adapted digital signals as a test stimulus to test dynamic parameters of analog-to-digital converters (ADC). In the first instance, the simplest digital waveform, a pulse signal, is taken as the test stimulus. The dynamics of the device under test while applying

An adaptive robust controller for time delay maglev transportation systems

Science.gov (United States)

Milani, Reza Hamidi; Zarabadipour, Hassan; Shahnazi, Reza

2012-12-01

For engineering systems, uncertainties and time delays are two important issues that must be considered in control design. Uncertainties are often encountered in various dynamical systems due to modeling errors, measurement noises, linearization and approximations. Time delays have always been among the most difficult problems encountered in process control. In practical applications of feedback control, time delay arises frequently and can severely degrade closed-loop system performance and in some cases, drives the system to instability. Therefore, stability analysis and controller synthesis for uncertain nonlinear time-delay systems are important both in theory and in practice and many analytical techniques have been developed using delay-dependent Lyapunov function. In the past decade the magnetic and levitation (maglev) transportation system as a new system with high functionality has been the focus of numerous studies. However, maglev transportation systems are highly nonlinear and thus designing controller for those are challenging. The main topic of this paper is to design an adaptive robust controller for maglev transportation systems with time-delay, parametric uncertainties and external disturbances. In this paper, an adaptive robust control (ARC) is designed for this purpose. It should be noted that the adaptive gain is derived from Lyapunov-Krasovskii synthesis method, therefore asymptotic stability is guaranteed.

A Nonlinear Dynamic Inversion Predictor-Based Model Reference Adaptive Controller for a Generic Transport Model

Science.gov (United States)

Campbell, Stefan F.; Kaneshige, John T.

2010-01-01

Presented here is a Predictor-Based Model Reference Adaptive Control (PMRAC) architecture for a generic transport aircraft. At its core, this architecture features a three-axis, non-linear, dynamic-inversion controller. Command inputs for this baseline controller are provided by pilot roll-rate, pitch-rate, and sideslip commands. This paper will first thoroughly present the baseline controller followed by a description of the PMRAC adaptive augmentation to this control system. Results are presented via a full-scale, nonlinear simulation of NASA s Generic Transport Model (GTM).

Sex speeds adaptation by altering the dynamics of molecular evolution.

Science.gov (United States)

McDonald, Michael J; Rice, Daniel P; Desai, Michael M

2016-03-10

Sex and recombination are pervasive throughout nature despite their substantial costs. Understanding the evolutionary forces that maintain these phenomena is a central challenge in biology. One longstanding hypothesis argues that sex is beneficial because recombination speeds adaptation. Theory has proposed several distinct population genetic mechanisms that could underlie this advantage. For example, sex can promote the fixation of beneficial mutations either by alleviating interference competition (the Fisher-Muller effect) or by separating them from deleterious load (the ruby in the rubbish effect). Previous experiments confirm that sex can increase the rate of adaptation, but these studies did not observe the evolutionary dynamics that drive this effect at the genomic level. Here we present the first, to our knowledge, comparison between the sequence-level dynamics of adaptation in experimental sexual and asexual Saccharomyces cerevisiae populations, which allows us to identify the specific mechanisms by which sex speeds adaptation. We find that sex alters the molecular signatures of evolution by changing the spectrum of mutations that fix, and confirm theoretical predictions that it does so by alleviating clonal interference. We also show that substantially deleterious mutations hitchhike to fixation in adapting asexual populations. In contrast, recombination prevents such mutations from fixing. Our results demonstrate that sex both speeds adaptation and alters its molecular signature by allowing natural selection to more efficiently sort beneficial from deleterious mutations.

The Dynamics of Vulnerability and Implications for Climate Change Adaptation: Lessons from Urban Water Management

Science.gov (United States)

Dilling, L.; Daly, M.; Travis, W.; Wilhelmi, O.; Klein, R.; Kenney, D.; Ray, A. J.; Miller, K.

2013-12-01

Recent reports and scholarship have suggested that adapting to current climate variability may represent a "no regrets" strategy for adapting to climate change. Filling "adaptation deficits" and other approaches that rely on addressing current vulnerabilities are of course helpful for responding to current climate variability, but we find here that they are not sufficient for adapting to climate change. First, following a comprehensive review and unique synthesis of the natural hazards and climate adaptation literatures, we advance six reasons why adapting to climate variability is not sufficient for adapting to climate change: 1) Vulnerability is different at different levels of exposure; 2) Coping with climate variability is not equivalent to adaptation to longer term change; 3) The socioeconomic context for vulnerability is constantly changing; 4) The perception of risk associated with climate variability does not necessarily promote adaptive behavior in the face of climate change; 5) Adaptations made to short term climate variability may reduce the flexibility of the system in the long term; and 6) Adaptive actions may shift vulnerabilities to other parts of the system or to other people. Instead we suggest that decision makers faced with choices to adapt to climate change must consider the dynamics of vulnerability in a connected system-- how choices made in one part of the system might impact other valued outcomes or even create new vulnerabilities. Furthermore we suggest that rather than expressing climate change adaptation as an extension of adaptation to climate variability, the research and practice communities would do well to articulate adaptation as an imperfect policy, with tradeoffs and consequences and that decisions be prioritized to preserve flexibility be revisited often as climate change unfolds. We then present the results of a number of empirical studies of decision making for drought in urban water systems in the United States to understand

Dynamic Socio-technical System Design based on Stakeholder Interaction

Directory of Open Access Journals (Sweden)

Albert Fleischmann

2015-07-01

Full Text Available In order to directly involve stakeholders in socio-technical system design, we argue for streamlining executable process specifications with business process modeling. Due to current agility requirements of organizations, socio-technical system development is considered one of the key activities of members of the organizations. Dynamic process adaptation enable handling the volatility of business operation and IT infrastructure. Subject-oriented process representations are key enablers to dynamic adaptation due to their capability for stakeholders to create directly executable models. In this way stakeholder can be involved in change management pro-actively. Subject-oriented models (i represent all relevant features required for system control and decision making, and (ii are executable on demand. This effectiveness enables organizational change in a creative and efficient way, while establishing innovative design and change management tools. Subject-oriented Business Process Management capabilities are reflected in this realm revealing benefits and potential for further research.

Anti-Synchronization of Chaotic Systems via Adaptive Sliding Mode Control

International Nuclear Information System (INIS)

Jawaada, Wafaa; Noorani, M. S. M.; Al-Sawalha, M. Mossa

2012-01-01

An anti-synchronization scheme is proposed to achieve the anti-synchronization behavior between chaotic systems with fully unknown parameters. A sliding surface and an adaptive sliding mode controller are designed to gain the anti-synchronization. The stability of the error dynamics is proven theoretically using the Lyapunov stability theory. Finally numerical results are presented to justify the theoretical analysis

Feasibility of the adaptive and automatic presentation of tasks (ADAPT system for rehabilitation of upper extremity function post-stroke

Directory of Open Access Journals (Sweden)

Choi Younggeun

2011-08-01

Full Text Available Abstract Background Current guidelines for rehabilitation of arm and hand function after stroke recommend that motor training focus on realistic tasks that require reaching and manipulation and engage the patient intensively, actively, and adaptively. Here, we investigated the feasibility of a novel robotic task-practice system, ADAPT, designed in accordance with such guidelines. At each trial, ADAPT selects a functional task according to a training schedule and with difficulty based on previous performance. Once the task is selected, the robot picks up and presents the corresponding tool, simulates the dynamics of the tasks, and the patient interacts with the tool to perform the task. Methods Five participants with chronic stroke with mild to moderate impairments (> 9 months post-stroke; Fugl-Meyer arm score 49.2 ± 5.6 practiced four functional tasks (selected out of six in a pre-test with ADAPT for about one and half hour and 144 trials in a pseudo-random schedule of 3-trial blocks per task. Results No adverse events occurred and ADAPT successfully presented the six functional tasks without human intervention for a total of 900 trials. Qualitative analysis of trajectories showed that ADAPT simulated the desired task dynamics adequately, and participants reported good, although not excellent, task fidelity. During training, the adaptive difficulty algorithm progressively increased task difficulty leading towards an optimal challenge point based on performance; difficulty was then continuously adjusted to keep performance around the challenge point. Furthermore, the time to complete all trained tasks decreased significantly from pretest to one-hour post-test. Finally, post-training questionnaires demonstrated positive patient acceptance of ADAPT. Conclusions ADAPT successfully provided adaptive progressive training for multiple functional tasks based on participant's performance. Our encouraging results establish the feasibility of ADAPT; its

A Near-Hover Adaptive Attitude Control Strategy of a Ducted Fan Micro Aerial Vehicle with Actuator Dynamics

Directory of Open Access Journals (Sweden)

Shouzhao Sheng

2015-09-01

Full Text Available The aerodynamic parameters of ducted fan micro aerial vehicles (MAVs are difficult and expensive to precisely measure and are, therefore, not available in most cases. Furthermore, the actuator dynamics with risks of potentially destabilizing the overall system are important but often neglected consideration factors in the control system design of ducted fan MAVs. This paper presents a near-hover adaptive attitude control strategy of a prototype ducted fan MAV with actuator dynamics and without any prior information about the behavior of the MAV. The proposed strategy consists of an online parameter estimation algorithm and an adaptive gain scheduling algorithm, with the former accommodating parametric uncertainties, and the latter approximately eliminating the coupling among axes and guaranteeing the control quality of the MAV. The effectiveness of the proposed strategy is verified numerically and experimentally.

Dynamic Difficulty Adaptation for Heterogeneously Skilled Player Groups in Multiplayer Collaborative Games

OpenAIRE

Greciano, Miguel Cristian

2016-01-01

This work focuses on the combination of two key concepts: Dynamic Difficulty Adjustment/Adaptation (video games adapting their difficulty according to the in-game performance of players, making themselves easier if the player performs poorly or more difficult if the player performs well) and Collaborative Multiplayer Games (video games where two or more human players work together to achieve a common goal). It considers and analyzes the challenges, potential and possibilities of Dynamic Diffi...

DyNAvectors: dynamic constitutional vectors for adaptive DNA transfection.

Science.gov (United States)

Clima, Lilia; Peptanariu, Dragos; Pinteala, Mariana; Salic, Adrian; Barboiu, Mihail

2015-12-25

Dynamic constitutional frameworks, based on squalene, PEG and PEI components, reversibly connected to core centers, allow the efficient identification of adaptive vectors for good DNA transfection efficiency and are well tolerated by mammalian cells.

Strategic Adaptation

DEFF Research Database (Denmark)

Andersen, Torben Juul

2015-01-01

This article provides an overview of theoretical contributions that have influenced the discourse around strategic adaptation including contingency perspectives, strategic fit reasoning, decision structure, information processing, corporate entrepreneurship, and strategy process. The related...... concepts of strategic renewal, dynamic managerial capabilities, dynamic capabilities, and strategic response capabilities are discussed and contextualized against strategic responsiveness. The insights derived from this article are used to outline the contours of a dynamic process of strategic adaptation....... This model incorporates elements of central strategizing, autonomous entrepreneurial behavior, interactive information processing, and open communication systems that enhance the organization's ability to observe exogenous changes and respond effectively to them....

A Dynamic and Adaptive Selection Radar Tracking Method Based on Information Entropy

Directory of Open Access Journals (Sweden)

Ge Jianjun

2017-12-01

Full Text Available Nowadays, the battlefield environment has become much more complex and variable. This paper presents a quantitative method and lower bound for the amount of target information acquired from multiple radar observations to adaptively and dynamically organize the detection of battlefield resources based on the principle of information entropy. Furthermore, for minimizing the given information entropy’s lower bound for target measurement at every moment, a method to dynamically and adaptively select radars with a high amount of information for target tracking is proposed. The simulation results indicate that the proposed method has higher tracking accuracy than that of tracking without adaptive radar selection based on entropy.

Variable threshold algorithm for division of labor analyzed as a dynamical system.

Science.gov (United States)

Castillo-Cagigal, Manuel; Matallanas, Eduardo; Navarro, Iñaki; Caamaño-Martín, Estefanía; Monasterio-Huelin, Félix; Gutiérrez, Álvaro

2014-12-01

Division of labor is a widely studied aspect of colony behavior of social insects. Division of labor models indicate how individuals distribute themselves in order to perform different tasks simultaneously. However, models that study division of labor from a dynamical system point of view cannot be found in the literature. In this paper, we define a division of labor model as a discrete-time dynamical system, in order to study the equilibrium points and their properties related to convergence and stability. By making use of this analytical model, an adaptive algorithm based on division of labor can be designed to satisfy dynamic criteria. In this way, we have designed and tested an algorithm that varies the response thresholds in order to modify the dynamic behavior of the system. This behavior modification allows the system to adapt to specific environmental and collective situations, making the algorithm a good candidate for distributed control applications. The variable threshold algorithm is based on specialization mechanisms. It is able to achieve an asymptotically stable behavior of the system in different environments and independently of the number of individuals. The algorithm has been successfully tested under several initial conditions and number of individuals.

Emergence, institutionalization and renewal: Rhythms of adaptive governance in complex social-ecological systems.

Science.gov (United States)

Chaffin, Brian C; Gunderson, Lance H

2016-01-01

Adaptive governance provides the capacity for environmental managers and decision makers to confront variable degrees of uncertainty inherent to complex social-ecological systems. Current theoretical conceptualizations of adaptive governance represent a series of structures and processes best suited for either adapting or transforming existing environmental governance regimes towards forms flexible enough to confront rapid ecological change. As the number of empirical examples of adaptive governance described in the literature grows, the conceptual basis of adaptive governance remains largely under theorized. We argue that reconnecting adaptive governance with foundational concepts of ecological resilience-specifically Panarchy and the adaptive cycle of complex systems-highlights the importance of episodic disturbances and cross-scale interactions in triggering reorganizations in governance. By envisioning the processes of adaptive governance through the lens of Panarchy, scholars and practitioners alike will be better able to identify the emergence of adaptive governance, as well as take advantage of opportunities to institutionalize this type of governance in pursuit of sustainability outcomes. The synergistic analysis of adaptive governance and Panarchy can provide critical insight for analyzing the role of social dynamics during oscillating periods of stability and instability in social-ecological systems. A deeper understanding of the potential for cross-scale interactions to shape adaptive governance regimes may be useful as society faces the challenge of mitigating the impacts of global environmental change. Copyright © 2015 Elsevier Ltd. All rights reserved.

Imperialist Competitive Algorithm with Dynamic Parameter Adaptation Using Fuzzy Logic Applied to the Optimization of Mathematical Functions

Directory of Open Access Journals (Sweden)

Emer Bernal

2017-01-01

Full Text Available In this paper we are presenting a method using fuzzy logic for dynamic parameter adaptation in the imperialist competitive algorithm, which is usually known by its acronym ICA. The ICA algorithm was initially studied in its original form to find out how it works and what parameters have more effect upon its results. Based on this study, several designs of fuzzy systems for dynamic adjustment of the ICA parameters are proposed. The experiments were performed on the basis of solving complex optimization problems, particularly applied to benchmark mathematical functions. A comparison of the original imperialist competitive algorithm and our proposed fuzzy imperialist competitive algorithm was performed. In addition, the fuzzy ICA was compared with another metaheuristic using a statistical test to measure the advantage of the proposed fuzzy approach for dynamic parameter adaptation.

Neural network-based model reference adaptive control system.

Science.gov (United States)

Patino, H D; Liu, D

2000-01-01

In this paper, an approach to model reference adaptive control based on neural networks is proposed and analyzed for a class of first-order continuous-time nonlinear dynamical systems. The controller structure can employ either a radial basis function network or a feedforward neural network to compensate adaptively the nonlinearities in the plant. A stable controller-parameter adjustment mechanism, which is determined using the Lyapunov theory, is constructed using a sigma-modification-type updating law. The evaluation of control error in terms of the neural network learning error is performed. That is, the control error converges asymptotically to a neighborhood of zero, whose size is evaluated and depends on the approximation error of the neural network. In the design and analysis of neural network-based control systems, it is important to take into account the neural network learning error and its influence on the control error of the plant. Simulation results showing the feasibility and performance of the proposed approach are given.

Market mood, adaptive beliefs and asset price dynamics

International Nuclear Information System (INIS)

Dieci, Roberto; Foroni, Ilaria; Gardini, Laura; He Xuezhong

2006-01-01

Empirical evidence has suggested that, facing different trading strategies and complicated decision, the proportions of agents relying on particular strategies may stay at constant level or vary over time. This paper presents a simple 'dynamic market fraction' model of two groups of traders, fundamentalists and trend followers, under a market maker scenario. Market mood and evolutionary adaption are characterized by fixed and adaptive switching fraction among two groups, respectively. Using local stability and bifurcation analysis, as well as numerical simulation, the role played by the key parameters in the market behaviour is examined. Particular attention is paid to the impact of the market fraction, determined by the fixed proportions of confident fundamentalists and trend followers, and by the proportion of adaptively rational agents, who adopt different strategies over time depending on realized profits

Perspectives on Adaptivity in Information Retrieval Interaction (PAIRI)

DEFF Research Database (Denmark)

Ingwersen, Peter; Larsen, Birger; Kelly, Diane

2010-01-01

Adaptivity in IR interactions requires the IR systems adapting to users’ situations and the users adapting to the systems. System adaption entails dynamic user modeling, effective information architecture and enhanced search features such as search integration and relevance feedback; user adaptat...

Hybrid Adaptive Flight Control with Model Inversion Adaptation

Science.gov (United States)

Nguyen, Nhan

2011-01-01

This study investigates a hybrid adaptive flight control method as a design possibility for a flight control system that can enable an effective adaptation strategy to deal with off-nominal flight conditions. The hybrid adaptive control blends both direct and indirect adaptive control in a model inversion flight control architecture. The blending of both direct and indirect adaptive control provides a much more flexible and effective adaptive flight control architecture than that with either direct or indirect adaptive control alone. The indirect adaptive control is used to update the model inversion controller by an on-line parameter estimation of uncertain plant dynamics based on two methods. The first parameter estimation method is an indirect adaptive law based on the Lyapunov theory, and the second method is a recursive least-squares indirect adaptive law. The model inversion controller is therefore made to adapt to changes in the plant dynamics due to uncertainty. As a result, the modeling error is reduced that directly leads to a decrease in the tracking error. In conjunction with the indirect adaptive control that updates the model inversion controller, a direct adaptive control is implemented as an augmented command to further reduce any residual tracking error that is not entirely eliminated by the indirect adaptive control.

Designing Dynamic Adaptive Policy Pathways using Many-Objective Robust Decision Making

Science.gov (United States)

Kwakkel, Jan; Haasnoot, Marjolijn

2017-04-01

Dealing with climate risks in water management requires confronting a wide variety of deeply uncertain factors, while navigating a many dimensional space of trade-offs amongst objectives. There is an emerging body of literature on supporting this type of decision problem, under the label of decision making under deep uncertainty. Two approaches within this literature are Many-Objective Robust Decision Making, and Dynamic Adaptive Policy Pathways. In recent work, these approaches have been compared. One of the main conclusions of this comparison was that they are highly complementary. Many-Objective Robust Decision Making is a model based decision support approach, while Dynamic Adaptive Policy Pathways is primarily a conceptual framework for the design of flexible strategies that can be adapted over time in response to how the future is actually unfolding. In this research we explore this complementarity in more detail. Specifically, we demonstrate how Many-Objective Robust Decision Making can be used to design adaptation pathways. We demonstrate this combined approach using a water management problem, in the Netherlands. The water level of Lake IJselmeer, the main fresh water resource of the Netherlands, is currently managed through discharge by gravity. Due to climate change, this won't be possible in the future, unless water levels are changed. Changing the water level has undesirable flood risk and spatial planning consequences. The challenge is to find promising adaptation pathways that balance objectives related to fresh water supply, flood risk, and spatial issues, while accounting for uncertain climatic and land use change. We conclude that the combination of Many-Objective Robust Decision Making and Dynamic Adaptive Policy Pathways is particularly suited for dealing with deeply uncertain climate risks.

Probabilistic DHP adaptive critic for nonlinear stochastic control systems.

Science.gov (United States)

Herzallah, Randa

2013-06-01

Following the recently developed algorithms for fully probabilistic control design for general dynamic stochastic systems (Herzallah & Káarnáy, 2011; Kárný, 1996), this paper presents the solution to the probabilistic dual heuristic programming (DHP) adaptive critic method (Herzallah & Káarnáy, 2011) and randomized control algorithm for stochastic nonlinear dynamical systems. The purpose of the randomized control input design is to make the joint probability density function of the closed loop system as close as possible to a predetermined ideal joint probability density function. This paper completes the previous work (Herzallah & Káarnáy, 2011; Kárný, 1996) by formulating and solving the fully probabilistic control design problem on the more general case of nonlinear stochastic discrete time systems. A simulated example is used to demonstrate the use of the algorithm and encouraging results have been obtained. Copyright © 2013 Elsevier Ltd. All rights reserved.

Generalized reconfigurable memristive dynamical system (MDS) for neuromorphic applications.

Science.gov (United States)

Bavandpour, Mohammad; Soleimani, Hamid; Linares-Barranco, Bernabé; Abbott, Derek; Chua, Leon O

2015-01-01

This study firstly presents (i) a novel general cellular mapping scheme for two dimensional neuromorphic dynamical systems such as bio-inspired neuron models, and (ii) an efficient mixed analog-digital circuit, which can be conveniently implemented on a hybrid memristor-crossbar/CMOS platform, for hardware implementation of the scheme. This approach employs 4n memristors and no switch for implementing an n-cell system in comparison with 2n (2) memristors and 2n switches of a Cellular Memristive Dynamical System (CMDS). Moreover, this approach allows for dynamical variables with both analog and one-hot digital values opening a wide range of choices for interconnections and networking schemes. Dynamical response analyses show that this circuit exhibits various responses based on the underlying bifurcation scenarios which determine the main characteristics of the neuromorphic dynamical systems. Due to high programmability of the circuit, it can be applied to a variety of learning systems, real-time applications, and analytically indescribable dynamical systems. We simulate the FitzHugh-Nagumo (FHN), Adaptive Exponential (AdEx) integrate and fire, and Izhikevich neuron models on our platform, and investigate the dynamical behaviors of these circuits as case studies. Moreover, error analysis shows that our approach is suitably accurate. We also develop a simple hardware prototype for experimental demonstration of our approach.

Turbine system and adapter

Science.gov (United States)

Hogberg, Nicholas Alvin; Garcia-Crespo, Andres Jose

2017-05-30

A turbine system and adapter are disclosed. The adapter includes a turbine attachment portion having a first geometry arranged to receive a corresponding geometry of a wheelpost of a turbine rotor, and a bucket attachment portion having a second geometry arranged to receive a corresponding geometry of a root portion of a non-metallic turbine bucket. Another adapter includes a turbine attachment portion arranged to receive a plurality of wheelposts of a turbine rotor, and a bucket attachment portion arranged to receive a plurality of non-metallic turbine buckets having single dovetail configuration root portions. The turbine system includes a turbine rotor wheel configured to receive metal buckets, at least one adapter secured to at least one wheelpost on the turbine rotor wheel, and at least one non-metallic bucket secured to the at least one adapter.

Dynamic modeling and adaptive vibration suppression of a high-speed macro-micro manipulator

Science.gov (United States)

Yang, Yi-ling; Wei, Yan-ding; Lou, Jun-qiang; Fu, Lei; Fang, Sheng; Chen, Te-huan

2018-05-01

This paper presents a dynamic modeling and microscopic vibration suppression for a flexible macro-micro manipulator dedicated to high-speed operation. The manipulator system mainly consists of a macro motion stage and a flexible micromanipulator bonded with one macro-fiber-composite actuator. Based on Hamilton's principle and the Bouc-Wen hysteresis equation, the nonlinear dynamic model is obtained. Then, a hybrid control scheme is proposed to simultaneously suppress the elastic vibration during and after the motor motion. In particular, the hybrid control strategy is composed of a trajectory planning approach and an adaptive variable structure control. Moreover, two optimization indices regarding the comprehensive torques and synthesized vibrations are designed, and the optimal trajectories are acquired using a genetic algorithm. Furthermore, a nonlinear fuzzy regulator is used to adjust the switching gain in the variable structure control. Thus, a fuzzy variable structure control with nonlinear adaptive control law is achieved. A series of experiments are performed to verify the effectiveness and feasibility of the established system model and hybrid control strategy. The excited vibration during the motor motion and the residual vibration after the motor motion are decreased. Meanwhile, the settling time is shortened. Both the manipulation stability and operation efficiency of the manipulator are improved by the proposed hybrid strategy.

Linear hypergeneralization of learned dynamics across movement speeds reveals anisotropic, gain-encoding primitives for motor adaptation.

Science.gov (United States)

Joiner, Wilsaan M; Ajayi, Obafunso; Sing, Gary C; Smith, Maurice A

2011-01-01

The ability to generalize learned motor actions to new contexts is a key feature of the motor system. For example, the ability to ride a bicycle or swing a racket is often first developed at lower speeds and later applied to faster velocities. A number of previous studies have examined the generalization of motor adaptation across movement directions and found that the learned adaptation decays in a pattern consistent with the existence of motor primitives that display narrow Gaussian tuning. However, few studies have examined the generalization of motor adaptation across movement speeds. Following adaptation to linear velocity-dependent dynamics during point-to-point reaching arm movements at one speed, we tested the ability of subjects to transfer this adaptation to short-duration higher-speed movements aimed at the same target. We found near-perfect linear extrapolation of the trained adaptation with respect to both the magnitude and the time course of the velocity profiles associated with the high-speed movements: a 69% increase in movement speed corresponded to a 74% extrapolation of the trained adaptation. The close match between the increase in movement speed and the corresponding increase in adaptation beyond what was trained indicates linear hypergeneralization. Computational modeling shows that this pattern of linear hypergeneralization across movement speeds is not compatible with previous models of adaptation in which motor primitives display isotropic Gaussian tuning of motor output around their preferred velocities. Instead, we show that this generalization pattern indicates that the primitives involved in the adaptation to viscous dynamics display anisotropic tuning in velocity space and encode the gain between motor output and motion state rather than motor output itself.

Risk-return relationship in a complex adaptive system.

Directory of Open Access Journals (Sweden)

Kunyu Song

Full Text Available For survival and development, autonomous agents in complex adaptive systems involving the human society must compete against or collaborate with others for sharing limited resources or wealth, by using different methods. One method is to invest, in order to obtain payoffs with risk. It is a common belief that investments with a positive risk-return relationship (namely, high risk high return and vice versa are dominant over those with a negative risk-return relationship (i.e., high risk low return and vice versa in the human society; the belief has a notable impact on daily investing activities of investors. Here we investigate the risk-return relationship in a model complex adaptive system, in order to study the effect of both market efficiency and closeness that exist in the human society and play an important role in helping to establish traditional finance/economics theories. We conduct a series of computer-aided human experiments, and also perform agent-based simulations and theoretical analysis to confirm the experimental observations and reveal the underlying mechanism. We report that investments with a negative risk-return relationship have dominance over those with a positive risk-return relationship instead in such a complex adaptive systems. We formulate the dynamical process for the system's evolution, which helps to discover the different role of identical and heterogeneous preferences. This work might be valuable not only to complexity science, but also to finance and economics, to management and social science, and to physics.

Risk-return relationship in a complex adaptive system.

Science.gov (United States)

Song, Kunyu; An, Kenan; Yang, Guang; Huang, Jiping

2012-01-01

For survival and development, autonomous agents in complex adaptive systems involving the human society must compete against or collaborate with others for sharing limited resources or wealth, by using different methods. One method is to invest, in order to obtain payoffs with risk. It is a common belief that investments with a positive risk-return relationship (namely, high risk high return and vice versa) are dominant over those with a negative risk-return relationship (i.e., high risk low return and vice versa) in the human society; the belief has a notable impact on daily investing activities of investors. Here we investigate the risk-return relationship in a model complex adaptive system, in order to study the effect of both market efficiency and closeness that exist in the human society and play an important role in helping to establish traditional finance/economics theories. We conduct a series of computer-aided human experiments, and also perform agent-based simulations and theoretical analysis to confirm the experimental observations and reveal the underlying mechanism. We report that investments with a negative risk-return relationship have dominance over those with a positive risk-return relationship instead in such a complex adaptive systems. We formulate the dynamical process for the system's evolution, which helps to discover the different role of identical and heterogeneous preferences. This work might be valuable not only to complexity science, but also to finance and economics, to management and social science, and to physics.

Generic adaptation framework for unifying adaptive web-based systems

NARCIS (Netherlands)

Knutov, E.

2012-01-01

The Generic Adaptation Framework (GAF) research project first and foremost creates a common formal framework for describing current and future adaptive hypermedia (AHS) and adaptive webbased systems in general. It provides a commonly agreed upon taxonomy and a reference model that encompasses the

Nonlinear dynamical systems for theory and research in ergonomics.

Science.gov (United States)

Guastello, Stephen J

2017-02-01

Nonlinear dynamical systems (NDS) theory offers new constructs, methods and explanations for phenomena that have in turn produced new paradigms of thinking within several disciplines of the behavioural sciences. This article explores the recent developments of NDS as a paradigm in ergonomics. The exposition includes its basic axioms, the primary constructs from elementary dynamics and so-called complexity theory, an overview of its methods, and growing areas of application within ergonomics. The applications considered here include: psychophysics, iconic displays, control theory, cognitive workload and fatigue, occupational accidents, resilience of systems, team coordination and synchronisation in systems. Although these applications make use of different subsets of NDS constructs, several of them share the general principles of the complex adaptive system. Practitioner Summary: Nonlinear dynamical systems theory reframes problems in ergonomics that involve complex systems as they change over time. The leading applications to date include psychophysics, control theory, cognitive workload and fatigue, biomechanics, occupational accidents, resilience of systems, team coordination and synchronisation of system components.

Challenges in Quantitative Abstractions for Collective Adaptive Systems

Directory of Open Access Journals (Sweden)

Mirco Tribastone

2016-07-01

Full Text Available Like with most large-scale systems, the evaluation of quantitative properties of collective adaptive systems is an important issue that crosscuts all its development stages, from design (in the case of engineered systems to runtime monitoring and control. Unfortunately it is a difficult problem to tackle in general, due to the typically high computational cost involved in the analysis. This calls for the development of appropriate quantitative abstraction techniques that preserve most of the system's dynamical behaviour using a more compact representation. This paper focuses on models based on ordinary differential equations and reviews recent results where abstraction is achieved by aggregation of variables, reflecting on the shortcomings in the state of the art and setting out challenges for future research.

The use of system dynamics for EROI simulation

DEFF Research Database (Denmark)

Atlason, Reynir Smari

to construct a systems dynamics model to represent a geothermal power plant and calculate the EROI3,i. The benefits of such models are their simplicity, and simulation power. The system simulated is adapted from Atlason et al. (2013) where the EROI for the Nesjavellir geothermal power plant was calculated....... The systems dynamics model essentially provides other researchers with a clear demonstration of inputs, outputs and assumptions used in the calculations. I propose, that EROI studies are supplemented with such models for clarity....... along with publications where inputs and outputs from energy systems are shown, but that is seldom or ever the case. Doing so would allow other researchers to see if energy systems or studies are actually comparable and if inputs, outputs and assumptions are the same. In this study, I demonstrate how...

Cross-Layer Adaptive Feedback Scheduling of Wireless Control Systems

Science.gov (United States)

Xia, Feng; Ma, Longhua; Peng, Chen; Sun, Youxian; Dong, Jinxiang

2008-01-01

There is a trend towards using wireless technologies in networked control systems. However, the adverse properties of the radio channels make it difficult to design and implement control systems in wireless environments. To attack the uncertainty in available communication resources in wireless control systems closed over WLAN, a cross-layer adaptive feedback scheduling (CLAFS) scheme is developed, which takes advantage of the co-design of control and wireless communications. By exploiting cross-layer design, CLAFS adjusts the sampling periods of control systems at the application layer based on information about deadline miss ratio and transmission rate from the physical layer. Within the framework of feedback scheduling, the control performance is maximized through controlling the deadline miss ratio. Key design parameters of the feedback scheduler are adapted to dynamic changes in the channel condition. An event-driven invocation mechanism for the feedback scheduler is also developed. Simulation results show that the proposed approach is efficient in dealing with channel capacity variations and noise interference, thus providing an enabling technology for control over WLAN. PMID:27879934

Cross-Layer Adaptive Feedback Scheduling of Wireless Control Systems

Directory of Open Access Journals (Sweden)

Jinxiang Dong

2008-07-01

Full Text Available There is a trend towards using wireless technologies in networked control systems. However, the adverse properties of the radio channels make it difficult to design and implement control systems in wireless environments. To attack the uncertainty in available communication resources in wireless control systems closed over WLAN, a cross-layer adaptive feedback scheduling (CLAFS scheme is developed, which takes advantage of the co-design of control and wireless communications. By exploiting crosslayer design, CLAFS adjusts the sampling periods of control systems at the application layer based on information about deadline miss ratio and transmission rate from the physical layer. Within the framework of feedback scheduling, the control performance is maximized through controlling the deadline miss ratio. Key design parameters of the feedback scheduler are adapted to dynamic changes in the channel condition. An eventdriven invocation mechanism for the feedback scheduler is also developed. Simulation results show that the proposed approach is efficient in dealing with channel capacity variations and noise interference, thus providing an enabling technology for control over WLAN.

A Self-adaptive Dynamic Evaluation Model for Diabetes Mellitus, Based on Evolutionary Strategies

Directory of Open Access Journals (Sweden)

An-Jiang Lu

2016-03-01

Full Text Available In order to evaluate diabetes mellitus objectively and accurately, this paper builds a self-adaptive dynamic evaluation model for diabetes mellitus, based on evolutionary strategies. First of all, on the basis of a formalized description of the evolutionary process of diabetes syndromes, using a state transition function, it judges whether a disease is evolutionary, through an excitation parameter. It then, provides evidence for the rebuilding of the evaluation index system. After that, by abstracting and rebuilding the composition of evaluation indexes, it makes use of a heuristic algorithm to determine the composition of the evolved evaluation index set of diabetes mellitus, It then, calculates the weight of each index in the evolved evaluation index set of diabetes mellitus by building a dependency matrix and realizes the self-adaptive dynamic evaluation of diabetes mellitus under an evolutionary environment. Using this evaluation model, it is possible to, quantify all kinds of diagnoses and treatment experiences of diabetes and finally to adopt ideal diagnoses and treatment measures for different patients with diabetics.

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.

Recruitment dynamics in adaptive social networks

Science.gov (United States)

Shkarayev, Maxim S.; Schwartz, Ira B.; Shaw, Leah B.

2013-06-01

We model recruitment in adaptive social networks in the presence of birth and death processes. Recruitment is characterized by nodes changing their status to that of the recruiting class as a result of contact with recruiting nodes. Only a susceptible subset of nodes can be recruited. The recruiting individuals may adapt their connections in order to improve recruitment capabilities, thus changing the network structure adaptively. We derive a mean-field theory to predict the dependence of the growth threshold of the recruiting class on the adaptation parameter. Furthermore, we investigate the effect of adaptation on the recruitment level, as well as on network topology. The theoretical predictions are compared with direct simulations of the full system. We identify two parameter regimes with qualitatively different bifurcation diagrams depending on whether nodes become susceptible frequently (multiple times in their lifetime) or rarely (much less than once per lifetime).

Recruitment dynamics in adaptive social networks

International Nuclear Information System (INIS)

Shkarayev, Maxim S; Shaw, Leah B; Schwartz, Ira B

2013-01-01

We model recruitment in adaptive social networks in the presence of birth and death processes. Recruitment is characterized by nodes changing their status to that of the recruiting class as a result of contact with recruiting nodes. Only a susceptible subset of nodes can be recruited. The recruiting individuals may adapt their connections in order to improve recruitment capabilities, thus changing the network structure adaptively. We derive a mean-field theory to predict the dependence of the growth threshold of the recruiting class on the adaptation parameter. Furthermore, we investigate the effect of adaptation on the recruitment level, as well as on network topology. The theoretical predictions are compared with direct simulations of the full system. We identify two parameter regimes with qualitatively different bifurcation diagrams depending on whether nodes become susceptible frequently (multiple times in their lifetime) or rarely (much less than once per lifetime). (paper)

Complex Adaptive Systems of Systems (CASOS) engineering environment.

Energy Technology Data Exchange (ETDEWEB)

Detry, Richard Joseph; Linebarger, John Michael; Finley, Patrick D.; Maffitt, S. Louise; Glass, Robert John, Jr.; Beyeler, Walter Eugene; Ames, Arlo Leroy

2012-02-01

Complex Adaptive Systems of Systems, or CASoS, are vastly complex physical-socio-technical systems which we must understand to design a secure future for the nation. The Phoenix initiative implements CASoS Engineering principles combining the bottom up Complex Systems and Complex Adaptive Systems view with the top down Systems Engineering and System-of-Systems view. CASoS Engineering theory and practice must be conducted together to develop a discipline that is grounded in reality, extends our understanding of how CASoS behave and allows us to better control the outcomes. The pull of applications (real world problems) is critical to this effort, as is the articulation of a CASoS Engineering Framework that grounds an engineering approach in the theory of complex adaptive systems of systems. Successful application of the CASoS Engineering Framework requires modeling, simulation and analysis (MS and A) capabilities and the cultivation of a CASoS Engineering Community of Practice through knowledge sharing and facilitation. The CASoS Engineering Environment, itself a complex adaptive system of systems, constitutes the two platforms that provide these capabilities.

Blind spots of dynamic capabilities: A systems theoretic perspective

Directory of Open Access Journals (Sweden)

Robert Burisch

2016-05-01

Full Text Available Dynamic capabilities remain one of the most popular, but also one of the most controversial topics in current knowledge and innovation research. This study exposes strengths and weaknesses of existing conceptualizations of dynamic capabilities by using a systems theoretic lens. Systems theory suggests that organizations operate in environments they cannot fully understand. Thus, organizational action patterns inevitably involve simplification, selectivity and uncertainty leading to inherent blind spots in every kind of strategic action. As the resulting insight, fully flexible organizational capabilities might not be achievable and continuous adaptation to every kind of environmental change cannot be possible from a systems theoretic perspective. Accordingly, this work discusses empirical difficulties that derive from the preceding argumentation and outlines a corresponding re-conceptualization of the dynamic capabilities concept.

A predator-2 prey fast-slow dynamical system for rapid predator evolution

DEFF Research Database (Denmark)

Piltz, Sofia Helena; Veerman, Frits; Maini, Philip K.

2017-01-01

We consider adaptive change of diet of a predator population that switches its feeding between two prey populations. We develop a novel 1 fast-3 slow dynamical system to describe the dynamics of the three populations amidst continuous but rapid evolution of the predator's diet choice. The two ext...

When do mixotrophs specialize? Adaptive dynamics theory applied to a dynamic energy budget model.

NARCIS (Netherlands)

Troost, T.A.; Kooi, B.W.; Kooijman, S.A.L.M.

2005-01-01

In evolutionary history, several events have occurred at which mixotrophs specialized into pure autotrophs and heterotrophs. We studied the conditions under which such events take place, using the Dynamic Energy Budget (DEB) theory for physiological rules of the organisms' metabolism and Adaptive

Discordant tasks and motor adjustments affect interactions between adaptations to altered kinematics and dynamics

Directory of Open Access Journals (Sweden)

Fritzie Arce

2010-01-01

Full Text Available Motor control and adaptation are multi-determinate processes with complex interactions. This is reflected for example in the ambiguous nature of interactions during sequential adaptation of reaching under kinematics and dynamics perturbations. It has been suggested that perturbations based on the same kinematic parameter interfere. Others posited that opposing motor adjustments underlie interference. Here, we examined the influence of discordances in task and in motor adjustments on sequential adaptations to visuomotor rotation and viscous force field perturbations. These two factors – perturbation direction and task discordance – have been examined separately by previous studies, thus the inherent difficulty to identify the roots of interference. Forty-eight human subjects adapted sequentially to one or two types of perturbations, of matched or conflicting directions. We found a gradient of interaction effects based on perturbation direction and task discordance. Perturbations of matched directions showed facilitation while perturbations of opposite directions, which required opposing motor adjustments, interfered with each other. Further, interaction effects increased with greater task discordance. We also found that force field and visuomotor rotation had mutual anterograde and retrograde effects. However, we found independence between anterograde and retrograde interferences between similar tasks. The results suggest that the newly acquired internal models of kinematic and dynamic perturbations are not independent but they share common neuronal resources and interact between them. Such overlap does not necessarily imply competition of resources. Rather, our results point to an additional principle of sensorimotor adaptation allowing the system to tap or harness common features across diverse sensory inputs and task contexts whenever available.

Adaptive dynamics on an environmental gradient that changes over a geological time-scale.

Science.gov (United States)

Fortelius, Mikael; Geritz, Stefan; Gyllenberg, Mats; Toivonen, Jaakko

2015-07-07

The standard adaptive dynamics framework assumes two timescales, i.e. fast population dynamics and slow evolutionary dynamics. We further assume a third timescale, which is even slower than the evolutionary timescale. We call this the geological timescale and we assume that slow climatic change occurs within this timescale. We study the evolution of our model population over this very slow geological timescale with bifurcation plots of the standard adaptive dynamics framework. The bifurcation parameter being varied describes the abiotic environment that changes over the geological timescale. We construct evolutionary trees over the geological timescale and observe both gradual phenotypic evolution and punctuated branching events. We concur with the established notion that branching of a monomorphic population on an environmental gradient only happens when the gradient is not too shallow and not too steep. However, we show that evolution within the habitat can produce polymorphic populations that inhabit steep gradients. What is necessary is that the environmental gradient at some point in time is such that the initial branching of the monomorphic population can occur. We also find that phenotypes adapted to environments in the middle of the existing environmental range are more likely to branch than phenotypes adapted to extreme environments. Copyright © 2015 Elsevier Ltd. All rights reserved.

Adaptive Nonsingular Terminal Sliding Model Control and Its Application to Permanent Magnet Synchronous Motor Drive System

OpenAIRE

Liu Yue; Zhou Shuo

2016-01-01

To improve the dynamic performance of permanent magnet synchronous motor(PMSM) drive system, a adaptive nonsingular terminal sliding model control((NTSMC) strategy was proposed. The proposed control strategy presents an adaptive variable-rated exponential reaching law which the L1 norm of state variables is introduced. Exponential and constant approach speed can adaptively adjust according to the state variables’ distance to the equilibrium position.The proposed scheme can shorten the reachin...

Problems of Aero-optics and Adaptive Optical Systems: Analytical Review

Directory of Open Access Journals (Sweden)

Yu. I. Shanin

2017-01-01

Full Text Available The analytical review gives the basic concepts of the aero-optics problem arising from the radiation propagation in the region of the boundary layers of a laser installation carrier aircraft. Estimates the radiation wave front distortions at its propagation in the near and far field. Presents main calculation approaches and methods to solve the gas-dynamic and optical problems in propagating laser radiation. Conducts a detailed analysis of the flows and their generating optical aberrations introduced by the aircraft turret (a projection platform of the on-board laser. Considers the effect of various factors (shock wave, difference in wall and flow temperatures on the flow pattern and the optical aberrations. Provides research data on the aero-optics obtained in the flying laboratory directly while in flight. Briefly considers the experimental research methods, diagnostic equipment, and synthesis of results while studying the aero-optics problem. Discusses some methods for mitigating the aerodynamic effects on the light propagation under flight conditions. Presents data about the passive, active, and hybrid effects on the flow in the boundary layers in order to reduce aberrations through improving the flow aerodynamics.The paper considers operation of adaptive optical systems under conditions of aero-optical distortions. Presents the study results concerning the reduction of the aero-optics effect on the characteristics of radiation in far field. Gives some research results regarding the effect on the efficiency of the adaptive system of a laser beam jitter and a time delay in the feedback signal transmission, which occur under application conditions. Provides data on adaptive correction of aero-optical wave fronts of radiation. Considers some application aspects in control systems of the on-board adaptive optics of adaptive filtration as a way to improve the efficiency of adaptive optical systems. The project in mind is to use obtained results

Dynamics of the stochastic Lorenz chaotic system with long memory effects

Energy Technology Data Exchange (ETDEWEB)

Zeng, Caibin, E-mail: zeng.cb@mail.scut.edu.cn; Yang, Qigui, E-mail: qgyang@scut.edu.cn [School of Mathematics, South China University of Technology, Guangzhou 510640 (China)

2015-12-15

Little seems to be known about the ergodic dynamics of stochastic systems with fractional noise. This paper is devoted to discern such long time dynamics through the stochastic Lorenz chaotic system (SLCS) with long memory effects. By a truncation technique, the SLCS is proved to generate a continuous stochastic dynamical system Λ. Based on the Krylov-Bogoliubov criterion, the required Lyapunov function is further established to ensure the existence of the invariant measure of Λ. Meanwhile, the uniqueness of the invariant measure of Λ is proved by examining the strong Feller property, together with an irreducibility argument. Therefore, the SLCS has exactly one adapted stationary solution.

Stabilizing periodic orbits of chaotic systems using fuzzy adaptive sliding mode control

Energy Technology Data Exchange (ETDEWEB)

Layeghi, Hamed [Department of Mechanical Engineering, Sharif University of Technology, Center of Excellence in Design, Robotics and Automation, Azadi Avenue, Postal Code 11365-9567 Tehran (Iran, Islamic Republic of)], E-mail: layeghi@mech.sharif.edu; Arjmand, Mehdi Tabe [Department of Mechanical Engineering, Sharif University of Technology, Center of Excellence in Design, Robotics and Automation, Azadi Avenue, Postal Code 11365-9567 Tehran (Iran, Islamic Republic of)], E-mail: arjmand@mech.sharif.edu; Salarieh, Hassan [Department of Mechanical Engineering, Sharif University of Technology, Center of Excellence in Design, Robotics and Automation, Azadi Avenue, Postal Code 11365-9567 Tehran (Iran, Islamic Republic of)], E-mail: salarieh@mech.sharif.edu; Alasty, Aria [Department of Mechanical Engineering, Sharif University of Technology, Center of Excellence in Design, Robotics and Automation, Azadi Avenue, Postal Code 11365-9567 Tehran (Iran, Islamic Republic of)], E-mail: aalasti@sharif.edu

2008-08-15

In this paper by using a combination of fuzzy identification and the sliding mode control a fuzzy adaptive sliding mode scheme is designed to stabilize the unstable periodic orbits of chaotic systems. The chaotic system is assumed to have an affine form x{sup (n)} = f(X) + g(X)u where f and g are unknown functions. Using only the input-output data obtained from the underlying dynamical system, two fuzzy systems are constructed for identification of f and g. Two distinct methods are utilized for fuzzy modeling, the least squares and the gradient descent techniques. Based on the estimated fuzzy models, an adaptive controller, which works through the sliding mode control, is designed to make the system track the desired unstable periodic orbits. The stability analysis of the overall closed loop system is presented in the paper and the effectiveness of the proposed adaptive scheme is numerically investigated. As a case of study, modified Duffing system is selected for applying the proposed method to stabilize its 2{pi} and 4{pi} periodic orbits. Simulation results show the high performance of the method for stabilizing the unstable periodic orbits of unknown chaotic systems.

Dynamic Adaptive Neural Network Arrays: A Neuromorphic Architecture

Energy Technology Data Exchange (ETDEWEB)

Disney, Adam [University of Tennessee (UT); Reynolds, John [University of Tennessee (UT)

2015-01-01

Dynamic Adaptive Neural Network Array (DANNA) is a neuromorphic hardware implementation. It differs from most other neuromorphic projects in that it allows for programmability of structure, and it is trained or designed using evolutionary optimization. This paper describes the DANNA structure, how DANNA is trained using evolutionary optimization, and an application of DANNA to a very simple classification task.

Patient-adapted reconstruction and acquisition dynamic imaging method (PARADIGM) for MRI

International Nuclear Information System (INIS)

Aggarwal, Nitin; Bresler, Yoram

2008-01-01

Dynamic magnetic resonance imaging (MRI) is a challenging problem because the MR data acquisition is often not fast enough to meet the combined spatial and temporal Nyquist sampling rate requirements. Current approaches to this problem include hardware-based acceleration of the acquisition, and model-based image reconstruction techniques. In this paper we propose an alternative approach, called PARADIGM, which adapts both the acquisition and reconstruction to the spatio-temporal characteristics of the imaged object. The approach is based on time-sequential sampling theory, addressing the problem of acquiring a spatio-temporal signal under the constraint that only a limited amount of data can be acquired at a time instant. PARADIGM identifies a model class for the particular imaged object using a scout MR scan or auxiliary data. This object-adapted model is then used to optimize MR data acquisition, such that the imaging constraints are met, acquisition speed requirements are minimized, essentially perfect reconstruction of any object in the model class is guaranteed, and the inverse problem of reconstructing the dynamic object has a condition number of one. We describe spatio-temporal object models for various dynamic imaging applications including cardiac imaging. We present the theory underlying PARADIGM and analyze its performance theoretically and numerically. We also propose a practical MR imaging scheme for 2D dynamic cardiac imaging based on the theory. For this application, PARADIGM is predicted to provide a 10–25 × acceleration compared to the optimal non-adaptive scheme. Finally we present generalized optimality criteria and extend the scheme to dynamic imaging with three spatial dimensions

Complex Nonlinear Dynamic System of Oligopolies Price Game with Heterogeneous Players Under Noise

Science.gov (United States)

Liu, Feng; Li, Yaguang

A nonlinear four oligopolies price game with heterogeneous players, that are boundedly rational and adaptive, is built using two different special demand costs. Based on the theory of complex discrete dynamical system, the stability and the existing equilibrium point are investigated. The complex dynamic behavior is presented via bifurcation diagrams, the Lyapunov exponents to show equilibrium state, bifurcation and chaos with the variation in parameters. As disturbance is ubiquitous in economic systems, this paper focuses on the analysis of delay feedback control method under noise circumstances. Stable dynamics is confirmed to depend mainly on the low price adjustment speed, and if all four players have limited opportunities to stabilize the market, the new adaptive player facing profits of scale are found to be higher than the incumbents of bounded rational.

Ant Systems for a Dynamic TSP - Ants Caught in a Traffic Jam

NARCIS (Netherlands)

Eyckelhof, C.J.; Dorigo, M.; Caro Di, G.; Snoek, M.; Sampels, M.

2002-01-01

In this paper we present a new Ants System approach to a dynamic Travelling Salesman Problem. Here the travel times between the cities are subject to change. To handle this dynamism several ways of adapting the pheromone matrix both locally and globally are considered. We show that the strategy of

Vehicle Sliding Mode Control with Adaptive Upper Bounds: Static versus Dynamic Allocation to Saturated Tire Forces

Directory of Open Access Journals (Sweden)

Ali Tavasoli

2012-01-01

Full Text Available Nonlinear vehicle control allocation is achieved through distributing the task of vehicle control among individual tire forces, which are constrained to nonlinear saturation conditions. A high-level sliding mode control with adaptive upper bounds is considered to assess the body yaw moment and lateral force for the vehicle motion. The proposed controller only requires the online adaptation of control gains without acquiring the knowledge of upper bounds on system uncertainties. Static and dynamic control allocation approaches have been formulated to distribute high-level control objectives among the system inputs. For static control allocation, the interior-point method is applied to solve the formulated nonlinear optimization problem. Based on the dynamic control allocation method, a dynamic update law is derived to allocate vehicle control to tire forces. The allocated tire forces are fed into a low-level control module, where the applied torque and active steering angle at each wheel are determined through a slip-ratio controller and an inverse tire model. Computer simulations are used to prove the significant effects of the proposed control allocation methods on improving the stability and handling performance. The advantages and limitations of each method have been discussed, and conclusions have been derived.

Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems.

Science.gov (United States)

Tseng, Chien-Hao; Lin, Sheng-Fuu; Jwo, Dah-Jing

2016-07-26

This paper presents a sensor fusion method based on the combination of cubature Kalman filter (CKF) and fuzzy logic adaptive system (FLAS) for the integrated navigation systems, such as the GPS/INS (Global Positioning System/inertial navigation system) integration. The third-degree spherical-radial cubature rule applied in the CKF has been employed to avoid the numerically instability in the system model. In processing navigation integration, the performance of nonlinear filter based estimation of the position and velocity states may severely degrade caused by modeling errors due to dynamics uncertainties of the vehicle. In order to resolve the shortcoming for selecting the process noise covariance through personal experience or numerical simulation, a scheme called the fuzzy adaptive cubature Kalman filter (FACKF) is presented by introducing the FLAS to adjust the weighting factor of the process noise covariance matrix. The FLAS is incorporated into the CKF framework as a mechanism for timely implementing the tuning of process noise covariance matrix based on the information of degree of divergence (DOD) parameter. The proposed FACKF algorithm shows promising accuracy improvement as compared to the extended Kalman filter (EKF), unscented Kalman filter (UKF), and CKF approaches.

Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems

Directory of Open Access Journals (Sweden)

Chien-Hao Tseng

2016-07-01

Full Text Available This paper presents a sensor fusion method based on the combination of cubature Kalman filter (CKF and fuzzy logic adaptive system (FLAS for the integrated navigation systems, such as the GPS/INS (Global Positioning System/inertial navigation system integration. The third-degree spherical-radial cubature rule applied in the CKF has been employed to avoid the numerically instability in the system model. In processing navigation integration, the performance of nonlinear filter based estimation of the position and velocity states may severely degrade caused by modeling errors due to dynamics uncertainties of the vehicle. In order to resolve the shortcoming for selecting the process noise covariance through personal experience or numerical simulation, a scheme called the fuzzy adaptive cubature Kalman filter (FACKF is presented by introducing the FLAS to adjust the weighting factor of the process noise covariance matrix. The FLAS is incorporated into the CKF framework as a mechanism for timely implementing the tuning of process noise covariance matrix based on the information of degree of divergence (DOD parameter. The proposed FACKF algorithm shows promising accuracy improvement as compared to the extended Kalman filter (EKF, unscented Kalman filter (UKF, and CKF approaches.

Evolution and Adaptation in Pseudomonas aeruginosa Biofilms Driven by Mismatch Repair System-Deficient Mutators

DEFF Research Database (Denmark)

Luján, Adela M.; Maciá, María D.; Yang, Liang

2011-01-01

, which are rarely eradicated despite intensive antibiotic therapy. Current knowledge indicates that three major adaptive strategies, biofilm development, phenotypic diversification, and mutator phenotypes [driven by a defective mismatch repair system (MRS)], play important roles in P. aeruginosa chronic...... infections, but the relationship between these strategies is still poorly understood. We have used the flow-cell biofilm model system to investigate the impact of the mutS associated mutator phenotype on development, dynamics, diversification and adaptation of P. aeruginosa biofilms. Through competition...... diversification, evidenced by biofilm architecture features and by a wider range and proportion of morphotypic colony variants, respectively. Additionally, morphotypic variants generated in mutator biofilms showed increased competitiveness, providing further evidence for mutator-driven adaptive evolution...

An Adaptive Learning Based Network Selection Approach for 5G Dynamic Environments

Directory of Open Access Journals (Sweden)

Xiaohong Li

2018-03-01

Full Text Available Networks will continue to become increasingly heterogeneous as we move toward 5G. Meanwhile, the intelligent programming of the core network makes the available radio resource be more changeable rather than static. In such a dynamic and heterogeneous network environment, how to help terminal users select optimal networks to access is challenging. Prior implementations of network selection are usually applicable for the environment with static radio resources, while they cannot handle the unpredictable dynamics in 5G network environments. To this end, this paper considers both the fluctuation of radio resources and the variation of user demand. We model the access network selection scenario as a multiagent coordination problem, in which a bunch of rationally terminal users compete to maximize their benefits with incomplete information about the environment (no prior knowledge of network resource and other users’ choices. Then, an adaptive learning based strategy is proposed, which enables users to adaptively adjust their selections in response to the gradually or abruptly changing environment. The system is experimentally shown to converge to Nash equilibrium, which also turns out to be both Pareto optimal and socially optimal. Extensive simulation results show that our approach achieves significantly better performance compared with two learning and non-learning based approaches in terms of load balancing, user payoff and the overall bandwidth utilization efficiency. In addition, the system has a good robustness performance under the condition with non-compliant terminal users.

Adaptive security systems -- Combining expert systems with adaptive technologies

International Nuclear Information System (INIS)

Argo, P.; Loveland, R.; Anderson, K.

1997-01-01

The Adaptive Multisensor Integrated Security System (AMISS) uses a variety of computational intelligence techniques to reason from raw sensor data through an array of processing layers to arrive at an assessment for alarm/alert conditions based on human behavior within a secure facility. In this paper, the authors give an overview of the system and briefly describe some of the major components of the system. This system is currently under development and testing in a realistic facility setting

Dynamic Extensions of Batch Systems with Cloud Resources

International Nuclear Information System (INIS)

Hauth, T; Quast, G; Büge, V; Scheurer, A; Kunze, M; Baun, C

2011-01-01

Compute clusters use Portable Batch Systems (PBS) to distribute workload among individual cluster machines. To extend standard batch systems to Cloud infrastructures, a new service monitors the number of queued jobs and keeps track of the price of available resources. This meta-scheduler dynamically adapts the number of Cloud worker nodes according to the requirement profile. Two different worker node topologies are presented and tested on the Amazon EC2 Cloud service.

The repertoire and dynamics of evolutionary adaptations to controlled nutrient-limited environments in yeast.

Directory of Open Access Journals (Sweden)

David Gresham

2008-12-01

Full Text Available The experimental evolution of laboratory populations of microbes provides an opportunity to observe the evolutionary dynamics of adaptation in real time. Until very recently, however, such studies have been limited by our inability to systematically find mutations in evolved organisms. We overcome this limitation by using a variety of DNA microarray-based techniques to characterize genetic changes -- including point mutations, structural changes, and insertion variation -- that resulted from the experimental adaptation of 24 haploid and diploid cultures of Saccharomyces cerevisiae to growth in either glucose, sulfate, or phosphate-limited chemostats for approximately 200 generations. We identified frequent genomic amplifications and rearrangements as well as novel retrotransposition events associated with adaptation. Global nucleotide variation detection in ten clonal isolates identified 32 point mutations. On the basis of mutation frequencies, we infer that these mutations and the subsequent dynamics of adaptation are determined by the batch phase of growth prior to initiation of the continuous phase in the chemostat. We relate these genotypic changes to phenotypic outcomes, namely global patterns of gene expression, and to increases in fitness by 5-50%. We found that the spectrum of available mutations in glucose- or phosphate-limited environments combined with the batch phase population dynamics early in our experiments allowed several distinct genotypic and phenotypic evolutionary pathways in response to these nutrient limitations. By contrast, sulfate-limited populations were much more constrained in both genotypic and phenotypic outcomes. Thus, the reproducibility of evolution varies with specific selective pressures, reflecting the constraints inherent in the system-level organization of metabolic processes in the cell. We were able to relate some of the observed adaptive mutations (e.g., transporter gene amplifications to known features

Computerized adaptive testing item selection in computerized adaptive learning systems

NARCIS (Netherlands)

Eggen, Theodorus Johannes Hendrikus Maria; Eggen, T.J.H.M.; Veldkamp, B.P.

2012-01-01

Item selection methods traditionally developed for computerized adaptive testing (CAT) are explored for their usefulness in item-based computerized adaptive learning (CAL) systems. While in CAT Fisher information-based selection is optimal, for recovering learning populations in CAL systems item

Dynamic adaptation of tendon and muscle connective tissue to mechanical loading

DEFF Research Database (Denmark)

Mackey, Abigail; Heinemeier, Katja Maria; Koskinen, Satu Osmi Anneli

2008-01-01

The connective tissue of tendon and skeletal muscle is a crucial structure for force transmission. A dynamic adaptive capacity of these tissues in healthy individuals is evident from reports of altered gene expression and protein levels of the fibrillar and network-forming collagens, when subjected...... in this article provide strong evidence for the highly adaptable nature of connective tissue in muscle and tendon....

Vertebrate gravity sensors as dynamic systems

Science.gov (United States)

Ross, M. D.

1985-01-01

This paper considers verterbrate gravity receptors as dynamic sensors. That is, it is hypothesized that gravity is a constant force to which an acceleration-sensing system would readily adapt. Premises are considered in light of the presence of kinocilia on hair cells of vertebrate gravity sensors; differences in loading of the sensors among species; and of possible reduction in loading by inclusion of much organic material in otoconia. Moreover, organic-inorganic interfaces may confer a piezoelectric property upon otoconia, which increase the sensitivity of the sensory system to small accelerations. Comparisons with man-made accelerometers are briefly taken up.

Adaptive fuzzy-neural-network control for maglev transportation system.

Science.gov (United States)

Wai, Rong-Jong; Lee, Jeng-Dao

2008-01-01

A magnetic-levitation (maglev) transportation system including levitation and propulsion control is a subject of considerable scientific interest because of highly nonlinear and unstable behaviors. In this paper, the dynamic model of a maglev transportation system including levitated electromagnets and a propulsive linear induction motor (LIM) based on the concepts of mechanical geometry and motion dynamics is developed first. Then, a model-based sliding-mode control (SMC) strategy is introduced. In order to alleviate chattering phenomena caused by the inappropriate selection of uncertainty bound, a simple bound estimation algorithm is embedded in the SMC strategy to form an adaptive sliding-mode control (ASMC) scheme. However, this estimation algorithm is always a positive value so that tracking errors introduced by any uncertainty will cause the estimated bound increase even to infinity with time. Therefore, it further designs an adaptive fuzzy-neural-network control (AFNNC) scheme by imitating the SMC strategy for the maglev transportation system. In the model-free AFNNC, online learning algorithms are designed to cope with the problem of chattering phenomena caused by the sign action in SMC design, and to ensure the stability of the controlled system without the requirement of auxiliary compensated controllers despite the existence of uncertainties. The outputs of the AFNNC scheme can be directly supplied to the electromagnets and LIM without complicated control transformations for relaxing strict constrains in conventional model-based control methodologies. The effectiveness of the proposed control schemes for the maglev transportation system is verified by numerical simulations, and the superiority of the AFNNC scheme is indicated in comparison with the SMC and ASMC strategies.

Adaptive dynamic capacity borrowing in road-covering mobile networks

NARCIS (Netherlands)

Ule, A.; Boucherie, Richardus J.; Li, W.; Pan, Y.

2006-01-01

This paper introduces adaptive dynamic capacity borrowing strategies for wireless networks covering a road. In a F/TDMA-based model, road traffic prediction models are used to characterise the movement of hot spots, such as traffic jams, and subsequently to predict the teletraffic load offered to

A control systems engineering approach for adaptive behavioral interventions: illustration with a fibromyalgia intervention.

Science.gov (United States)

Deshpande, Sunil; Rivera, Daniel E; Younger, Jarred W; Nandola, Naresh N

2014-09-01

The term adaptive intervention has been used in behavioral medicine to describe operationalized and individually tailored strategies for prevention and treatment of chronic, relapsing disorders. Control systems engineering offers an attractive means for designing and implementing adaptive behavioral interventions that feature intensive measurement and frequent decision-making over time. This is illustrated in this paper for the case of a low-dose naltrexone treatment intervention for fibromyalgia. System identification methods from engineering are used to estimate dynamical models from daily diary reports completed by participants. These dynamical models then form part of a model predictive control algorithm which systematically decides on treatment dosages based on measurements obtained under real-life conditions involving noise, disturbances, and uncertainty. The effectiveness and implications of this approach for behavioral interventions (in general) and pain treatment (in particular) are demonstrated using informative simulations.

Air-Breathing Hypersonic Vehicle Tracking Control Based on Adaptive Dynamic Programming.

Science.gov (United States)

Mu, Chaoxu; Ni, Zhen; Sun, Changyin; He, Haibo

2017-03-01

In this paper, we propose a data-driven supplementary control approach with adaptive learning capability for air-breathing hypersonic vehicle tracking control based on action-dependent heuristic dynamic programming (ADHDP). The control action is generated by the combination of sliding mode control (SMC) and the ADHDP controller to track the desired velocity and the desired altitude. In particular, the ADHDP controller observes the differences between the actual velocity/altitude and the desired velocity/altitude, and then provides a supplementary control action accordingly. The ADHDP controller does not rely on the accurate mathematical model function and is data driven. Meanwhile, it is capable to adjust its parameters online over time under various working conditions, which is very suitable for hypersonic vehicle system with parameter uncertainties and disturbances. We verify the adaptive supplementary control approach versus the traditional SMC in the cruising flight, and provide three simulation studies to illustrate the improved performance with the proposed approach.

Robust control for a biaxial servo with time delay system based on adaptive tuning technique.

Science.gov (United States)

Chen, Tien-Chi; Yu, Chih-Hsien

2009-07-01

A robust control method for synchronizing a biaxial servo system motion is proposed in this paper. A new network based cross-coupled control and adaptive tuning techniques are used together to cancel out the skew error. The conventional fixed gain PID cross-coupled controller (CCC) is replaced with the adaptive cross-coupled controller (ACCC) in the proposed control scheme to maintain biaxial servo system synchronization motion. Adaptive-tuning PID (APID) position and velocity controllers provide the necessary control actions to maintain synchronization while following a variable command trajectory. A delay-time compensator (DTC) with an adaptive controller was augmented to set the time delay element, effectively moving it outside the closed loop, enhancing the stability of the robust controlled system. This scheme provides strong robustness with respect to uncertain dynamics and disturbances. The simulation and experimental results reveal that the proposed control structure adapts to a wide range of operating conditions and provides promising results under parameter variations and load changes.

Loss Aversion, Adaptive Beliefs, and Asset Pricing Dynamics

Directory of Open Access Journals (Sweden)

Kamal Samy Selim

2015-01-01

Full Text Available We study asset pricing dynamics in artificial financial markets model. The financial market is populated with agents following two heterogeneous trading beliefs, the technical and the fundamental prediction rules. Agents switch between trading rules with respect to their past performance. The agents are loss averse over asset price fluctuations. Loss aversion behaviour depends on the past performance of the trading strategies in terms of an evolutionary fitness measure. We propose a novel application of the prospect theory to agent-based modelling, and by simulation, the effect of evolutionary fitness measure on adaptive belief system is investigated. For comparison, we study pricing dynamics of a financial market populated with chartists perceive losses and gains symmetrically. One of our contributions is validating the agent-based models using real financial data of the Egyptian Stock Exchange. We find that our framework can explain important stylized facts in financial time series, such as random walk price behaviour, bubbles and crashes, fat-tailed return distributions, power-law tails in the distribution of returns, excess volatility, volatility clustering, the absence of autocorrelation in raw returns, and the power-law autocorrelations in absolute returns. In addition to this, we find that loss aversion improves market quality and market stability.

Selective host molecules obtained by dynamic adaptive chemistry.

Science.gov (United States)

Matache, Mihaela; Bogdan, Elena; Hădade, Niculina D

2014-02-17

Up till 20 years ago, in order to endow molecules with function there were two mainstream lines of thought. One was to rationally design the positioning of chemical functionalities within candidate molecules, followed by an iterative synthesis-optimization process. The second was the use of a "brutal force" approach of combinatorial chemistry coupled with advanced screening for function. Although both methods provided important results, "rational design" often resulted in time-consuming efforts of modeling and synthesis only to find that the candidate molecule was not performing the designed job. "Combinatorial chemistry" suffered from a fundamental limitation related to the focusing of the libraries employed, often using lead compounds that limit its scope. Dynamic constitutional chemistry has developed as a combination of the two approaches above. Through the rational use of reversible chemical bonds together with a large plethora of precursor libraries, one is now able to build functional structures, ranging from quite simple molecules up to large polymeric structures. Thus, by introduction of the dynamic component within the molecular recognition processes, a new perspective of deciphering the world of the molecular events has aroused together with a new field of chemistry. Since its birth dynamic constitutional chemistry has continuously gained attention, in particular due to its ability to easily create from scratch outstanding molecular structures as well as the addition of adaptive features. The fundamental concepts defining the dynamic constitutional chemistry have been continuously extended to currently place it at the intersection between the supramolecular chemistry and newly defined adaptive chemistry, a pivotal feature towards evolutive chemistry. Copyright © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Block Fusion on Dynamically Adaptive Spacetree Grids for Shallow Water Waves

KAUST Repository

Weinzierl, Tobias

2014-09-01

© 2014 World Scientific Publishing Company. Spacetrees are a popular formalism to describe dynamically adaptive Cartesian grids. Even though they directly yield a mesh, it is often computationally reasonable to embed regular Cartesian blocks into their leaves. This promotes stencils working on homogeneous data chunks. The choice of a proper block size is sensitive. While large block sizes foster loop parallelism and vectorisation, they restrict the adaptivity\\'s granularity and hence increase the memory footprint and lower the numerical accuracy per byte. In the present paper, we therefore use a multiscale spacetree-block coupling admitting blocks on all spacetree nodes. We propose to find sets of blocks on the finest scale throughout the simulation and to replace them by fused big blocks. Such a replacement strategy can pick up hardware characteristics, i.e. which block size yields the highest throughput, while the dynamic adaptivity of the fine grid mesh is not constrained - applications can work with fine granular blocks. We study the fusion with a state-of-the-art shallow water solver at hands of an Intel Sandy Bridge and a Xeon Phi processor where we anticipate their reaction to selected block optimisation and vectorisation.

Adaptive control of port-Hamiltonian systems

NARCIS (Netherlands)

Dirksz, D.A.; Scherpen, J.M.A.; Edelmayer, András

2010-01-01

In this paper an adaptive control scheme is presented for general port-Hamiltonian systems. Adaptive control is used to compensate for control errors that are caused by unknown or uncertain parameter values of a system. The adaptive control is also combined with canonical transformation theory for

Adaptive Backstepping Sliding-Mode Control of the Electronic Throttle System in Modern Automobiles

Directory of Open Access Journals (Sweden)

Rui Bai

2014-01-01

Full Text Available In modern automobiles, electronic throttle is a DC-motor-driven valve that regulates air inflow into the vehicle’s combustion system. The electronic throttle is increasingly being used in order to improve the vehicle drivability, fuel economy, and emissions. Electronic throttle system has the nonlinear dynamical characteristics with the unknown disturbance and parameters. At first, the dynamical nonlinear model of the electronic throttle is built in this paper. Based on the model and using the backstepping design technique, a new adaptive backstepping sliding-mode controller of the electronic throttle is developed. During the backstepping design process, parameter adaptive law is designed to estimate the unknown parameter, and sliding-mode control term is applied to compensate the unknown disturbance. The proposed controller can make the actual angle of the electronic throttle track its set point with the satisfactory performance. Finally, a computer simulation is performed, and simulation results verify that the proposed control method can achieve favorable tracking performance.

Stress and Systemic Inflammation: Yin-Yang Dynamics in Health and Diseases.

Science.gov (United States)

Yan, Qing

2018-01-01

Studies in psychoneuroimmunology (PNI) would provide better insights into the "whole mind-body system." Systems biology models of the complex adaptive systems (CASs), such as a conceptual framework of "Yin-Yang dynamics," may be helpful for identifying systems-based biomarkers and targets for more effective prevention and treatment. The disturbances in the Yin-Yang dynamical balance may result in stress, inflammation, and various disorders including insomnia, Alzheimer's disease, obesity, diabetes, cardiovascular diseases, skin disorders, and cancer. At the molecular and cellular levels, the imbalances in the cytokine pathways, mitochondria networks, redox systems, and various signaling pathways may contribute to systemic inflammation. In the nervous system, Yin and Yang may represent the dynamical associations between the progressive and regressive processes in aging and neurodegenerative diseases. In response to the damages to the heart, the Yin-Yang dynamical balance between proinflammatory and anti-inflammatory cytokine networks is crucial. The studies of cancer have revealed the importance of the Yin-Yang dynamics in the tumoricidal and tumorigenic activities of the immune system. Stress-induced neuroimmune imbalances are also essential in chronic skin disorders including atopic dermatitis and psoriasis. With the integrative framework, the restoration of the Yin-Yang dynamics can become the objective of dynamical systems medicine.

Dynamic game balancing implementation using adaptive algorithm in mobile-based Safari Indonesia game

Science.gov (United States)

Yuniarti, Anny; Nata Wardanie, Novita; Kuswardayan, Imam

2018-03-01

In developing a game there is one method that should be applied to maintain the interest of players, namely dynamic game balancing. Dynamic game balancing is a process to match a player’s playing style with the behaviour, attributes, and game environment. This study applies dynamic game balancing using adaptive algorithm in scrolling shooter game type called Safari Indonesia which developed using Unity. The game of this type is portrayed by a fighter aircraft character trying to defend itself from insistent enemy attacks. This classic game is chosen to implement adaptive algorithms because it has quite complex attributes to be developed using dynamic game balancing. Tests conducted by distributing questionnaires to a number of players indicate that this method managed to reduce frustration and increase the pleasure factor in playing.

FEATURES OF LOGISTIC SYSTEM ADAPTIVE MANAGEMENT

Directory of Open Access Journals (Sweden)

Natalya VOZNENKO

2015-08-01

Full Text Available The study presents literature survey on enterprise logistic system adaptive management place and structure in the general enterprise management system. The theoretical basics of logistic system functioning, levels of its management and its effectiveness had been investigated. The role of adaptive management and its types had been scrutinized. The necessity of creating company’s adaptive regulator such as its economic mechanism had been proved.

Adaptively locating unknown steady states: Formalism and basin of attraction

International Nuclear Information System (INIS)

Wu, Yu; Lin, Wei

2011-01-01

The adaptive technique, which includes both dynamical estimators and coupling gains, has been recently verified to be practical for locating the unknown steady states numerically. This Letter, in the light of the center manifold theory for dynamical systems and the matrix spectrum principle, establishes an analytical formalism of this adaptive technique and reveals a connection between this technique and the original adaptive controller which includes only the dynamical estimator. More interestingly, in study of the well-known Lorenz system, the selections of the estimator parameters and initial values are found to be crucial to the successful application of the adaptive technique. Some Milnor-like basins of attraction with fractal structures are found quantitatively. All the results obtained in the Letter can be further extended to more general dynamical systems of higher dimensions. -- Highlights: → Establishing a new and rigorous formalism for the adaptive stabilization technique. → Showing a close connection between the adaptive technique and the original controller. → Providing feasible algorithms for simultaneous stabilization of multiple steady states. → Finding Milnor-like basins of attraction with fractal structures in adaptive control.

Complexity Thinking in PE: Game-Centred Approaches, Games as Complex Adaptive Systems, and Ecological Values

Science.gov (United States)

Storey, Brian; Butler, Joy

2013-01-01

Background: This article draws on the literature relating to game-centred approaches (GCAs), such as Teaching Games for Understanding, and dynamical systems views of motor learning to demonstrate a convergence of ideas around games as complex adaptive learning systems. This convergence is organized under the title "complexity thinking"…

Passivation and control of partially known SISO nonlinear systems via dynamic neural networks

Directory of Open Access Journals (Sweden)

Reyes-Reyes J.

2000-01-01

Full Text Available In this paper, an adaptive technique is suggested to provide the passivity property for a class of partially known SISO nonlinear systems. A simple Dynamic Neural Network (DNN, containing only two neurons and without any hidden-layers, is used to identify the unknown nonlinear system. By means of a Lyapunov-like analysis the new learning law for this DNN, guarantying both successful identification and passivation effects, is derived. Based on this adaptive DNN model, an adaptive feedback controller, serving for wide class of nonlinear systems with an a priori incomplete model description, is designed. Two typical examples illustrate the effectiveness of the suggested approach.

Adaptation in CRISPR-Cas Systems.

Science.gov (United States)

Sternberg, Samuel H; Richter, Hagen; Charpentier, Emmanuelle; Qimron, Udi

2016-03-17

Clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated (Cas) proteins constitute an adaptive immune system in prokaryotes. The system preserves memories of prior infections by integrating short segments of foreign DNA, termed spacers, into the CRISPR array in a process termed adaptation. During the past 3 years, significant progress has been made on the genetic requirements and molecular mechanisms of adaptation. Here we review these recent advances, with a focus on the experimental approaches that have been developed, the insights they generated, and a proposed mechanism for self- versus non-self-discrimination during the process of spacer selection. We further describe the regulation of adaptation and the protein players involved in this fascinating process that allows bacteria and archaea to harbor adaptive immunity. Copyright © 2016 Elsevier Inc. All rights reserved.

Neural Computations in a Dynamical System with Multiple Time Scales.

Science.gov (United States)

Mi, Yuanyuan; Lin, Xiaohan; Wu, Si

2016-01-01

Neural systems display rich short-term dynamics at various levels, e.g., spike-frequency adaptation (SFA) at the single-neuron level, and short-term facilitation (STF) and depression (STD) at the synapse level. These dynamical features typically cover a broad range of time scales and exhibit large diversity in different brain regions. It remains unclear what is the computational benefit for the brain to have such variability in short-term dynamics. In this study, we propose that the brain can exploit such dynamical features to implement multiple seemingly contradictory computations in a single neural circuit. To demonstrate this idea, we use continuous attractor neural network (CANN) as a working model and include STF, SFA and STD with increasing time constants in its dynamics. Three computational tasks are considered, which are persistent activity, adaptation, and anticipative tracking. These tasks require conflicting neural mechanisms, and hence cannot be implemented by a single dynamical feature or any combination with similar time constants. However, with properly coordinated STF, SFA and STD, we show that the network is able to implement the three computational tasks concurrently. We hope this study will shed light on the understanding of how the brain orchestrates its rich dynamics at various levels to realize diverse cognitive functions.

SU-E-J-254: Utility of Pinnacle Dynamic Planning Module Utilizing Deformable Image Registration in Adaptive Radiotherapy

International Nuclear Information System (INIS)

Jani, S

2014-01-01

Purpose For certain highly conformal treatment techniques, changes in patient anatomy due to weight loss and/or tumor shrinkage can result in significant changes in dose distribution. Recently, the Pinnacle treatment planning system added a Dynamic Planning module utilizing Deformable Image Registration (DIR). The objective of this study was to evaluate the effectiveness of this software in adapting to altered anatomy and adjusting treatment plans to account for it. Methods We simulated significant tumor response by changing patient thickness and altered chin positions using a commercially-available head and neck (H and N) phantom. In addition, we studied 23 CT image sets of fifteen (15) patients with H and N tumors and eight (8) patients with prostate cancer. In each case, we applied deformable image registration through Dynamic Planning module of our Pinnacle Treatment Planning System. The dose distribution of the original CT image set was compared to the newly computed dose without altering any treatment parameter. Result was a dose if we did not adjust the plan to reflect anatomical changes. Results For the H and N phantom, a tumor response of up to 3.5 cm was correctly deformed by the Pinnacle Dynamic module. Recomputed isodose contours on new anatomies were within 1 mm of the expected distribution. The Pinnacle system configuration allowed dose computations resulting from original plans on new anatomies without leaving the planning system. Original and new doses were available side-by-side with both CT image sets. Based on DIR, about 75% of H and N patients (11/15) required a re-plan using new anatomy. Among prostate patients, the DIR predicted near-correct bladder volume in 62% of the patients (5/8). Conclusions The Dynamic Planning module of the Pinnacle system proved to be an accurate and useful tool in our ability to adapt to changes in patient anatomy during a course of radiotherapy

Fuel cell/back-up battery hybrid energy conversion systems: Dynamic modeling and harmonic considerations

International Nuclear Information System (INIS)

Fathabadi, Hassan

2015-01-01

Highlights: • Novel technique to completely eliminate the harmful harmonics of fuel cell system. • Presenting a novel high accurate detailed electrochemical dynamic model of fuel cells. • Back-up battery system to compensate the slow dynamic response of fuel cell system. • Exact analysis of real electrochemical reactions occurring inside fuel cells. - Abstract: In this study, a novel dynamic model of fuel cells is presented. High accurate static and dynamic responses of the proposed model are experimentally validated by comparing simulated results with real experimental data. The obtained model together with theoretical results shows that a fuel cell or a fuel cell stack has very slow dynamic response, so that, it cannot adapt itself to the fast variations in load demand. It is shown that for adapting well a fuel cell stack to the load demand, the stack should be equipped with a proposed back-up battery system which compensates the slow dynamic response of the stack by providing a bidirectional path to transmit/absorb the extra instant power. It is proved that the conventional switching waveforms used in the converters of the stacks and back-up systems produce an enormous amount of harmful harmonics. Then, a novel technique is proposed to completely eliminate main harmful harmonics. It is worthwhile to note that all the other techniques only reduce the harmful harmonics. Simulated results verify that the back-up battery system together with applying the proposed technique provide a fast dynamic response for the fuel cell/back-up battery system, and also completely eliminate the main harmful harmonics

Adaptive synchronization of the complex dynamical network with non-derivative and derivative coupling

Energy Technology Data Exchange (ETDEWEB)

Xu Yuhua, E-mail: yuhuaxu2004@163.co [College of Information Science and Technology, Donghua University, Shanghai 201620 (China) and Department of Maths, Yunyang Teachers' College, Hubei 442000 (China); Zhou Wuneng, E-mail: wnzhou@163.co [College of Information Science and Technology, Donghua University, Shanghai 201620 (China); Fang Jian' an [College of Information Science and Technology, Donghua University, Shanghai 201620 (China); Sun Wen [School of Mathematics and Information, Yangtze University, Hubei Jingzhou 434023 (China)

2010-04-05

This Letter investigates the synchronization of a general complex dynamical network with non-derivative and derivative coupling. Based on LaSalle's invariance principle, adaptive synchronization criteria are obtained. Analytical result shows that under the designed adaptive controllers, a general complex dynamical network with non-derivative and derivative coupling can asymptotically synchronize to a given trajectory, and several useful criteria for synchronization are given. What is more, the coupling matrix is not assumed to be symmetric or irreducible. Finally, simulations results show the method is effective.

Quinoa - Adaptive Computational Fluid Dynamics, 0.2

Energy Technology Data Exchange (ETDEWEB)

2017-09-22

Quinoa is a set of computational tools that enables research and numerical analysis in fluid dynamics. At this time it remains a test-bed to experiment with various algorithms using fully asynchronous runtime systems. Currently, Quinoa consists of the following tools: (1) Walker, a numerical integrator for systems of stochastic differential equations in time. It is a mathematical tool to analyze and design the behavior of stochastic differential equations. It allows the estimation of arbitrary coupled statistics and probability density functions and is currently used for the design of statistical moment approximations for multiple mixing materials in variable-density turbulence. (2) Inciter, an overdecomposition-aware finite element field solver for partial differential equations using 3D unstructured grids. Inciter is used to research asynchronous mesh-based algorithms and to experiment with coupling asynchronous to bulk-synchronous parallel code. Two planned new features of Inciter, compared to the previous release (LA-CC-16-015), to be implemented in 2017, are (a) a simple Navier-Stokes solver for ideal single-material compressible gases, and (b) solution-adaptive mesh refinement (AMR), which enables dynamically concentrating compute resources to regions with interesting physics. Using the NS-AMR problem we plan to explore how to scale such high-load-imbalance simulations, representative of large production multiphysics codes, to very large problems on very large computers using an asynchronous runtime system. (3) RNGTest, a test harness to subject random number generators to stringent statistical tests enabling quantitative ranking with respect to their quality and computational cost. (4) UnitTest, a unit test harness, running hundreds of tests per second, capable of testing serial, synchronous, and asynchronous functions. (5) MeshConv, a mesh file converter that can be used to convert 3D tetrahedron meshes from and to either of the following formats: Gmsh

Cooperative control of multi-agent systems optimal and adaptive design approaches

CERN Document Server

Lewis, Frank L; Hengster-Movric, Kristian; Das, Abhijit

2014-01-01

Task complexity, communication constraints, flexibility and energy-saving concerns are all factors that may require a group of autonomous agents to work together in a cooperative manner. Applications involving such complications include mobile robots, wireless sensor networks, unmanned aerial vehicles (UAVs), spacecraft, and so on. In such networked multi-agent scenarios, the restrictions imposed by the communication graph topology can pose severe problems in the design of cooperative feedback control systems.  Cooperative control of multi-agent systems is a challenging topic for both control theorists and practitioners and has been the subject of significant recent research. Cooperative Control of Multi-Agent Systems extends optimal control and adaptive control design methods to multi-agent systems on communication graphs.  It develops Riccati design techniques for general linear dynamics for cooperative state feedback design, cooperative observer design, and cooperative dynamic output feedback design.  B...

Global network reorganization during dynamic adaptations of Bacillus subtilis metabolism

DEFF Research Database (Denmark)

Buescher, Joerg Martin; Liebermeister, Wolfram; Jules, Matthieu

2012-01-01

Adaptation of cells to environmental changes requires dynamic interactions between metabolic and regulatory networks, but studies typically address only one or a few layers of regulation. For nutritional shifts between two preferred carbon sources of Bacillus subtilis, we combined statistical...

Adaptive evolution of body size subject to indirect effect in trophic cascade system.

Science.gov (United States)

Wang, Xin; Fan, Meng; Hao, Lina

2017-09-01

Trophic cascades represent a classic example of indirect effect and are wide-spread in nature. Their ecological impact are well established, but the evolutionary consequences have received even less theoretical attention. We theoretically and numerically investigate the trait (i.e., body size of consumer) evolution in response to indirect effect in a trophic cascade system. By applying the quantitative trait evolutionary theory and the adaptive dynamic theory, we formulate and explore two different types of eco-evolutionary resource-consumer-predator trophic cascade model. First, an eco-evolutionary model incorporating the rapid evolution is formulated to investigate the effect of rapid evolution of the consumer's body size, and to explore the impact of density-mediate indirect effect on the population dynamics and trait dynamics. Next, by employing the adaptive dynamic theory, a long-term evolutionary model of consumer body size is formulated to evaluate the effect of long-term evolution on the population dynamics and the effect of trait-mediate indirect effect. Those models admit rich dynamics that has not been observed yet in empirical studies. It is found that, both in the trait-mediated and density-mediated system, the body size of consumer in predator-consumer-resource interaction (indirect effect) evolves smaller than that in consumer-resource and predator-consumer interaction (direct effect). Moreover, in the density-mediated system, we found that the evolution of consumer body size contributes to avoiding consumer extinction (i.e., evolutionary rescue). The trait-mediate and density-mediate effects may produce opposite evolutionary response. This study suggests that the trophic cascade indirect effect affects consumer evolution, highlights a more comprehensive mechanistic understanding of the intricate interplay between ecological and evolutionary force. The modeling approaches provide avenue for study on indirect effects from an evolutionary perspective

Adaptive Synchronization of Fractional Order Complex-Variable Dynamical Networks via Pinning Control

Science.gov (United States)

Ding, Da-Wei; Yan, Jie; Wang, Nian; Liang, Dong

2017-09-01

In this paper, the synchronization of fractional order complex-variable dynamical networks is studied using an adaptive pinning control strategy based on close center degree. Some effective criteria for global synchronization of fractional order complex-variable dynamical networks are derived based on the Lyapunov stability theory. From the theoretical analysis, one concludes that under appropriate conditions, the complex-variable dynamical networks can realize the global synchronization by using the proper adaptive pinning control method. Meanwhile, we succeed in solving the problem about how much coupling strength should be applied to ensure the synchronization of the fractional order complex networks. Therefore, compared with the existing results, the synchronization method in this paper is more general and convenient. This result extends the synchronization condition of the real-variable dynamical networks to the complex-valued field, which makes our research more practical. Finally, two simulation examples show that the derived theoretical results are valid and the proposed adaptive pinning method is effective. Supported by National Natural Science Foundation of China under Grant No. 61201227, National Natural Science Foundation of China Guangdong Joint Fund under Grant No. U1201255, the Natural Science Foundation of Anhui Province under Grant No. 1208085MF93, 211 Innovation Team of Anhui University under Grant Nos. KJTD007A and KJTD001B, and also supported by Chinese Scholarship Council

Dynamic modelling and adaptive robust tracking control of a space robot with two-link flexible manipulators under unknown disturbances

Science.gov (United States)

Yang, Xinxin; Ge, Shuzhi Sam; He, Wei

2018-04-01

In this paper, both the closed-form dynamics and adaptive robust tracking control of a space robot with two-link flexible manipulators under unknown disturbances are developed. The dynamic model of the system is described with assumed modes approach and Lagrangian method. The flexible manipulators are represented as Euler-Bernoulli beams. Based on singular perturbation technique, the displacements/joint angles and flexible modes are modelled as slow and fast variables, respectively. A sliding mode control is designed for trajectories tracking of the slow subsystem under unknown but bounded disturbances, and an adaptive sliding mode control is derived for slow subsystem under unknown slowly time-varying disturbances. An optimal linear quadratic regulator method is proposed for the fast subsystem to damp out the vibrations of the flexible manipulators. Theoretical analysis validates the stability of the proposed composite controller. Numerical simulation results demonstrate the performance of the closed-loop flexible space robot system.

Dynamic data filtering system and method

Science.gov (United States)

Bickford, Randall L; Palnitkar, Rahul M

2014-04-29

A computer-implemented dynamic data filtering system and method for selectively choosing operating data of a monitored asset that modifies or expands a learned scope of an empirical model of normal operation of the monitored asset while simultaneously rejecting operating data of the monitored asset that is indicative of excessive degradation or impending failure of the monitored asset, and utilizing the selectively chosen data for adaptively recalibrating the empirical model to more accurately monitor asset aging changes or operating condition changes of the monitored asset.

Self-Adaptive Systems for Machine Intelligence

CERN Document Server

He, Haibo

2011-01-01

This book will advance the understanding and application of self-adaptive intelligent systems; therefore it will potentially benefit the long-term goal of replicating certain levels of brain-like intelligence in complex and networked engineering systems. It will provide new approaches for adaptive systems within uncertain environments. This will provide an opportunity to evaluate the strengths and weaknesses of the current state-of-the-art of knowledge, give rise to new research directions, and educate future professionals in this domain. Self-adaptive intelligent systems have wide application

Fuzzy Control of Cold Storage Refrigeration System with Dynamic Coupling Compensation

Directory of Open Access Journals (Sweden)

Xiliang Ma

2018-01-01

Full Text Available Cold storage refrigeration systems possess the characteristics of multiple input and output and strong coupling, which brings challenges to the optimize control. To reduce the adverse effects of the coupling and improve the overall control performance of cold storage refrigeration systems, a control strategy with dynamic coupling compensation was studied. First, dynamic model of a cold storage refrigeration system was established based on the requirements of the control system. At the same time, the coupling between the components was studied. Second, to reduce the adverse effects of the coupling, a fuzzy controller with dynamic coupling compensation was designed. As for the fuzzy controller, a self-tuning fuzzy controller was served as the primary controller, and an adaptive neural network was adopted to compensate the dynamic coupling. Finally, the proposed control strategy was employed to the cold storage refrigeration system, and simulations were carried out in the condition of start-up, variable load, and variable degree of superheat, respectively. The simulation results verify the effectiveness of the fuzzy control method with dynamic coupling compensation.

Adaptive local basis set for Kohn–Sham density functional theory in a discontinuous Galerkin framework II: Force, vibration, and molecular dynamics calculations

Energy Technology Data Exchange (ETDEWEB)

Zhang, Gaigong [Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720 (United States); Lin, Lin, E-mail: linlin@math.berkeley.edu [Department of Mathematics, University of California, Berkeley, Berkeley, CA 94720 (United States); Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720 (United States); Hu, Wei, E-mail: whu@lbl.gov [Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720 (United States); Yang, Chao, E-mail: cyang@lbl.gov [Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720 (United States); Pask, John E., E-mail: pask1@llnl.gov [Physics Division, Lawrence Livermore National Laboratory, Livermore, CA 94550 (United States)

2017-04-15

Recently, we have proposed the adaptive local basis set for electronic structure calculations based on Kohn–Sham density functional theory in a pseudopotential framework. The adaptive local basis set is efficient and systematically improvable for total energy calculations. In this paper, we present the calculation of atomic forces, which can be used for a range of applications such as geometry optimization and molecular dynamics simulation. We demonstrate that, under mild assumptions, the computation of atomic forces can scale nearly linearly with the number of atoms in the system using the adaptive local basis set. We quantify the accuracy of the Hellmann–Feynman forces for a range of physical systems, benchmarked against converged planewave calculations, and find that the adaptive local basis set is efficient for both force and energy calculations, requiring at most a few tens of basis functions per atom to attain accuracies required in practice. Since the adaptive local basis set has implicit dependence on atomic positions, Pulay forces are in general nonzero. However, we find that the Pulay force is numerically small and systematically decreasing with increasing basis completeness, so that the Hellmann–Feynman force is sufficient for basis sizes of a few tens of basis functions per atom. We verify the accuracy of the computed forces in static calculations of quasi-1D and 3D disordered Si systems, vibration calculation of a quasi-1D Si system, and molecular dynamics calculations of H{sub 2} and liquid Al–Si alloy systems, where we show systematic convergence to benchmark planewave results and results from the literature.

Occupants’ behavioural adaptation in workplaces with non-central heating and cooling systems

International Nuclear Information System (INIS)

Liu Jing; Yao Runming; Wang Juan; Li Baizhan

2012-01-01

Occupants’ behaviour when improving the indoor environment plays a significant role in saving energy in buildings. Therefore the key step to reducing energy consumption and carbon emissions from buildings is to understand how occupants interact with the environment they are exposed to in terms of achieving thermal comfort and well-being; though such interaction is complex. This paper presents a dynamic process of occupant behaviours involving technological, personal and psychological adaptations in response to varied thermal conditions based on the data covering four seasons gathered from the field study in Chongqing, China. It demonstrates that occupants are active players in environmental control and their adaptive responses are driven strongly by ambient thermal stimuli and vary from season to season and from time to time, even on the same day. Positive, dynamic, behavioural adaptation will help save energy used in heating and cooling buildings. However, when environmental parameters cannot fully satisfy occupants’ requirements, negative behaviours could conflict with energy saving. The survey revealed that about 23% of windows are partly open for fresh air when air-conditioners are in operation in summer. This paper addresses the issues how the building and environmental systems should be designed, operated and managed in a way that meets the requirements of energy efficiency without compromising wellbeing and productivity. - Highlights: ► A year-long field study of observing user behaviour in workplace. ► Occupants’ behavioural adaptation plays an important role in reducing energy from buildings. ► Occupants actively and dynamically respond to outdoor climate change. ► Positive behavioural adaptations will eliminate the use of heating/cooling facilities. ► The poor use of heating/cooling facilities will cause a significant waste of energy.

Clustering of tethered satellite system simulation data by an adaptive neuro-fuzzy algorithm

Science.gov (United States)

Mitra, Sunanda; Pemmaraju, Surya

1992-01-01

Recent developments in neuro-fuzzy systems indicate that the concepts of adaptive pattern recognition, when used to identify appropriate control actions corresponding to clusters of patterns representing system states in dynamic nonlinear control systems, may result in innovative designs. A modular, unsupervised neural network architecture, in which fuzzy learning rules have been embedded is used for on-line identification of similar states. The architecture and control rules involved in Adaptive Fuzzy Leader Clustering (AFLC) allow this system to be incorporated in control systems for identification of system states corresponding to specific control actions. We have used this algorithm to cluster the simulation data of Tethered Satellite System (TSS) to estimate the range of delta voltages necessary to maintain the desired length rate of the tether. The AFLC algorithm is capable of on-line estimation of the appropriate control voltages from the corresponding length error and length rate error without a priori knowledge of their membership functions and familarity with the behavior of the Tethered Satellite System.

Indirect adaptive fuzzy wavelet neural network with self- recurrent consequent part for AC servo system.

Science.gov (United States)

Hou, Runmin; Wang, Li; Gao, Qiang; Hou, Yuanglong; Wang, Chao

2017-09-01

This paper proposes a novel indirect adaptive fuzzy wavelet neural network (IAFWNN) to control the nonlinearity, wide variations in loads, time-variation and uncertain disturbance of the ac servo system. In the proposed approach, the self-recurrent wavelet neural network (SRWNN) is employed to construct an adaptive self-recurrent consequent part for each fuzzy rule of TSK fuzzy model. For the IAFWNN controller, the online learning algorithm is based on back propagation (BP) algorithm. Moreover, an improved particle swarm optimization (IPSO) is used to adapt the learning rate. The aid of an adaptive SRWNN identifier offers the real-time gradient information to the adaptive fuzzy wavelet neural controller to overcome the impact of parameter variations, load disturbances and other uncertainties effectively, and has a good dynamic. The asymptotical stability of the system is guaranteed by using the Lyapunov method. The result of the simulation and the prototype test prove that the proposed are effective and suitable. Copyright © 2017. Published by Elsevier Ltd.

A TSK neuro-fuzzy approach for modeling highly dynamic systems

NARCIS (Netherlands)

Acampora, G.

2011-01-01

This paper introduces a new type of TSK-based neuro-fuzzy approach and its application to modeling highly dynamic systems. In details, our proposal performs an adaptive supervised learning on a collection of time series in order to create a so-called Timed Automata Based Fuzzy Controller, i.e. an

Spike-threshold adaptation predicted by membrane potential dynamics in vivo.

Directory of Open Access Journals (Sweden)

Bertrand Fontaine

2014-04-01

Full Text Available Neurons encode information in sequences of spikes, which are triggered when their membrane potential crosses a threshold. In vivo, the spiking threshold displays large variability suggesting that threshold dynamics have a profound influence on how the combined input of a neuron is encoded in the spiking. Threshold variability could be explained by adaptation to the membrane potential. However, it could also be the case that most threshold variability reflects noise and processes other than threshold adaptation. Here, we investigated threshold variation in auditory neurons responses recorded in vivo in barn owls. We found that spike threshold is quantitatively predicted by a model in which the threshold adapts, tracking the membrane potential at a short timescale. As a result, in these neurons, slow voltage fluctuations do not contribute to spiking because they are filtered by threshold adaptation. More importantly, these neurons can only respond to input spikes arriving together on a millisecond timescale. These results demonstrate that fast adaptation to the membrane potential captures spike threshold variability in vivo.

SELECT NUMERICAL METHODS FOR MODELING THE DYNAMICS SYSTEMS

Directory of Open Access Journals (Sweden)

Tetiana D. Panchenko

2016-07-01

Full Text Available The article deals with the creation of methodical support for mathematical modeling of dynamic processes in elements of the systems and complexes. As mathematical models ordinary differential equations have been used. The coefficients of the equations of the models can be nonlinear functions of the process. The projection-grid method is used as the main tool. It has been described iterative method algorithms taking into account the approximate solution prior to the first iteration and proposed adaptive control computing process. The original method of estimation error in the calculation solutions as well as for a given level of error of the technique solutions purpose adaptive method for solving configuration parameters is offered. A method for setting an adaptive method for solving the settings for a given level of error is given. The proposed method can be used for distributed computing.

Adaptive Actor-Critic Design-Based Integral Sliding-Mode Control for Partially Unknown Nonlinear Systems With Input Disturbances.

Science.gov (United States)

Fan, Quan-Yong; Yang, Guang-Hong

2016-01-01

This paper is concerned with the problem of integral sliding-mode control for a class of nonlinear systems with input disturbances and unknown nonlinear terms through the adaptive actor-critic (AC) control method. The main objective is to design a sliding-mode control methodology based on the adaptive dynamic programming (ADP) method, so that the closed-loop system with time-varying disturbances is stable and the nearly optimal performance of the sliding-mode dynamics can be guaranteed. In the first step, a neural network (NN)-based observer and a disturbance observer are designed to approximate the unknown nonlinear terms and estimate the input disturbances, respectively. Based on the NN approximations and disturbance estimations, the discontinuous part of the sliding-mode control is constructed to eliminate the effect of the disturbances and attain the expected equivalent sliding-mode dynamics. Then, the ADP method with AC structure is presented to learn the optimal control for the sliding-mode dynamics online. Reconstructed tuning laws are developed to guarantee the stability of the sliding-mode dynamics and the convergence of the weights of critic and actor NNs. Finally, the simulation results are presented to illustrate the effectiveness of the proposed method.

Full-Scaled Advanced Systems Testbed: Ensuring Success of Adaptive Control Research Through Project Lifecycle Risk Mitigation

Science.gov (United States)

Pavlock, Kate M.

2011-01-01

The National Aeronautics and Space Administration's Dryden Flight Research Center completed flight testing of adaptive controls research on the Full-Scale Advance Systems Testbed (FAST) in January of 2011. The research addressed technical challenges involved with reducing risk in an increasingly complex and dynamic national airspace. Specific challenges lie with the development of validated, multidisciplinary, integrated aircraft control design tools and techniques to enable safe flight in the presence of adverse conditions such as structural damage, control surface failures, or aerodynamic upsets. The testbed is an F-18 aircraft serving as a full-scale vehicle to test and validate adaptive flight control research and lends a significant confidence to the development, maturation, and acceptance process of incorporating adaptive control laws into follow-on research and the operational environment. The experimental systems integrated into FAST were designed to allow for flexible yet safe flight test evaluation and validation of modern adaptive control technologies and revolve around two major hardware upgrades: the modification of Production Support Flight Control Computers (PSFCC) and integration of two, fourth-generation Airborne Research Test Systems (ARTS). Post-hardware integration verification and validation provided the foundation for safe flight test of Nonlinear Dynamic Inversion and Model Reference Aircraft Control adaptive control law experiments. To ensure success of flight in terms of cost, schedule, and test results, emphasis on risk management was incorporated into early stages of design and flight test planning and continued through the execution of each flight test mission. Specific consideration was made to incorporate safety features within the hardware and software to alleviate user demands as well as into test processes and training to reduce human factor impacts to safe and successful flight test. This paper describes the research configuration

A Memristor-Based Hyperchaotic Complex Lü System and Its Adaptive Complex Generalized Synchronization

Directory of Open Access Journals (Sweden)

Shibing Wang

2016-02-01

Full Text Available This paper introduces a new memristor-based hyperchaotic complex Lü system (MHCLS and investigates its adaptive complex generalized synchronization (ACGS. Firstly, the complex system is constructed based on a memristor-based hyperchaotic real Lü system, and its properties are analyzed theoretically. Secondly, its dynamical behaviors, including hyperchaos, chaos, transient phenomena, as well as periodic behaviors, are explored numerically by means of bifurcation diagrams, Lyapunov exponents, phase portraits, and time history diagrams. Thirdly, an adaptive controller and a parameter estimator are proposed to realize complex generalized synchronization and parameter identification of two identical MHCLSs with unknown parameters based on Lyapunov stability theory. Finally, the numerical simulation results of ACGS and its applications to secure communication are presented to verify the feasibility and effectiveness of the proposed method.

Electromechanical dynamic analysis for the drum driving system of the long-wall shearer

Directory of Open Access Journals (Sweden)

Changzhao Liu

2015-10-01

Full Text Available The drum driving system is one of the weakest parts of the long-wall shearer, and some methods are also needed to monitor and control the long-wall shearer to adapt to the important trend of unmanned operation in future mining systems. Therefore, it is essential to conduct an electromechanical dynamic analysis for the drum driving system of the long-wall shearer. First, a torsional dynamic model of planetary gears is proposed which is convenient to be connected to the electric motor model for electromechanical dynamic analysis. Next, an electromechanical dynamic model for the drum driving system is constructed including the electric motor, the gear transmission system, and the drum. Then, the electromechanical dynamic characteristics are simulated when the shock loads are acted on the drum driving system. Finally, some advices are proposed for improving the reliability, monitoring the operating state, and choosing the control signals of the long-wall shearer based on the simulation.

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.

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

Environmentally-adapted local energy systems

Energy Technology Data Exchange (ETDEWEB)

Moe, N; Oefverholm, E [NUTEK, Stockholm (Sweden); Andersson, Owe [EKAN Gruppen (Sweden); Froste, H [Swedish Environmental Protection Agency, Stockholm (Sweden)

1997-10-01

Energy companies, municipalities, property companies, firms of consultants, environmental groups and individuals are examples of players working locally to shape environmentally adapted energy systems. These players have needed information making them better able to make decisions on cost-efficient, environmentally-adapted energy systems. This book answers many of the questions they have put. The volume is mainly based on Swedish handbooks produced by the Swedish National Board for Industrial and Technical Development, NUTEK, together with the Swedish Environmental Protection Agency. These handbooks have been used in conjunction with municipal energy planning, local Agenda 21 work, to provide a basis for deciding on concrete local energy systems. The contents in brief: -The book throws new light on the concept of energy efficiency; -A section on the environment compares how air-polluting emissions vary with different methods of energy production; -A section contains more than 40 ideas for measures which can be profitable, reduce energy consumption and the impact on the environment all at the same time; -The book gives concrete examples of new, alternative and environmentally-adapted local energy systems. More efficient use of energy is included as a possible change of energy system; -The greatest emphasis is laid upon alternative energy systems for heating. It may be heating in a house, block of flats, office building or school; -Finally, there are examples of environmentally-adapted local energy planning.

Use of dynamic grid adaption in the ASWR-method

International Nuclear Information System (INIS)

Graf, U.; Romstedt, P.; Werner, W.

1985-01-01

A dynamic grid adaption method has been developed for use with the ASWR-method. The method automatically adapts the number and position of the spatial meshpoints as the solution of hyperbolic or parabolic vector partial differential equations progresses in time. The mesh selection algorithm is based on the minimization of the L 2 -norm of the spatial discretization error. The method permits accurate calculation of the evolution of inhomogenities like wave fronts, shock layers and other sharp transitions, while generally using a coarse computational grid. The number of required mesh points is significantly reduced, relative to a fixed Eulerian grid. Since the mesh selection algorithm is computationally inexpensive, a corresponding reduction of computing time results

Dynamic Post-Earthquake Image Segmentation with an Adaptive Spectral-Spatial Descriptor

Directory of Open Access Journals (Sweden)

Genyun Sun

2017-08-01

Full Text Available The region merging algorithm is a widely used segmentation technique for very high resolution (VHR remote sensing images. However, the segmentation of post-earthquake VHR images is more difficult due to the complexity of these images, especially high intra-class and low inter-class variability among damage objects. Herein two key issues must be resolved: the first is to find an appropriate descriptor to measure the similarity of two adjacent regions since they exhibit high complexity among the diverse damage objects, such as landslides, debris flow, and collapsed buildings. The other is how to solve over-segmentation and under-segmentation problems, which are commonly encountered with conventional merging strategies due to their strong dependence on local information. To tackle these two issues, an adaptive dynamic region merging approach (ADRM is introduced, which combines an adaptive spectral-spatial descriptor and a dynamic merging strategy to adapt to the changes of merging regions for successfully detecting objects scattered globally in a post-earthquake image. In the new descriptor, the spectral similarity and spatial similarity of any two adjacent regions are automatically combined to measure their similarity. Accordingly, the new descriptor offers adaptive semantic descriptions for geo-objects and thus is capable of characterizing different damage objects. Besides, in the dynamic region merging strategy, the adaptive spectral-spatial descriptor is embedded in the defined testing order and combined with graph models to construct a dynamic merging strategy. The new strategy can find the global optimal merging order and ensures that the most similar regions are merged at first. With combination of the two strategies, ADRM can identify spatially scattered objects and alleviates the phenomenon of over-segmentation and under-segmentation. The performance of ADRM has been evaluated by comparing with four state-of-the-art segmentation methods

The puzzle of partial migration: Adaptive dynamics and evolutionary game theory perspectives.

Science.gov (United States)

De Leenheer, Patrick; Mohapatra, Anushaya; Ohms, Haley A; Lytle, David A; Cushing, J M

2017-01-07

We consider the phenomenon of partial migration which is exhibited by populations in which some individuals migrate between habitats during their lifetime, but others do not. First, using an adaptive dynamics approach, we show that partial migration can be explained on the basis of negative density dependence in the per capita fertilities alone, provided that this density dependence is attenuated for increasing abundances of the subtypes that make up the population. We present an exact formula for the optimal proportion of migrants which is expressed in terms of the vital rates of migrant and non-migrant subtypes only. We show that this allocation strategy is both an evolutionary stable strategy (ESS) as well as a convergence stable strategy (CSS). To establish the former, we generalize the classical notion of an ESS because it is based on invasion exponents obtained from linearization arguments, which fail to capture the stabilizing effects of the nonlinear density dependence. These results clarify precisely when the notion of a "weak ESS", as proposed in Lundberg (2013) for a related model, is a genuine ESS. Secondly, we use an evolutionary game theory approach, and confirm, once again, that partial migration can be attributed to negative density dependence alone. In this context, the result holds even when density dependence is not attenuated. In this case, the optimal allocation strategy towards migrants is the same as the ESS stemming from the analysis based on the adaptive dynamics. The key feature of the population models considered here is that they are monotone dynamical systems, which enables a rather comprehensive mathematical analysis. Copyright © 2016 Elsevier Ltd. All rights reserved.

Discrete-Time Local Value Iteration Adaptive Dynamic Programming: Admissibility and Termination Analysis.

Science.gov (United States)

Wei, Qinglai; Liu, Derong; Lin, Qiao

In this paper, a novel local value iteration adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon optimal control problems for discrete-time nonlinear systems. The focuses of this paper are to study admissibility properties and the termination criteria of discrete-time local value iteration ADP algorithms. In the discrete-time local value iteration ADP algorithm, the iterative value functions and the iterative control laws are both updated in a given subset of the state space in each iteration, instead of the whole state space. For the first time, admissibility properties of iterative control laws are analyzed for the local value iteration ADP algorithm. New termination criteria are established, which terminate the iterative local ADP algorithm with an admissible approximate optimal control law. Finally, simulation results are given to illustrate the performance of the developed algorithm.In this paper, a novel local value iteration adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon optimal control problems for discrete-time nonlinear systems. The focuses of this paper are to study admissibility properties and the termination criteria of discrete-time local value iteration ADP algorithms. In the discrete-time local value iteration ADP algorithm, the iterative value functions and the iterative control laws are both updated in a given subset of the state space in each iteration, instead of the whole state space. For the first time, admissibility properties of iterative control laws are analyzed for the local value iteration ADP algorithm. New termination criteria are established, which terminate the iterative local ADP algorithm with an admissible approximate optimal control law. Finally, simulation results are given to illustrate the performance of the developed algorithm.

Dynamic Reconfiguration of a RGBD Sensor Based on QoS and QoC Requirements in Distributed Systems.

Science.gov (United States)

Munera, Eduardo; Poza-Lujan, Jose-Luis; Posadas-Yagüe, Juan-Luis; Simó-Ten, José-Enrique; Noguera, Juan Fco Blanes

2015-07-24

The inclusion of embedded sensors into a networked system provides useful information for many applications. A Distributed Control System (DCS) is one of the clearest examples where processing and communications are constrained by the client's requirements and the capacity of the system. An embedded sensor with advanced processing and communications capabilities supplies high level information, abstracting from the data acquisition process and objects recognition mechanisms. The implementation of an embedded sensor/actuator as a Smart Resource permits clients to access sensor information through distributed network services. Smart resources can offer sensor services as well as computing, communications and peripheral access by implementing a self-aware based adaptation mechanism which adapts the execution profile to the context. On the other hand, information integrity must be ensured when computing processes are dynamically adapted. Therefore, the processing must be adapted to perform tasks in a certain lapse of time but always ensuring a minimum process quality. In the same way, communications must try to reduce the data traffic without excluding relevant information. The main objective of the paper is to present a dynamic configuration mechanism to adapt the sensor processing and communication to the client's requirements in the DCS. This paper describes an implementation of a smart resource based on a Red, Green, Blue, and Depth (RGBD) sensor in order to test the dynamic configuration mechanism presented.

Adaptive Indoor Positioning Model Based on WLAN-Fingerprinting for Dynamic and Multi-Floor Environments

Directory of Open Access Journals (Sweden)

Iyad Husni Alshami

2017-08-01

Full Text Available The Global Positioning System demonstrates the significance of Location Based Services but it cannot be used indoors due to the lack of line of sight between satellites and receivers. Indoor Positioning Systems are needed to provide indoor Location Based Services. Wireless LAN fingerprints are one of the best choices for Indoor Positioning Systems because of their low cost, and high accuracy, however they have many drawbacks: creating radio maps is time consuming, the radio maps will become outdated with any environmental change, different mobile devices read the received signal strength (RSS differently, and peoples’ presence in LOS between access points and mobile device affects the RSS. This research proposes a new Adaptive Indoor Positioning System model (called DIPS based on: a dynamic radio map generator, RSS certainty technique and peoples’ presence effect integration for dynamic and multi-floor environments. Dynamic in our context refers to the effects of people and device heterogeneity. DIPS can achieve 98% and 92% positioning accuracy for floor and room positioning, and it achieves 1.2 m for point positioning error. RSS certainty enhanced the positioning accuracy for floor and room for different mobile devices by 11% and 9%. Then by considering the peoples’ presence effect, the error is reduced by 0.2 m. In comparison with other works, DIPS achieves better positioning without extra devices.

Evolution dynamics of a model for gene duplication under adaptive conflict

Science.gov (United States)

Ancliff, Mark; Park, Jeong-Man

2014-06-01

We present and solve the dynamics of a model for gene duplication showing escape from adaptive conflict. We use a Crow-Kimura quasispecies model of evolution where the fitness landscape is a function of Hamming distances from two reference sequences, which are assumed to optimize two different gene functions, to describe the dynamics of a mixed population of individuals with single and double copies of a pleiotropic gene. The evolution equations are solved through a spin coherent state path integral, and we find two phases: one is an escape from an adaptive conflict phase, where each copy of a duplicated gene evolves toward subfunctionalization, and the other is a duplication loss of function phase, where one copy maintains its pleiotropic form and the other copy undergoes neutral mutation. The phase is determined by a competition between the fitness benefits of subfunctionalization and the greater mutational load associated with maintaining two gene copies. In the escape phase, we find a dynamics of an initial population of single gene sequences only which escape adaptive conflict through gene duplication and find that there are two time regimes: until a time t* single gene sequences dominate, and after t* double gene sequences outgrow single gene sequences. The time t* is identified as the time necessary for subfunctionalization to evolve and spread throughout the double gene sequences, and we show that there is an optimum mutation rate which minimizes this time scale.

Adaptive chaos control and synchronization in only locally Lipschitz systems

International Nuclear Information System (INIS)

Lin Wei

2008-01-01

In the existing results on chaos control and synchronization based on the adaptive controlling technique (ACT), a uniform Lipschitz condition on a given dynamical system is always assumed in advance. However, without this uniform Lipschitz condition, the ACT might be failed in both theoretical analysis and in numerical experiment. This Letter shows how to utilize the ACT to get a rigorous control for the system which is not uniformly Lipschitz but only locally Lipschitz, and even for the system which has unbounded trajectories. In fact, the ACT is proved to possess some limitation, which is actually induced by the nonlinear degree of the original system. Consequently, a piecewise ACT is proposed so as to improve the performance of the existing techniques

Shape memory alloys applied to improve rotor-bearing system dynamics - an experimental investigation

DEFF Research Database (Denmark)

Enemark, Søren; Santos, Ilmar; Savi, Marcelo A.

2015-01-01

passing through critical speeds. In this work, the feasibility of applying shape memory alloys to a rotating system is experimentally investigated. Shape memory alloys can change their stiffness with temperature variations and thus they may change system dynamics. Shape memory alloys also exhibit...... perturbations and mass imbalance responses of the rotor-bearing system at different temperatures and excitation frequencies are carried out to determine the dynamic behaviour of the system. The behaviour and the performance in terms of vibration reduction and system adaptability are compared against a benchmark...... configuration comprised by the same system having steel springs instead of shape memory alloy springs. The experimental results clearly show that the stiffness changes and hysteretic behaviour of the shape memory alloys springs alter system dynamics both in terms of critical speeds and mode shapes. Vibration...

Adaptive fuzzy dynamic surface control for the chaotic permanent magnet synchronous motor using Nussbaum gain

Energy Technology Data Exchange (ETDEWEB)

Luo, Shaohua [School of Automation, Chongqing University, Chongqing 400044, China and College of Mechanical Engineering, Hunan University of Arts and Science, Hunan 415000 (China)

2014-09-01

This paper is concerned with the problem of adaptive fuzzy dynamic surface control (DSC) for the permanent magnet synchronous motor (PMSM) system with chaotic behavior, disturbance and unknown control gain and parameters. Nussbaum gain is adopted to cope with the situation that the control gain is unknown. And the unknown items can be estimated by fuzzy logic system. The proposed controller guarantees that all the signals in the closed-loop system are bounded and the system output eventually converges to a small neighborhood of the desired reference signal. Finally, the numerical simulations indicate that the proposed scheme can suppress the chaos of PMSM and show the effectiveness and robustness of the proposed method.

Eco-evolutionary dynamics in a coevolving host-virus system.

Science.gov (United States)

Frickel, Jens; Sieber, Michael; Becks, Lutz

2016-04-01

Eco-evolutionary dynamics have been shown to be important for understanding population and community stability and their adaptive potential. However, coevolution in the framework of eco-evolutionary theory has not been addressed directly. Combining experiments with an algal host and its viral parasite, and mathematical model analyses we show eco-evolutionary dynamics in antagonistic coevolving populations. The interaction between antagonists initially resulted in arms race dynamics (ARD) with selective sweeps, causing oscillating host-virus population dynamics. However, ARD ended and populations stabilised after the evolution of a general resistant host, whereas a trade-off between host resistance and growth then maintained host diversity over time (trade-off driven dynamics). Most importantly, our study shows that the interaction between ecology and evolution had important consequences for the predictability of the mode and tempo of adaptive change and for the stability and adaptive potential of populations. © 2016 John Wiley & Sons Ltd/CNRS.

Application of System Dynamics Methodology in Population Analysis

Directory of Open Access Journals (Sweden)

August Turina

2009-09-01

Full Text Available The goal of this work is to present the application of system dynamics and system thinking, as well as the advantages and possible defects of this analytic approach, in order to improve the analysis of complex systems such as population and, thereby, to monitor more effectively the underlying causes of migrations. This methodology has long been present in interdisciplinary scientific circles, but its scientific contribution has not been sufficiently applied in analysis practice in Croatia. Namely, the major part of system analysis is focused on detailed complexity rather than on dynamic complexity. Generally, the science of complexity deals with emergence, innovation, learning and adaptation. Complexity is viewed according to the number of system components, or through a number of combinations that must be continually analyzed in order to understand and consequently provide adequate decisions. Simulations containing thousands of variables and complex arrays of details distract overall attention from the basic cause patterns and key inter-relations emerging and prevailing within an analyzed population. Systems thinking offers a holistic and integral perspective for observation of the world.

Exploring adaptive program behavior

DEFF Research Database (Denmark)

Bonnichsen, Lars Frydendal; Probst, Christian W.

Modern computer systems are increasingly complex, with ever changing bottlenecks. This makes it difficult to ensure consistent performance when porting software, or even running it. Adaptivity, ie, switching between program variations, and dynamic recompilation have been suggested as solutions....... Both solutions come at a cost; adaptivity issues a runtime overhead and requires more design effort, while dynamic recompilation takes time to perform. In this project, we plan to investigate the possibilities, limitations, and benefits of these techniques. This abstract covers our thoughts on how...

Molecular dynamics coupled with a virtual system for effective conformational sampling.

Science.gov (United States)

Hayami, Tomonori; Kasahara, Kota; Nakamura, Haruki; Higo, Junichi

2018-07-15

An enhanced conformational sampling method is proposed: virtual-system coupled canonical molecular dynamics (VcMD). Although VcMD enhances sampling along a reaction coordinate, this method is free from estimation of a canonical distribution function along the reaction coordinate. This method introduces a virtual system that does not necessarily obey a physical law. To enhance sampling the virtual system couples with a molecular system to be studied. Resultant snapshots produce a canonical ensemble. This method was applied to a system consisting of two short peptides in an explicit solvent. Conventional molecular dynamics simulation, which is ten times longer than VcMD, was performed along with adaptive umbrella sampling. Free-energy landscapes computed from the three simulations mutually converged well. The VcMD provided quicker association/dissociation motions of peptides than the conventional molecular dynamics did. The VcMD method is applicable to various complicated systems because of its methodological simplicity. © 2018 Wiley Periodicals, Inc. © 2018 Wiley Periodicals, Inc.

Adaptive control of structural balance for complex dynamical networks based on dynamic coupling of nodes

Science.gov (United States)

Gao, Zilin; Wang, Yinhe; Zhang, Lili

2018-02-01

In the existing research results of the complex dynamical networks controlled, the controllers are mainly used to guarantee the synchronization or stabilization of the nodes’ state, and the terms coupled with connection relationships may affect the behaviors of nodes, this obviously ignores the dynamic common behavior of the connection relationships between the nodes. In fact, from the point of view of large-scale system, a complex dynamical network can be regarded to be composed of two time-varying dynamic subsystems, which can be called the nodes subsystem and the connection relationships subsystem, respectively. Similar to the synchronization or stabilization of the nodes subsystem, some characteristic phenomena can be also emerged in the connection relationships subsystem. For example, the structural balance in the social networks and the synaptic facilitation in the biological neural networks. This paper focuses on the structural balance in dynamic complex networks. Generally speaking, the state of the connection relationships subsystem is difficult to be measured accurately in practical applications, and thus it is not easy to implant the controller directly into the connection relationships subsystem. It is noted that the nodes subsystem and the relationships subsystem are mutually coupled, which implies that the state of the connection relationships subsystem can be affected by the controllable state of nodes subsystem. Inspired by this observation, by using the structural balance theory of triad, the controller with the parameter adaptive law is proposed for the nodes subsystem in this paper, which may ensure the connection relationship matrix to approximate a given structural balance matrix in the sense of the uniformly ultimately bounded (UUB). That is, the structural balance may be obtained by employing the controlling state of the nodes subsystem. Finally, the simulations are used to show the validity of the method in this paper.

Development and application of the dynamic system doctor to nuclear reactor probabilistic risk assessments

International Nuclear Information System (INIS)

Kunsman, David Marvin; Aldemir, Tunc; Rutt, Benjamin; Metzroth, Kyle; Catalyurek, Umit; Denning, Richard; Hakobyan, Aram; Dunagan, Sean C.

2008-01-01

This LDRD project has produced a tool that makes probabilistic risk assessments (PRAs) of nuclear reactors - analyses which are very resource intensive - more efficient. PRAs of nuclear reactors are being increasingly relied on by the United States Nuclear Regulatory Commission (U.S.N.R.C.) for licensing decisions for current and advanced reactors. Yet, PRAs are produced much as they were 20 years ago. The work here applied a modern systems analysis technique to the accident progression analysis portion of the PRA; the technique was a system-independent multi-task computer driver routine. Initially, the objective of the work was to fuse the accident progression event tree (APET) portion of a PRA to the dynamic system doctor (DSD) created by Ohio State University. Instead, during the initial efforts, it was found that the DSD could be linked directly to a detailed accident progression phenomenological simulation code - the type on which APET construction and analysis relies, albeit indirectly - and thereby directly create and analyze the APET. The expanded DSD computational architecture and infrastructure that was created during this effort is called ADAPT (Analysis of Dynamic Accident Progression Trees). ADAPT is a system software infrastructure that supports execution and analysis of multiple dynamic event-tree simulations on distributed environments. A simulator abstraction layer was developed, and a generic driver was implemented for executing simulators on a distributed environment. As a demonstration of the use of the methodological tool, ADAPT was applied to quantify the likelihood of competing accident progression pathways occurring for a particular accident scenario in a particular reactor type using MELCOR, an integrated severe accident analysis code developed at Sandia. (ADAPT was intentionally created with flexibility, however, and is not limited to interacting with only one code. With minor coding changes to input files, ADAPT can be linked to other

In-Flight Suppression of an Unstable F/A-18 Structural Mode Using the Space Launch System Adaptive Augmenting Control System

Science.gov (United States)

VanZwieten, Tannen S.; Gilligan, Eric T.; Wall, John H.; Miller, Christopher J.; Hanson, Curtis E.; Orr, Jeb S.

2015-01-01

NASA's Space Launch System (SLS) Flight Control System (FCS) includes an Adaptive Augmenting Control (AAC) component which employs a multiplicative gain update law to enhance the performance and robustness of the baseline control system for extreme off-nominal scenarios. The SLS FCS algorithm including AAC has been flight tested utilizing a specially outfitted F/A-18 fighter jet in which the pitch axis control of the aircraft was performed by a Non-linear Dynamic Inversion (NDI) controller, SLS reference models, and the SLS flight software prototype. This paper describes test cases from the research flight campaign in which the fundamental F/A-18 airframe structural mode was identified using post-flight frequency-domain reconstruction, amplified to result in closed loop instability, and suppressed in-flight by the SLS adaptive control system.

In-Flight Suppression of a Destabilized F/A-18 Structural Mode Using the Space Launch System Adaptive Augmenting Control System

Science.gov (United States)

Wall, John H.; VanZwieten, Tannen S.; Gilligan, Eric T.; Miller, Christopher J.; Hanson, Curtis E.; Orr, Jeb S.

2015-01-01

NASA's Space Launch System (SLS) Flight Control System (FCS) includes an Adaptive Augmenting Control (AAC) component which employs a multiplicative gain update law to enhance the performance and robustness of the baseline control system for extreme off nominal scenarios. The SLS FCS algorithm including AAC has been flight tested utilizing a specially outfitted F/A-18 fighter jet in which the pitch axis control of the aircraft was performed by a Non-linear Dynamic Inversion (NDI) controller, SLS reference models, and the SLS flight software prototype. This paper describes test cases from the research flight campaign in which the fundamental F/A-18 airframe structural mode was identified using frequency-domain reconstruction of flight data, amplified to result in closed loop instability, and suppressed in-flight by the SLS adaptive control system.

On Event-Triggered Adaptive Architectures for Decentralized and Distributed Control of Large-Scale Modular Systems.

Science.gov (United States)

Albattat, Ali; Gruenwald, Benjamin C; Yucelen, Tansel

2016-08-16

The last decade has witnessed an increased interest in physical systems controlled over wireless networks (networked control systems). These systems allow the computation of control signals via processors that are not attached to the physical systems, and the feedback loops are closed over wireless networks. The contribution of this paper is to design and analyze event-triggered decentralized and distributed adaptive control architectures for uncertain networked large-scale modular systems; that is, systems consist of physically-interconnected modules controlled over wireless networks. Specifically, the proposed adaptive architectures guarantee overall system stability while reducing wireless network utilization and achieving a given system performance in the presence of system uncertainties that can result from modeling and degraded modes of operation of the modules and their interconnections between each other. In addition to the theoretical findings including rigorous system stability and the boundedness analysis of the closed-loop dynamical system, as well as the characterization of the effect of user-defined event-triggering thresholds and the design parameters of the proposed adaptive architectures on the overall system performance, an illustrative numerical example is further provided to demonstrate the efficacy of the proposed decentralized and distributed control approaches.

On Event-Triggered Adaptive Architectures for Decentralized and Distributed Control of Large-Scale Modular Systems

Directory of Open Access Journals (Sweden)

Ali Albattat

2016-08-01

Full Text Available The last decade has witnessed an increased interest in physical systems controlled over wireless networks (networked control systems. These systems allow the computation of control signals via processors that are not attached to the physical systems, and the feedback loops are closed over wireless networks. The contribution of this paper is to design and analyze event-triggered decentralized and distributed adaptive control architectures for uncertain networked large-scale modular systems; that is, systems consist of physically-interconnected modules controlled over wireless networks. Specifically, the proposed adaptive architectures guarantee overall system stability while reducing wireless network utilization and achieving a given system performance in the presence of system uncertainties that can result from modeling and degraded modes of operation of the modules and their interconnections between each other. In addition to the theoretical findings including rigorous system stability and the boundedness analysis of the closed-loop dynamical system, as well as the characterization of the effect of user-defined event-triggering thresholds and the design parameters of the proposed adaptive architectures on the overall system performance, an illustrative numerical example is further provided to demonstrate the efficacy of the proposed decentralized and distributed control approaches.

Integrating Systems Health Management with Adaptive Controls for a Utility-Scale Wind Turbine

Science.gov (United States)

Frost, Susan A.; Goebel, Kai; Trinh, Khanh V.; Balas, Mark J.; Frost, Alan M.

2011-01-01

Increasing turbine up-time and reducing maintenance costs are key technology drivers for wind turbine operators. Components within wind turbines are subject to considerable stresses due to unpredictable environmental conditions resulting from rapidly changing local dynamics. Systems health management has the aim to assess the state-of-health of components within a wind turbine, to estimate remaining life, and to aid in autonomous decision-making to minimize damage. Advanced adaptive controls can provide the mechanism to enable optimized operations that also provide the enabling technology for Systems Health Management goals. The work reported herein explores the integration of condition monitoring of wind turbine blades with contingency management and adaptive controls. Results are demonstrated using a high fidelity simulator of a utility-scale wind turbine.

Blended particle filters for large-dimensional chaotic dynamical systems

Science.gov (United States)

Majda, Andrew J.; Qi, Di; Sapsis, Themistoklis P.

2014-01-01

A major challenge in contemporary data science is the development of statistically accurate particle filters to capture non-Gaussian features in large-dimensional chaotic dynamical systems. Blended particle filters that capture non-Gaussian features in an adaptively evolving low-dimensional subspace through particles interacting with evolving Gaussian statistics on the remaining portion of phase space are introduced here. These blended particle filters are constructed in this paper through a mathematical formalism involving conditional Gaussian mixtures combined with statistically nonlinear forecast models compatible with this structure developed recently with high skill for uncertainty quantification. Stringent test cases for filtering involving the 40-dimensional Lorenz 96 model with a 5-dimensional adaptive subspace for nonlinear blended filtering in various turbulent regimes with at least nine positive Lyapunov exponents are used here. These cases demonstrate the high skill of the blended particle filter algorithms in capturing both highly non-Gaussian dynamical features as well as crucial nonlinear statistics for accurate filtering in extreme filtering regimes with sparse infrequent high-quality observations. The formalism developed here is also useful for multiscale filtering of turbulent systems and a simple application is sketched below. PMID:24825886

Nonequilibrium Enhances Adaptation Efficiency of Stochastic Biochemical Systems.

Directory of Open Access Journals (Sweden)

Chen Jia

Full Text Available Adaptation is a crucial biological function possessed by many sensory systems. Early work has shown that some influential equilibrium models can achieve accurate adaptation. However, recent studies indicate that there are close relationships between adaptation and nonequilibrium. In this paper, we provide an explanation of these two seemingly contradictory results based on Markov models with relatively simple networks. We show that as the nonequilibrium driving becomes stronger, the system under consideration will undergo a phase transition along a fixed direction: from non-adaptation to simple adaptation then to oscillatory adaptation, while the transition in the opposite direction is forbidden. This indicates that although adaptation may be observed in equilibrium systems, it tends to occur in systems far away from equilibrium. In addition, we find that nonequilibrium will improve the performance of adaptation by enhancing the adaptation efficiency. All these results provide a deeper insight into the connection between adaptation and nonequilibrium. Finally, we use a more complicated network model of bacterial chemotaxis to validate the main results of this paper.

Recommendation System for Adaptive Learning.

Science.gov (United States)

Chen, Yunxiao; Li, Xiaoou; Liu, Jingchen; Ying, Zhiliang

2018-01-01

An adaptive learning system aims at providing instruction tailored to the current status of a learner, differing from the traditional classroom experience. The latest advances in technology make adaptive learning possible, which has the potential to provide students with high-quality learning benefit at a low cost. A key component of an adaptive learning system is a recommendation system, which recommends the next material (video lectures, practices, and so on, on different skills) to the learner, based on the psychometric assessment results and possibly other individual characteristics. An important question then follows: How should recommendations be made? To answer this question, a mathematical framework is proposed that characterizes the recommendation process as a Markov decision problem, for which decisions are made based on the current knowledge of the learner and that of the learning materials. In particular, two plain vanilla systems are introduced, for which the optimal recommendation at each stage can be obtained analytically.