Approximate Dynamic Model Research Articles

Dynamic Modeling for a Thin Plate with Large Deformation

In this paper, based on the nonlinear elastic theory, dynamic modeling for a thin plate with large deformation is proposed considering high-order deformation terms using hybrid-coordinate formulation. Through the calculation the potential energy and kinetic energy of the plate, rigid-flexible coupling dynamic equations is established. It is shown that the difference between the deformation results obtained by the present high-order dynamic model and those obtained by one-order approximate dynamic model is not significant in case of large deformation ,both them can calculation large deformation problem. However, the amplitude of vibration obtained by the present high-order dynamicmodel is smaller than that obtained by one-order approximate dynamic model. Furthermore, dynamic stiffening and softening effect of the rigid-flexible coupling system undergoing translational large overall motion is investigated. It is shown that with the reduce of the modulus of elasticity, the influence of the translational acceleration on the vibration frequency of the rigid-flexible coupling system is more significant.

Robust vehicle lateral stabilisation via set-based methods for uncertain piecewise affine systems

The paper presents the design of a lateral stability controller for ground vehicles based on front steering and four wheels independent braking. The control objective is to track yaw rate and lateral velocity reference signals while avoiding front and rear wheel traction force saturation. Control design is based on an approximate piecewise-affine nonlinear dynamical model of the vehicle. Vehicle longitudinal velocity and drivers steering input are modelled as measured disturbances taking values in a compact set. A time-optimal control strategy which ensures convergence into a maximal robust control invariant (RCI) set is proposed. This paper presents the uncertain model, the RCI computation, and the control algorithm. Experimental tests at high-speed on ice with aggressive driver manoeuvres show the effectiveness of the proposed scheme.

Large-eddy simulation of natural convection in an asymmetrically-heated vertical parallel-plate channel: Assessment of subgrid-scale models

The performance of four different large-eddy simulation subgrid-scale models has been examined a posteriori for natural convection in an asymmetrically-heated vertical parallel-plate channel with a high aspect ratio. The compressible three-dimensional Favre-filtered mass, momentum and energy conservation equations have been closed using the Smagorinsky, dynamic, approximate localised dynamic and Vreman models. A two-stage predictor–corrector numerical methodology for low-Mach-number compressible flows was adopted to strongly couple the density with the Navier–Stokes equations. Based on the comparison with experimental data, it has been shown that the Smagorinsky model predicts inaccurate near-wall flow dynamics and delayed transitional behaviour while both dynamic procedures to compute the Smagorinsky model coefficient result in over prediction of wall temperatures, suggesting an under estimation of subgrid-scale dissipation. The time extrapolation procedure utilised in the approximate localised dynamic model has been shown to produce better adaptation towards the local flow behaviour when compared with the standard dynamic model. At the same time, time-averaged wall temperature and velocity field profiles have been well captured by the Vreman model, demonstrating its superiority when compared to the rest of the models.

Adaptive Sliding Mode Control of Single‐Phase Shunt Active Power Filter

This paper presents a thorough study of the adaptive sliding mode technique with application to single‐phase shunt active power filter (APF). Based on the basic principle of single‐phase shunt APF, the approximate dynamic model is derived. A model reference adaptive sliding mode control algorithm is proposed to implement the harmonic compensation for the single‐phase shunt APF. This method will use the tracking error of harmonic and APF current as the control input and adopt the tracking error of reference model and APF output as the control objects of adaptive sliding mode. In the reference current track loop, a novel adaptive sliding mode controller is implemented to tracking the reference currents, thus improving harmonic treating performance. Simulation results demonstrate the satisfactory control performance and rapid compensation ability of the proposed control approach under different conditions of the nonlinear load current distortion and the mutation load, respectively.

A Model for Vast Panels of Volatilities

We introduce an approximate dynamic factor model for modeling and forecasting large panels of realized volatilities. Since the model is estimated by means of principal components and low dimensional maximum likelihood, it does not suffer from the curse of dimensionality. We apply the model to a panel of 90 daily realized volatilities pertaining to S&P100 from January 2001 to December 2008. Results show that our model is able to capture the stylized facts of panels of volatilities (comovements, clustering, long memory, dynamic volatility, skewness and heavy tails), and that it performs fairly well in forecasting, in particular in period of turmoil in which it outperforms standard univariate benchmarks.

Evolutionarily stable consumer home range size in relation to resource demography and consumer spatial organization

There is a large variation in home range size within species, yet few models relate that variation to demographic and life-history traits. We derive an approximate deterministic population dynamics model keeping track of spatial structure, via spatial moment equations, from an individual-based spatial consumer-resource model; where space-use of consumers resembles that of central place foragers. Using invasion analyses, we investigate how the evolutionarily stable home range size of the consumer depends on a number of ecological and behavioral traits of both the resource and the consumer. We show that any trait variation leading to a decreased overall resource production or an increased spatial segregation between consumer and resource acts to increase consumer home range size. In this way, we extend theoretical predictions on optimal territory size to a larger range of ecological scenarios where home ranges overlap and population dynamics feedbacks are possible. Consideration of spatial traits such as dispersal distances also generates new results: (1) consumer home range size decreases with increased resource dispersal distance, and (2) when consumer agonistic behavior is weak, more philopatric consumers have larger home ranges. Finally, our results emphasize the role of the spatial correlation between consumer and resource distributions in determining home range size, and suggest resource dispersion is less important.

A Quasi–Maximum Likelihood Approach for Large, Approximate Dynamic Factor Models

Is maximum likelihood suitable for factor models in large cross-sections of time series? We answer this question from both an asymptotic and an empirical perspective. We show that estimates of the common factors based on maximum likelihood are consistent for the size of the cross-section (n) and the sample size (T) going to infinity along any path of n and T and that therefore maximum likelihood is viable for n large. The estimator is robust to misspecification of the cross-sectional and time series correlation of the the idiosyncratic components. In practice, the estimator can be easily implemented using the Kalman smoother and the EM algorithm as in traditional factor analysis.

A computational neuromusculoskeletal model of human arm movements

This work proposes a computational neuromusculoskeletal model of human arm movements. The model consists of three components: the supraspinal neural control system, the spinal motor system, and the muscle-tendon actuation system. In the supraspinal neural system model, the cerebellum is regarded as having feedforward control and the cerebrum as feedback control principally based on the feedback-error learning scheme. This computational model proposes that the feedforward control of the cerebellum may not need to be an explicit locus of an inverse dynamic model. This model also includes the modularly organized spinal motor system such that it simplifies controlling redundant muscular actuators. Cerebellar feedforward control and the spinal motor system are assumed to be adaptive. The two motor adaptations seem to synergistically promote motion flexibility and simplify the neural system structure. The neural control system is combined with the Hill-type muscle-tendon model to generate arm movement. The overall model proposes that an approximate inverse dynamic model may implicitly be constructed over the integrated neuromusculoskeletal system, and it is not necessary to be explicitly computed in a specific motor system. To cope with the human neural system, neuromuscular activation dynamics and neural transmission delays are included in the model. A computational simulation study using the model was implemented to verify the feasibility of the model. Center out reaching movements and learning of those movements as well s generations of figure eightlike movements were computationally tested. A plausible motor control scheme of movement is discussed using the model.

Low order model for the dynamics of bi-stable composite plates

This article presents the derivation and validation of a low order model for the non-linear dynamics of cross-ply bi-stable composite plates focusing on the response of one stable state. The Rayleigh–Ritz method is used to solve the associated linear problem to obtain valuable theoretical insight into how to formulate an approximate non-linear dynamic model. This allows us to follow a Galerkin approach projecting the solution of the non-linear problem onto the mode shapes of the linear problem. The order of the non-linear model is reduced using theoretical results from the linear solution yielding a low order model. The dynamic response of a bi-stable plate specimen is studied to simplify the model further by only keeping the non-linear terms leading to observed oscillations. Simulations for the dynamic response using the derived model are presented showing excellent agreement with the experimentally observed behaviour. Additionally, deflection shapes are measured and compared with the calculated mode shapes, showing good agreement.

Forecasting with Approximate Dynamic Factor Models: The Role of Non-Pervasive Shocks

In this paper we investigate whether accounting for non-pervasive shocks improves the forecast of a factor model. We compare four models on a large panel of US quarterly data: factor models, factor models estimated on selected variables, Bayesian shrinkage, and factor models together with Bayesian shrinkage for the idiosyncratic component. The results of the forecasting exercise show that the four approaches considered perform equally well and produce highly correlated forecasts, meaning that non-pervasive shocks are of no helps in forecasting. We conclude that comovements captured by factor models are informative enough to make accurate forecasts.

A two-step estimator for large approximate dynamic factor models based on Kalman filtering

This paper shows consistency of a two-step estimation of the factors in a dynamic approximate factor model when the panel of time series is large ( n large). In the first step, the parameters of the model are estimated from an OLS on principal components. In the second step, the factors are estimated via the Kalman smoother. The analysis develops the theory for the estimator considered in Giannone et al. (2004) and Giannone et al. (2008) and for the many empirical papers using this framework for nowcasting.

Nowcasting norwegian GDP: the role of asset prices in a small open economy

This article finds that asset prices on Oslo Stock Exchange is the single most important block of data to improve estimates of current quarter GDP in Norway. We use an approximate dynamic factor model that is able to handle new information as it is released, thus the marginal impact on mean square nowcasting error can be studied for a large number of variables. We use a panel of 148 non-synchronous variables. The high informational content in asset prices is explained by reference to the small size of companies on Oslo Stock Exchange and the small and open nature of the Norwegian economy.

Learning the Covariance Dynamics of a Large-Scale Environment for Informative Path Planning of Unmanned Aerial Vehicle Sensors

This work addresses problems regarding trajectory planning for unmanned aerial vehicle sensors. Such sensors are used for taking measurements of large nonlinear systems. The sensor investigations presented here entails methods for improving estimations and predictions of large nonlinear systems. Thoroughly understanding the global system state typically requires probabilistic state estimation. Thus, in order to meet this requirement, the goal is to find trajectories such that the measurements along each trajectory minimize the expected error of the predicted state of the system. The considerable nonlinearity of the dynamics governing these systems necessitates the use of computationally costly Monte-Carlo estimation techniques, which are needed to update the state distribution over time. This computational burden renders planning to be infeasible since the search process must calculate the covariance of the posterior state estimate for each candidate path. To resolve this challenge, this work proposes to replace the computationally intensive numerical prediction process with an approximate covariance dynamics model learned using a nonlinear time-series regression. The use of autoregressive time-series featuring a regularized least squares algorithm facilitates the learning of accurate and efficient parametric models. The learned covariance dynamics are demonstrated to outperform other approximation strategies, such as linearization and partial ensemble propagation, when used for trajectory optimization, in terms of accuracy and speed, with examples of simplified weather forecasting.

A new adaptive PI for bicarbonate regulation in an anaerobic digestion process

This paper deals with the design and validation via numerical simulations of an adaptive proportional integral (PI) controller for bicarbonate regulation in an anaerobic wastewater treatment process. The proportional and integral gains are deduced as a function of the parameters of an approximate dynamical model. First, a brief explanation of the control structure is introduced. After that, a general equation of an adaptive PI controller is proposed. Next, the controller is implemented in order to regulate bicarbonate by two control actions in a completely stirred tank reactor (CSTR): adding a base and manipulating the dilution rate. Finally, a smooth switching mechanism which allows the process to operate in open or closed loop according to the operating conditions is implemented. The performance of the process with the proposed control scheme is validated via simulations. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society

France in the global economy: a structural approximate dynamic factor model analysis

This study identifies the main shocks that cause fluctuations in French output and their channels of transmission. It uses a large-dimensional structural approximate dynamic factor model. There are three main findings. First, common shocks, especially demand shocks, which seem to originate from the U.S., play an important role in explaining French economic activity. While international trade, relative prices, and foreign direct investment (FDI) flows are the main channels of transmission, the stock market, consumer confidence, and interest rates also matter. Second, France’s integration with the rest of the world has increased over time. Third, there is some tentative evidence of regional components in explaining French output fluctuations; country-specific components also contribute. The predominance of exogenous factors affecting French output, the asymmetry in the transmission of shocks, and France’s participation in a currency area argue for making French goods, services, and labor markets as flexible as possible.

Polymer blends used to prepare nifedipine loaded hollow microspheres for a floating-type oral drug delivery system: In vitro evaluation

The aim of this study was to investigate whether hollow microspheres prepared from polymer blends of polyvinyl pyrrolidone (PVP) and ethyl cellulose (EC) could improve the vitro release behavior of the poorly water-soluble drug nifedipine. Hollow microspheres containing nifedipine were prepared by a solvent diffusion-evaporation method using various ratios of PVP and EC codissolved with drug in ethanol/ether (5:1, v/v). The hollow microspheres could float in release medium for more than 24 h, and floating capacities were not be influenced by mixing PVP. In vitro release profiles of hollow microspheres prepared using EC along showed an initial burst release to some extent, and the cumulative release percentage was less than 55% after 24 h. But, not only the slope but also the shape of the release curves was affected by using mixture of PVP and EC. What's more important, when the ratio (PVP/EC) increased to 1.5:8.5, the cumulative release percentage could be increased to 95.8%. Furthermore, the release rate of microspheres showed a zero order approximate dynamic model and could be expressed by the following equation: Q=3.78t+8.52 (r=0.990). Consequently, hollow microspheres prepared using polymer blends of PVP and EC (1.5:8.5, w/w) could be suitable for floating-type controlled-release delivery systems for the oral administration of nifedipine.

Improved nonlinear trajectory tracking using RBFNN for a robotic helicopter

AbstractThis paper presents a backstepping control method using radial‐basis‐function neural network (RBFNN) for improving trajectory tracking performance of a robotic helicopter. Many well‐known nonlinear controllers for robotic helicopters have been constructed based on the approximate dynamic model in which the coupling effect is neglected; their qualitative behavior must be further analyzed to ensure that the unmodeled dynamics do not destroy the stability of the closed‐loop system. In order to improve the controller design process, the proposed controller is developed based on the complete dynamic model of robotic helicopters by using an RBFNN function approximation to the neglected dynamic uncertainties, and then proving that all the trajectory tracking error variables are globally ultimately bounded and converge to a neighborhood of the origin. The merits of the proposal controller are exemplified by four numerical simulations, showing that the proposed controller outperforms a well‐known controller in (J. Robust Nonlinear Control 2004; 14(12):1035–1059). Copyright © 2009 John Wiley & Sons, Ltd.

Boundary Condition Relaxation Method for Stepwise Pedipulation Planning of Biped Robots

A completely stepwise online pedipulation planning method is proposed. It is an analytical approach based on the general solution of the equation of motion of an approximate dynamical biped model whose mass is concentrated at the center of mass. A physically feasible referential trajectory with a constraint about the reaction force taken into account is planned only in one interval by relaxing the boundary condition, namely, by admitting a certain level of error between the desired and actually reached states, and discontinuity of zero-moment point at each end of the interval. It potentially creates responsive motions that require strong instantaneous acceleration. A semiautomatic continual pedipulation planning method is also presented. It generates a referential trajectory of the whole body only from the next desired foot placement. The validity of the proposed method is ensured through experiments with a small anthropomorphic robot.

Dynamic Site Layout Planning Using Approximate Dynamic Programming

Dynamic site layout planning requires identifying and updating the positions of all temporary construction facilities such as offices, storage areas, and workshops over the entire project duration. Existing models do not guarantee global optimal solutions because they focus on optimizing the planning and layout of successive construction stages in a chronological order, without considering the future implications of layout decisions made in early stages. This paper presents the development of an approximate dynamic programming model that is capable of searching for and identifying global optimal dynamic site layout plans. The model applies the concepts of approximate dynamic programming to estimate the future effects of layout decisions in early stages on future decisions in later stages. The model is developed in three main phases: (1) formulating the decision variables, geometric constraints, and objective function of the dynamic site layout planning problem; (2) modeling the problem using approximate dynamic programming; and (3) implementing and evaluating the performance of the model. An evaluation example is analyzed to illustrate the use of the model and demonstrate its capabilities in generating global optimal solution for dynamic site layout planning of construction projects.

Application of Elastic Network Models to Proteins in the Crystalline State

Normal mode analysis using elastic network models has grown popular for probing the low-frequency collective dynamics of proteins and other biomolecular assemblies. In most previous studies, these models were validated by comparing calculated atomic fluctuations for isolated proteins with experimental temperature factors determined in the crystalline state, although there were also hints that including crystal contacts in the calculations has an impact on the comparison. In this study, a set of 83 ultra-high resolution crystal structures with experimentally determined anisotropic displacement parameters is used to evaluate several Cα-based elastic network models that either ignore or treat the crystal environment in different ways; the latter include using periodic boundary conditions defined with respect to the asymmetric unit or the primitive unit cell as well as using the Born-von Kármán boundary condition that accounts for lattice vibrations. For all elastic network models, treating the crystal environment leads to better agreement with experimental anisotropic displacement parameters with the Born-von Kármán boundary condition giving the best agreement. Atomic correlations over the entire protein are clearly affected by the presence of the crystal contacts and fairly sensitive to the way that the crystal environment is treated. These observations highlight the importance of properly treating the protein system in an environment consistent with experiment when either evaluating approximate protein models or using approximate dynamic models in structural refinement application types. Finally, investigation of the scaling behaviors of the cumulative density of states and the heat capacity indicates that there are still gaps between simplified elastic models and all-atom models for proteins.