Average Modeling Approach Research Articles

Sequence-Based Control of an Isolated DC/AC Matrix Converter

This paper describes the control of an isolated multiphase high-frequency-link (HFL) matrix inverter by controlling the time evolution of its switching states (or, switching sequences). It is referred to as the optimal-switching-sequence-based control (OSBC). Unlike several conventional control schemes, where, typically the control is based on averaged model of the inverter and the modulation scheme sets a predetermined switching sequence, OSBC synthesizes the fundamental switching sequence depending on the control needs on the fly. For instance, OSBC can seamlessly play with the inverter switching sequences if the input voltage of the inverter changes as evident in solar or wind-based energy systems. This necessitates that OSBC use a switching-sequence-based discontinuous (instead of averaged) modeling approach. Further, the stability of the closed-loop inverter is determined in OSBC using an advanced composite Lyapunov-function based approach, which also enables one to predetermine the reachability of the inverter dynamics for a given switching sequence. Thus, optimal control in OSBC is ensured under stability bound of the switching sequence. This provides a fundamental difference between OSBC and model predictive control for inverters. This also implies that in a multiobjective OSBC, one can shift the weights of the individual cost functions to yield better inverter performance without compromising the stability.

Average Mathematical Modeling of the Electric Cars Autonomy

The main objective of this work is to develop a highly parameterized model of the electric cars autonomy compatible to optimization algorithms. Control models are not recommended because they require a significant simulation time, which makes their integrations to optimization approaches unprofitable. In addition, these models need the change of the regulators parameters at each iteration. In this context, the present paper describes an average mathematical modelling approach of the electric cars autonomy with reduced simulation time. This approach offers the advantage of a significant reduction in simulation time of the autonomy model in relation to autonomy model based on control models. In conclusion, the average model of the autonomy validated by comparison to a control model is highly compatible with optimization approaches.

Dynamic phasor analysis and design of phase-locked loops for single phase grid connected converters

Purpose – The purpose of the paper is to introduce the dynamic phasor modelling (DPM) approach for stability investigation and control design of single-phase phase-locked loops (PLLs). The aim is to identify the system instabilities not predicted using the existent analysis and design methods based on the simplified average model approach. Design/methodology/approach – This paper starts by investigating the performance of three commonly used PLL schemes: the inverse park-PLL, the second-order generalised integrators (SOGI)-frequency-locked loop and the enhanced-PLL, designed using the simplified average model and will show that following this approach, there is a mismatch between their actual and desired transient performance. A new PLL design method is then proposed based on the DPM approach that allows the development of fourth-order DPM models. The small-signal eigenvalues analysis of the fourth-order DPM models is used to determine the control gains and the stability limits. Findings – The DPM approach is proven to be useful for single-phase PLLs stability analysis and control parameters design. It has been successfully used to design the control parameters and to predict the PLL stability limits, which have been validated via simulation and experimental tests consisting of grid voltage sag, phase jump and frequency step change. Originality/value – This paper has introduced the use of DPM approach for the purpose of single-phase PLL stability analysis and control design. The approach has enabled accurate control gains design and stability limits identification of single-phase PLLs.

A continuous dynamic modeling approach for an omnidirectional mobile robot

MY wheel-II is one of switch omnidirectional wheel mechanisms. The omnidirectional mobile robot based on MY wheels-II is a switched non-linear system (i.e. discontinuous system). The aim of this paper is to propose a continuous modeling approach which can be employed to derive a continuous model from any given discontinuous robot dynamic model. This approach results in a continuous non-linear parameter varying (NLPV) model, and offers one solution for model-based control design. Firstly, our previously proposed average dynamic modeling approach is analyzed. We find that this modeling approach is effective only for a specific class of robot configurations. To overcome this problem, we first derive the switching conditions of MY wheel-II. Based on derived switching conditions, we then propose a simple continuous NLPV modeling approach. The new approach replaces the real discontinuous contact radius in the discontinuous dynamic model with an adaptive continuous curve. An illustrative example of the adaptive continuous curve design is provided. Both simulation and experimental results verify the effectiveness of the proposed NLPV modeling approach against the average modeling approach.

Application of Continuous and Structural ARMA modeling for noise analysis of a BWR coupled core and plant instability event

This paper presents a first application of a novel Continuous and Structural Autoregressive Moving Average (CSARMA) modeling approach to BWR noise analysis. The CSARMA approach derives a unique representation of the system dynamics by more robust and reliable canonical models as basis for signal analysis in general and for reactor diagnostics in particular. In this paper, a stability event that occurred in a Swiss BWR plant during power ascension phase is analyzed as well as the time periods that preceded and followed the event. Focusing only on qualitative trends at this stage, the obtained results clearly indicate a different dynamical state during the unstable event compared to the two other stable periods. Also, they could be interpreted as pointing out a disturbance in the pressure control system as primary cause for the event. To benchmark these findings, the frequency-domain based signal transmission-path (STP) method is also applied. And with the STP method, we obtained similar relationships as mentioned above. This consistency between both methods can be considered as being a confirmation that the event was caused by a pressure control system disturbance and not induced by the core. Also, it is worth noting that the STP analysis failed to catch the relations among the processes during the stable periods, that were clearly indicated by the CSARMA method, since the last uses more precise models as basis.

Modeling an Average Monthly Temperature of Sokoto Metropolis Using Short Term Memory Models

In this paper, the results of seasonal modeling of Sokoto monthly average temperature have been obtained using seasonal autoregressive integrated moving average modeling approach. Based on this seasonal modeling analysis, we conclude that , the best seasonal model among the models that are adequate to describe the seasonal dynamics for Sokoto city temperature is SARIMA (3,0,1)(4,1,0) 12, SARIMA (1,0,0)(0,1,1) 12 and SARIMA (4,0,2)(5,1,1) 12 models. These models are the only models that passed all the diagnostic tests and thus it can be used for forecasting at some future time.

Using multi-level data to estimate the effect of an ‘alcogenic’ environment on hazardous alcohol consumption in the former Soviet Union

PurposeThe purpose of this study was to assess whether alcohol-related community characteristics act collectively to influence individual-level alcohol consumption in the former Soviet Union (fSU). Methods and resultsUsing multi-level data from nine countries in the fSU we conducted a factor analysis of seven alcohol-related community characteristics. The association between any latent factors underlying these characteristics and two measures of hazardous alcohol consumption was then analysed using a population average regression modelling approach. Our factor analysis produced one factor with an eigenvalue >1 (EV=1.28), which explained 94% of the variance. This factor was statistically significantly associated with increased odds of CAGE problem drinking (OR=1.40 (1.08–1.82)). The estimated association with EHD was not statistically significant (OR=1.10 (0.85–1.44)). ConclusionsOur findings suggest that a high number of beer, wine and spirit advertisements and high alcohol outlet density may work together to create an ‘alcogenic’ environment that encourages hazardous alcohol consumption in the fSU.

Analysis and Modeling of Parallel Three-Phase Boost Converters Using Three-Phase Coupled Inductor

The main issue of parallel three-phase boost converters is reduction of the low- and high frequency circulating currents. Most present technologies concentrate on low frequency circulating current because the circulating current controller cannot mitigate the high frequency circulating current. In this paper, analytical approach of three-phase coupled inductor applied to parallel system becomes an important objective to effectively reduce the low- and high frequency circulating currents. The characteristics of three-phase coupled inductor based on a structure and voltage equations are mathematically derived. The modified voltage equations are then applied to parallel three-phase boost converters to develop averaged models in stationary coordinates and rotating coordinates. Based on the averaged modeling approach, design of the circulating current controller is presented. Simulation and experimental results demonstrate the effectiveness of the analysis and modeling for the parallel threephase boost converters using three-phase coupled inductor.

Control Loop Design for a PFC Boost Converter With Ripple Steering

In this paper, an average switch model approach to the power stage modeling, feedback compensation network design, and dynamic analysis of power factor correction (PFC) boost converters with ripple steering is presented. The model is expressed by the derivation of power stage transfer functions for a conventional boost converter and then followed by the derivation of the power stage transfer functions for a boost converter with ripple steering. A detailed design procedure and comparison of both voltage and current feedback loops are given in a conventional PFC boost converter and boost converter with ripple steering. Experimental and simulation results of a prototype boost converter converting universal ac input voltage to 400 V dc at 1.6 kW are given to verify the proof of concept and analytical work reported. The experimental results demonstrate that the model can correctly predict the steady-state behavior of a continuous conduction mode PFC boost converter with ripple steering.

Three-Phase HFL-DVR With Independently Controlled Phases

Conventional dynamic voltage restorers (DVRs) are connected to the power grid through power-frequency transformers. These bulky and costly transformers cause voltage drop and power losses. In this paper, a high-frequency-link dynamic voltage restorer (HFL-DVR) is proposed based on transformer-isolated topologies. This topology facilitates independent operation conditions for each phase in a three-phase system. It enjoys relatively low cost, low losses, and small size. Also, it is free from transformer inrush currents. Small-signal ac equivalent circuit for the power stage including HFL-DVR is derived based on an averaged modeling approach. Transfer functions are obtained to study the effect of inputs such as dc-link voltage, grid voltage, and the load current on the output of HFL-DVR. In order to obtain acceptable properties such as transient overshoot, setting time, and steady-state error, a PID controller is added to the system. This shows that the effect of disturbances on the output of HFL-DVR can be reduced. The experimental results are obtained from a 220V/50Hz HFL-DVR setup. The simulation and experimental results have been compared to verify theoretical aspect of the proposed DVR for both symmetrical and asymmetrical voltage sag conditions.

Modelling and forecasting Oman crude oil prices using Box-Jenkins techniques

The Box-Jenkins’ Auto Regressive Integrated Moving Average (ARIMA) modelling approach has been applied for the time series analysis of monthly average prices of Oman crude oil taken over a period of 10 years. Several seasonal and non-seasonal ARIMA models were identified. These models were then estimated and compared for their adequacy using the significance of the parameter estimates, mean square errors and Modified Box-Pierce (Ljung-Box) Chi-Square statistic. Based on these criterion a multiplicative seasonal model of the form ARIMA (1,1,5) × (1,1,1) was recommended for short term forecasting.

Comparison of the Performance of Statistical Models in Forecasting Monthly Total Dissolved Solids in the Rio Grande1

Abudu, S., J.P. King, Z. Sheng, 2011. Comparison of the Performance of Statistical Models in Forecasting Monthly Total Dissolved Solids in the Rio Grande. Journal of the American Water Resources Association (JAWRA) 48(1): 10‐23. DOI: 10.1111/j.1752‐1688.2011.00587.xAbstract: This paper presents the application of autoregressive integrated moving average (ARIMA), transfer function‐noise (TFN), and artificial neural networks (ANNs) modeling approaches in forecasting monthly total dissolved solids (TDS) of water in the Rio Grande at El Paso, Texas. Predictability analysis was performed between the precipitation, temperature, streamflow rates at the site, releases from upstream reservoirs, and monthly TDS using cross‐correlation statistical tests. The chi‐square test results indicated that the average monthly temperature and precipitation did not show significant predictability on monthly TDS series. The performances of one‐ to three‐month‐ahead model forecasts for the testing period of 1984‐1994 showed that the TFN model that incorporated the streamflow rates at the site and Caballo Reservoir release improved monthly TDS forecasts slightly better than the ARIMA models. Except for one‐month‐ahead forecasts, the ANN models using the streamflow rates at the site as inputs resulted in no significant improvements over the TFN models at two‐month‐ahead and three‐month‐ahead forecasts. For three‐month‐ahead forecasts, the simple ARIMA showed similar performance compared to all other models. The results of this study suggested that simple deseasonalized ARIMA models could be used in one‐ to three‐month‐ahead TDS forecasting at the study site with a simple, explicit model structure and similar model performance as the TFN and ANN models for better water management in the Basin.

A state-based probabilistic model for tumor respiratory motion prediction

This work proposes a new probabilistic mathematical model for predicting tumor motion and position based on a finite state representation using the natural breathing states of exhale, inhale and end of exhale. Tumor motion was broken down into linear breathing states and sequences of states. Breathing state sequences and the observables representing those sequences were analyzed using a hidden Markov model (HMM) to predict the future sequences and new observables. Velocities and other parameters were clustered using a k-means clustering algorithm to associate each state with a set of observables such that a prediction of state also enables a prediction of tumor velocity. A time average model with predictions based on average past state lengths was also computed. State sequences which are known a priori to fit the data were fed into the HMM algorithm to set a theoretical limit of the predictive power of the model. The effectiveness of the presented probabilistic model has been evaluated for gated radiation therapy based on previously tracked tumor motion in four lung cancer patients. Positional prediction accuracy is compared with actual position in terms of the overall RMS errors. Various system delays, ranging from 33 to 1000 ms, were tested. Previous studies have shown duty cycles for latencies of 33 and 200 ms at around 90% and 80%, respectively, for linear, no prediction, Kalman filter and ANN methods as averaged over multiple patients. At 1000 ms, the previously reported duty cycles range from approximately 62% (ANN) down to 34% (no prediction). Average duty cycle for the HMM method was found to be 100% and 91 ± 3% for 33 and 200 ms latency and around 40% for 1000 ms latency in three out of four breathing motion traces. RMS errors were found to be lower than linear and no prediction methods at latencies of 1000 ms. The results show that for system latencies longer than 400 ms, the time average HMM prediction outperforms linear, no prediction, and the more general HMM-type predictive models. RMS errors for the time average model approach the theoretical limit of the HMM, and predicted state sequences are well correlated with sequences known to fit the data.

SU‐GG‐J‐87: State Based Motion Tracking and Prediction Using a Hidden Markov Model

Purpose: To evaluate the effectiveness of a probabilistic mathematical model at tracking and predicting respiratory motion gated and motion compensated in radiation therapy. Method and Materials: A previously recorded set of fiducial marker positions were recorded at 30Hz for four patients and modeled into finite states representing the natural breathing states of Exhale, Inhale, and End of Exhale. State sequences and observables representing those sequences were analyzed using a hidden Markov model (HMM) to predict a future set of sequences and new observables. Velocities and other parameters were clustered using a k‐means clustering algorithm to associate each state with a set of observables such that a prediction of state would enable a prediction of velocity. Positional results were then compared to several other published predictive models in terms of their overall RMS errors from actual position. System delay times from 33 to 1000 ms were considered and for additional comparison, a simple time average model which bases future sequence prediction on average past state lengths was also computed. State sequences which are known a priori to fit the data were fed into the hidden Markov model prediction algorithm to set a theoretical limit of the predictive power of the model at ∼20% of the motion amplitude. Results: RMS errors for motion traces modeled by the hidden Markov model show improvement over linear and phase shift type prediction models at latencies higher than 500 ms, but do not reach the target lower limit. RMS errors for the time average model approach the theoretical limit of the HMM, even though their state sequences are poorly correlated with sequences known to fit the data. Conclusion: Hidden Markov models and their simpler probabilistic counterparts show much promise for use in online prediction of tumor motion for improved accuracy of radiation delivery.

Vehicle-Specific Emissions Modeling Based upon on-Road Measurements

Vehicle-specific microscale fuel use and emissions rate models are developed based upon real-world hot-stabilized tailpipe measurements made using a portable emissions measurement system. Consecutive averaging periods of one to three multiples of the response time are used to compare two semiempirical physically based modeling schemes. One scheme is based on internally observable variables (IOVs), such as engine speed and manifold absolute pressure, while the other is based on externally observable variables (EOVs), such as speed, acceleration, and road grade. For NO, HC, and CO emission rates, the average R(2) ranged from 0.41 to 0.66 for the former and from 0.17 to 0.30 for the latter. The EOV models have R(2) for CO(2) of 0.43 to 0.79 versus 0.99 for the IOV models. The models are sensitive to episodic events in driving cycles such as high acceleration. Intervehicle and fleet average modeling approaches are compared; the former account for microscale variations that might be useful for some types of assessments. EOV-based models have practical value for traffic management or simulation applications since IOVs usually are not available or not used for emission estimation.

Identification of temporal trends in annual and seasonal low flows occurring in Canadian rivers: The effect of short- and long-term persistence

In order to investigate trends in time series of hydrological variables, observational records are generally assumed to be independent (IND) or having short-term persistence (STP), as opposed to long-term persistence (LTP). This study systematically investigates the effect of these assumptions for estimating significance of trends using simulated time series of known STP- and LTP-like serial structures and using observational records of annual, winter and summer 30-day low flows occurring in pristine rivers basins of Canada. For the analyses, the Mann–Kendall (MK) test and two modified versions of this test and a block bootstrap resampling based test is used to estimate significance of temporal trends under the assumptions of IND and STP. A semiparametric and a parametric procedure based on the fractional autoregressive integrated moving average modeling approach and a MK scaling test are used to estimate significance of temporal trends under the assumption of LTP. The results of the study suggest that for the majority of the time series of low flows analyzed, the assumption of IND or STP cannot be ruled out. On the contrary, the fluctuating behavior of trends revealed in selected time series of low flows, with longer records, using moving window technique favor the LTP hypothesis. However, only a modest evidence for the presence of LTP in low flow time series using estimates of the Hurst exponent could be revealed, perhaps because of small sample uncertainties. In general, the results of trend investigation suggest that the estimates of trend significance are highly sensitive to IND, STP and LTP assumptions, e.g. adopting IND assumption instead of LTP for a given set of hydrological time series exhibiting LTP, could result in incorrect estimation of trend significance. Also, substituting IND assumption for STP would result in incorrect estimates of trend significance. Therefore, for reliable trend investigation, satisfactory identification of STP- or LTP-like behavior in hydrological time series, which seldom exceed 100 years, is important and challenging and must be given adequate attention in all trend investigation studies.

PIECEWISE AFFINE SYSTEM MODELING AND CONTROL OF PWM CONVERTERS

The averaged switch modeling approach is a powerful method for representing the behavior of a wide variety of converters through equivalent circuits. The model is not linear and it is common to perform a small signal linearization about an operating point and design a linear controller. Models obtained with such method involve considerable approximation and produce results that are limited for high performance controller designs. In this paper a piecewise affine approximation technique is introduced for modeling PWM converters. This model is much more precise in predicting the dynamic response of averaged nonlinear model comparing the linear model. This paper also presents a piecewise linear controller synthesis method for PWM converters described by the proposed PWA model. The proposed controller is very efficient and effective. The design method is well suited for converters having a wide range of variation about their operating point. A simulation example on buck-boost converter is presented to demonstrate the performance of the proposed method for modeling the dynamics of the converter and designing the appropriate controller.

A Simulation Study of Running-in Wear Effect and Its Influence on Average Flow Factors

The profile of a new rough surface continuously varies under a running-in condition. A small change in the distribution of the asperity heights can have a significant effect on the hydrodynamic lubrication performance as the film thickness varies. For a realistic understanding of the thin-film lubrication phenomenon, it becomes imperative to consider the effect of “running-in wear.” To consider its effect on the hydrodynamic lubrication performance of tribological elements using an average flow model approach, a theoretical simulative study of running-in wear and its effect on pressure and shear flow factors has been presented that is based on numerical flow simulation of isotropic flattened rough surfaces. An average Reynolds equation for flattened rough surfaces is expressed in terms of pressure and shear flow factors. The flow factors have been computed by solving the conventional Reynolds equation using FEM on a model bearing of rough surfaces for various stages of running-in wear. The computation is performed by numerically generating the isotropic flattened rough surfaces for the different value of η, a normalized factor that is a measure of magnitude of running-in wear. The pressure and shear flow factors are expressed as a function of dimensionless parameters h/σ and η. The study shows that the value of pressure flow factors increases whereas that of shear flow factors decreases with the increase in the magnitude of η. The governing equations for flow factors and the curves presented in the paper are useful as these are required for the solution of an average Reynolds equation for studying the effect of running-in wear in lubrication problems using an average flow model approach.

New approach to information fusion steady-state Kalman filtering

By the modern time series analysis method, based on the autoregressive moving average (ARMA) innovation model, a unified and general information fusion steady-state Kalman filtering approach is presented for the general multisensor systems with different local dynamic models and correlated noises. It can handle the filtering, smoothing, and prediction fusion problems for state or signal. The optimal fusion rule weighted by matrices is re-derived as a weighted least squares (WLS) fuser, and is reviewed. An optimal fusion rule weighted by diagonal matrices is presented, which is equivalent to the optimal fusion rule weighted by scalars for components, and it realizes a decoupled fusion. The new algorithms of the steady-state Kalman estimator gains are presented. In order to compute the optimal weights, the formulas of computing the cross-covariances among local estimation errors by Lyapunov equations are presented. The exponential convergence of the iterative solution of Lyapunov equation is proved. It is proved that the optimal fusion estimators under three weighted fusion rules are locally optimal, but are globally suboptimal. The proposed steady-state Kalman fusers can reduce the on-line computational burden, and are suitable for real-time applications. A simulation example for the 3-sensor steady-state Kalman tracking fusion estimators shows their effectiveness and correctness, and gives the accuracy comparison of the fusion rules.

Segmentation of monochrome and color textures using moving average modeling approach

The segmentation of textures using features extracted with a 2-D moving average (MA) modeling approach is presented. The 2-D MA model represents a texture as an output of a 2-D finite impulse response (FIR) filter with simple input process. The 2-D MA model is flexible, and can be used for modeling both isotropic and anisotropic textures. The maximum likelihood (ML) estimators of the 2-D MA model are used as texture features. Supervised and unsupervised texture segmentation is considered. A neural network is used for supervised segmentation, and a fuzzy clustering algorithm is used for unsupervised segmentation. The texture features extracted by the 2-D MA modeling approach from sliding windows are classified with a neural network for supervised segmentation, and are clustered by a fuzzy clustering algorithm for unsupervised texture segmentation. The performance of the segmentation algorithms using MA model features are demonstrated in the experiment with both synthetic and natural images.