State-space Approach Research Articles

Heuristic design and modelling of modified interleaved boost converter for E-mobility control

PurposeThis study aims to present a modified interleaved boost converter (MIBC) topology for improving the reliability and efficiency of power electronic systems.Design/methodology/approachThe MIBC topology was implemented with two parallel converters, operated with a −180 degree phase shift. Using this methodology, ripples are reduced. The state-space model was analysed with a two-switch MIBC for different modes of operation. The simulation was carried out and validated using a hardware prototype.FindingsThe performance of the proposed MIBC shows better output voltage, current and power than the interleaved boost converter (IBC) for the solar PV array. The output power of the proposed converter is 1.353 times higher than that of existing converters, such as boost converter (BC) and IBC. The output power of the four-phase IBC is 30 kW, whereas that of the proposed two-phase MIBC is 40.59 kW. The efficiency of MIBC was better than that of IBC (87.01%). By incorporating interleaved techniques, the total inductor current is reduced by 29.60% compared with the existing converter.Practical implicationsThe proposed MIBC can be used in a grid-connected system with an inverter circuit for DC-to-AC conversion, electric vehicle speed control, power factor correction circuit, high-efficiency converters and battery chargers.Originality/valueThe work presented in this paper is a modified version of IBC. This modified MIBC was modelled using the state-space approach. Furthermore, the state-space model of a two-phase MIBC was implemented using a Simulink model, and the same was validated using a hardware setup.

Z-Reliable SE-Coprognosability of Discrete Event Systems and an Algebraic State Space Approach to Verification

This brief investigates the problem of fault prognosis in decentralized structure, where given systems are monitored by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula> local agents, and assumes that only <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$z$ </tex-math></inline-formula> ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1\leq z\leq r$ </tex-math></inline-formula> ) local agents’ information can be obtained. In this case, under a state-estimate-based protocol, a notion called <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$z$ </tex-math></inline-formula> -reliable SE-coprognosability is proposed. Compared with existing results, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$z$ </tex-math></inline-formula> -reliable SE-coprognosability is a more universal concept. Besides, with the help of semi-tensor product, an efficient algorithm for state estimation is presented, and based on which, the algorithm on verification of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$z$ </tex-math></inline-formula> -reliable SE-coprognosablity is also proposed. A dominating breakthrough is that the complexity of given algorithms is polynomial with respect to the number of agents.

Control analysis and synthesis of data-driven learning for uncertain linear systems

This paper aims to deal with the control analysis and synthesis problem of data-driven learning, regardless of unknown plant models and iteration-varying uncertainties. For the tracking of any desired target, a Kalman state–space approach is presented to transform it into two robust stability problems, which bridges a connection between data-driven control and model-based control. The proposed approach makes it possible to employ the extended state observer (ESO) in the design of data-driven learning to overcome the effect of iteration-varying uncertainties. It is shown that ESO-based data-driven learning ensures model-free systems to achieve the robust tracking of any desired target, and particularly is applicable for realizing the accurate tracking objective subject to the iteration-varying uncertainties with quasi-disappearing variations. Further, our developed results apply to iterative learning control, which is also verified by an example.

Bandgaps in functionally graded phononic crystals containing graphene origami-enabled metamaterials

This paper investigates the dispersion characteristics of elastic waves propagating along the thickness direction in functionally graded laminated phononic crystals (FGLPCs) containing novel auxetic metamaterials enabled by graphene origami that is created with the aid of hydrogenation. Both graphene weight fraction and hydrogen coverage which are the key parameters governing the auxetic property are nonuniformly distributed in unit cells of FGLPCs whose material properties are determined by genetic programming-assisted micromechanical models. The dispersion relations of elastic waves in the structure are obtained based on the state space approach and the method of reverberation-ray matrix. A comprehensive parametric study is conducted to discuss the effects of graphene origami weight fraction and hydrogen coverage on bulk waves in elastic solids made of the metamaterial and elastic waves in FGLPCs. It is found that introducing auxetic metamaterials into FGLPCs can effectively manipulate elastic waves. The graded distribution of weight fraction in FGLPCs can lead to bandgaps for both transverse and longitudinal waves, while a through-thickness graded pattern in hydrogen coverage can trigger broad bandgaps for longitudinal waves only with transverse waves nearly unchanged.

Forest stand-by-environment interaction invalidates the use of space-for-time substitution for site index modeling under climate change

Quantification and prediction of forest site productivity potential under climate changes are core issues of growth and yield modelling and crucial for the effective management of forest stands. Climatic factors typically enter these models as long-term baseline data of mean air temperature and sum of precipitation. Such models frequently are parameterized using spatially distributed data, an approach that can be considered as indirect space-for-time substitution (SFTS), which is based on the assumption that the growth response across spatial environmental gradients is equivalent to the dynamic growth response in time when site factors shift in situ due to environmental changes over time. In site index modelling this assumption has never been thoroughly tested. We applied procedures to test the validity of the SFTS approach using altitudinal transect analysis of data from long-term experimental plots with Norway spruce (Picea abies) in southwest Germany. The following procedures were applied 1. Partitioning of spatial and temporal variance components of site index and air temperature; 2. Test of the equivalence of spatial and temporal variation in models predicting site index from air temperature; 3. Bias analysis of the SFTS- and the altitudinal range-specific individual models. The results show that the response of site index to changes in growing season air temperature as estimated by the SFTS-based approach is significantly different from the observed temporal dynamics within the plots. The estimated sensitivity of site index to changes in growing season air temperature is significantly larger in the SFTS-model. Bias trends increase with increasing air temperature in all altitudinal ranges. We found significant effects of stand-by-environment interaction on site index responses to changing growing season air temperature. Possible reasons for the found differences are discussed. Furthermore, based on an independent data set from the German National Forestry Inventory (NFI), a SFTS-based climate-sensitive site index model was compared to a state-space modelling approach (SSM) which considers the dynamics of environmental changes over time. The results show that the SSM is statistically superior to the SFTS approach, corroborating the findings of the altitudinal transect analysis. Finally, future site index dynamics of Norway spruce were simulated on the basis of climate change scenarios using the SSM approach. We recommend for all climate sensitive forest growth and yield models based on spatially and temporally distributed data to properly account for within- and between-group effects to avoid erroneous model predictions under climate change.

A simple Bayesian state-space approach to the collective risk models

The collective risk model (CRM) for frequency and severity is an important tool for retail insurance ratemaking, natural disaster forecasting, as well as operational risk in banking regulation. This model, initially designed for cross-sectional data, has recently been adapted to a longitudinal context for both a priori and a posteriori ratemaking, through random effects specifications. However, the random effects are usually assumed to be static due to computational concerns, leading to predictive premiums that omit the seniority of the claims. In this paper, we propose a new CRM model with bivariate dynamic random effects processes. The model is based on Bayesian state-space models. It is associated with a simple predictive mean and closed form expression for the likelihood function, while also allowing for the dependence between the frequency and severity components. A real data application for auto insurance is proposed to show the performance of our method.

Developing a Regime-Switching Present Value Model: Switching Fundamentals and Bubbles

We develop a present-value model where the fundamental and non-fundamental components switch between distinct regimes. The non-fundamental component is specified as a periodically collapsing bubble of Balke and Wohar [Balke, N. S., & Wohar, M. E. (2009). Market fundamentals versus rational bubbles in stock prices: A Bayesian perspective. Journal of Applied Econometrics, 24(1), 35–75. https://doi.org/10.1002/jae.1025]. The fundamental component is constructed as in van Binsbergen and Koijen [van Binsbergen, J. H., & Koijen, R. S. J. (2010). Predictive regressions: A present-value approach. The Journal of Finance, 65(4), 1439–1471. https://doi.org/10.1111/j.1540-6261.2010.01575.x], by treating the expectations of market fundamentals as latent variables. Unlike existing methods, e.g. [Zhu, X. (2015). Tug-of-war: Time-varying predictability of stock returns and dividend growth. Review of Finance, 19(6), 2317–2358. https://doi.org/10.1093/rof/rfu047; Choi, K. H., Kim, C., & Park, C. (2017). Regime shifts in price-dividend ratios and expected stock returns: A present-value approach. Journal of Money, Credit and Banking, 49(2–3), 417–441. https://doi.org/10.1111/jmcb.12384; Chan, J. C., & Santi, C. (2021). Speculative bubbles in present-value models: A Bayesian Markov-switching state space approach. Journal of Economic Dynamics and Control, 127, 1–26], ours requires no unnecessary approximations, accommodates flexible forms of regime-switching, and the resulting present-value formula is internally consistent.

Single-Month Unemployment Rate Estimates for the Brazilian Labour Force Survey Using State-Space Models

AbstractThe Brazilian Labour Force Survey publishes monthly national indicators based on 3-month rolling data. This paper presents state-space models to produce state-level single-month unemployment rate estimates. The models account for sampling errors and the increased dynamics in the labour force series due to the unforeseen SARS-COV-2 pandemic. Bivariate time series models with claimant count auxiliary data and multivariate models combining survey data of several states are investigated. The results demonstrated the benefits of the univariate state-space approach to produce unemployment official statistics for Brazil. Additionally, the regional multivariate model shows promising results but requires further investigation.

DIAGNOSTICS OF AN ELECTRIC MOTOR BASED ON A STATE-SPACE MODEL

This paper develops a model for DC motor diagnostics based on a state-space model approach. Its digital twin works in parallel with the real motor. The output signals of the motor and the digital twin from the electric current and angular velocity sensors are analyzed. The motor defects are detected by the magnitude of the discrepancy. If a defect is detected, the diagnostic system transmits information about it to the control system, turns on the defect indication, then by fuzzy logic methods or other methods determines the type of defect, the system element in which the defect occurred, predicts the remaining life, the control system is fail-safe operation. If the magnitude of the defect is insignificant, then appropriate maintenance actions are recommended. If the magnitude of the defect is significant, an emergency shutdown of the motor must be performed.

V-Matrix: A wave theory of value creation for big data

This paper examines the “V-Matrix” and provides a wave theory life cycle model of organizations’ adoption of big data. The V-Matrix is based on the big data five “V’s”: Volume, Velocity, Variety, Veracity, and Value and captures and enumerates the different potential states that an organization can go through as part of its adoption and evolution towards big data. We extend the V-Matrix to a state space approach in order to provide a characterization of the adoption of big data technologies in an organization. We develop and use a wave theory of implementation to accommodate a firm’s movement through the V-Matrix. Accordingly, the V-Matrix provides a life cycle model of organizational use of the different aspects of big data. In addition, the model can help organizations’ plan for decision-making use of big data as they anticipate movement from one state to another, as they add big data capabilities. As part of this analysis, the paper examines some of the issues that occur in the different states, including synergies and other issues associated with co-occurrence of different V’s with each other. Finally, this paper integrates the V-Matrix with other data analytic life cycles and examines some of the implications of those models.

Time-domain Analytical Method for Aeroelastic Analysis of High Aspect Ratio Wing Using Unsteady Indicial Aerodynamics

This paper deals with the development of a novel time-domain analytical method to perform an aeroelastic stability analysis of a high aspect ratio wing. The aerodynamic model in this paper is built upon the frequency domain of Theodorsen aerodynamics. An inverse Fourier transform technique is applied to convert the Theodorsen frequency domain transfer function to the Wagner time-domain response function. Later, lift and moment expressions, containing aerodynamic lag states are obtained using the indicial function approach which performs the convolution of the unit step response Wagner lift function with arbitrary input and its time derivative using the principle of superposition. Additional equations containing the derivative of aerodynamic lag states are coupled with previously obtained lift and moment expressions to fully get the complete unsteady aerodynamic model in the time-domain. This time-domain unsteady aerodynamic model is then coupled with a combined bending torsion beam finite element model, using the state space approach. A complex eigenvalue analysis of this state space system is performed, using MATLAB, to determine both the static and dynamic aeroelastic stability boundary. Finally, in this paper, aeroelastic stability analysis of the high aspect ratio wing is performed using the current method and results are validated with MSC Nastran aeroelastic analysis and those available in the literature.

Free and forced vibration analysis of general multiple beam systems

As typical structural elements, multiple beam systems exist widely in many practical engineering applications. Existing research usually makes some simplifications in the systems and cannot analyze the actual structures very precisely. This paper focuses on studying both free and forced transverse vibrations of general multiple beam systems, which are a set of parallel Euler-Bernoulli beams joined by viscoelastic connections. In particular, the to-be-solved models are very generalized and it enables one to simultaneously consider (1) arbitrary number of beams, (2) any boundary conditions, (3) different beam lengths, (4) variable cross sections, (5) axial loads, and (6) various types of viscoelastic connections. Single uniform beams are introduced to modify the original governing equations to a set of inhomogeneous differential equations. Defining some mode-shape coefficients and state variables, a state-space approach is proposed to transfer these differential equations into a state-space equation. Natural frequencies and forced vibration responses are obtained through the state-space equation. With the obtained natural frequencies and a modal Rayleigh-Ritz method, the differential equations about mode shapes are transformed into several algebraic equations that are easily solved. The accuracy of the proposed methods is validated by numerical examples from previous literature and the finite element method. Finally, the requirements for the mode number in the developed methods are presented. This study is of great significance in analyzing and designing multiple beam systems in engineering practices.

Performance Analysis of Passive Resonance Circuit Breakers in HVDC Systems

High voltage direct current (HVDC) systems have been increasingly employed to provide a connection amongst various energy sources, especially renewable energies. In order to switch the load current in these systems, the passive resonance breaker (PRB) is employed as a transfer switch. The interruption capability of the PRBs is highly dependent on the dynamic behavior of the arc in this system. Accordingly, this article presents a black-box high-degree of freedom arc-model based on Schwarz and the genetic algorithm as a heuristic optimization to follow the characteristics of the static and quasi-static arc regarding the oscillation frequency of the interruption current. The model has been verified by the published experiments. Subsequently, the PRB operation has been quantified based on the state-space approach along with an accurate dynamic arc model to follow in a wide frequency range of the arc current. The results are indicated that the amplification coefficient alone is insufficient to determine the interruption capability of these protection tools. Therefore, this article attempts to quantitatively determine the interruption capability curve by introducing criteria, such as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Δt_PZ</i> (peak to zero time-interval), <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">di</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">arc</sub> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">/dt_CZ^-</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">du</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">arc</sub> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">/dt_CZ⁺</i> to obtain the origins of the interruption failure probabilities and dimensioning of the PRBs with respect to the interruption limits for gas blast CBs.

EXTENDING THE LEE–CARTER MODEL WITH VARIATIONAL AUTOENCODER: A FUSION OF NEURAL NETWORK AND BAYESIAN APPROACH

AbstractIn this study, we propose a nonlinear Bayesian extension of the Lee–Carter (LC) model using a single-stage procedure with a dimensionality reduction neural network (NN). LC is originally estimated using a two-stage procedure: dimensionality reduction of data by singular value decomposition followed by a time series model fitting. To address the limitations of LC, which are attributed to the two-stage estimation and insufficient model fitness to data, single-stage procedures using the Bayesian state-space (BSS) approaches and extensions of flexibility in modeling by NNs have been proposed. As a fusion of these two approaches, we propose a NN extension of LC with a variational autoencoder that performs the variational Bayesian estimation of a state-space model and dimensionality reduction by autoencoding. Despite being a NN model that performs single-stage estimation of parameters, our model has excellent interpretability and the ability to forecast with confidence intervals, as with the BSS models, without using Markov chain Monte Carlo methods.

Dynamic Species Distribution Modeling Reveals the Pivotal Role of Human-Mediated Long-Distance Dispersal in Plant Invasion

Simple SummaryUnderstanding biological invasion mechanisms is crucial to design effective management strategies preventing their impacts on ecosystems. If the role of long-distance dispersal and age-dependent fecundity in plant invasion speed has been characterized in theory, empirical support is still rare given the difficulty to jointly fit demographic and spread parameters of dynamic models to available data. We proposed a statistical model to fit such parameters to heterogeneous observations collected across space and time. We can directly test hypotheses on the importance of various mechanisms in a past invasion. We demonstrated the potential of this method by determining the roles of human-mediated long-distance dispersal and age-dependent fecundity in the invasion of the shrub Plectranthus barbatus in South Africa. Our model revealed a massive wave of spread driven by human-mediated long-distance dispersal and originating from the cities of first introduction. The species completed its invasion of all favorable areas a few years after, in the mid-1990s. Without human-mediated long-distance dispersal, the maximum population would have been obly 30% of the current population. The delayed reproductive maturity explained the invasion lag phase. It highlights the importance of early eradication of weedy horticultural alien plants around urban areas to hamper the invasive spread.Plant invasions generate massive ecological and economic costs worldwide. Predicting their spatial dynamics is crucial to the design of effective management strategies and the prevention of invasions. Earlier studies highlighted the crucial role of long-distance dispersal in explaining the speed of many invasions. In addition, invasion speed depends highly on the duration of its lag phase, which may depend on the scaling of fecundity with age, especially for woody plants, even though empirical proof is still rare. Bayesian dynamic species distribution models enable the fitting of process-based models to partial and heterogeneous observations using a state-space modeling approach, thus offering a tool to test such hypotheses on past invasions over large spatial scales. We use such a model to explore the roles of long-distance dispersal and age-structured fecundity in the transient invasion dynamics of Plectranthus barbatus, a woody plant invader in South Africa. Our lattice-based model accounts for both short and human-mediated long-distance dispersal, as well as age-structured fecundity. We fitted our model on opportunistic occurrences, accounting for the spatio-temporal variations of the sampling effort and the variable detection rates across datasets. The Bayesian framework enables us to integrate a priori knowledge on demographic parameters and control identifiability issues. The model revealed a massive wave of spatial spread driven by human-mediated long-distance dispersal during the first decade and a subsequent drastic population growth, leading to a global equilibrium in the mid-1990s. Without long-distance dispersal, the maximum population would have been equivalent to 30% of the current equilibrium population. We further identified the reproductive maturity at three years old, which contributed to the lag phase before the final wave of population growth. Our results highlighted the importance of the early eradication of weedy horticultural alien plants around urban areas to hamper and delay the invasive spread.

Design and implementation of high-order PID for second-order processes with time delay

In this study, a state-space pole placement approach is first proposed to design high-order PID controllers for high-order processes. The method makes use of a single parameter to determine the locations of closed-loop poles; thus, a high-order PID controller can be tuned with this parameter. To implement the high-order PID controller in practice, an observer-based PID structure is proposed. The structure utilizes a model-free observer to estimate the plant output and its derivatives, thus retaining the high-order PID structure but can filter the measurement noise and make the high-order derivatives of the plant output available for control. The proposed method is applied to design high-order PID controllers for second-order processes with time delay. Simulation results show that high-order PID can indeed improve the performance of conventional PID controllers for second-order processes with time delay in disturbance rejection and robustness.

A state space modeling for proactive management in equity investment

This paper proposes a novel state-space approach to explain stock market dynamics driven by different types of trading, which leads to a new promising scheme for proactive risk management in financial investment. Particularly, it is assumed that the current price changes are formulated through daily trading by multiple types of traders, each of whom follows a specific investment strategy based on technical indicators and a fuzzy logic using past data of stock prices, volumes and yield curves. Moreover, the current price changes are represented by a linear combination of those multiple trading types, where the coefficients corresponding with the size of impact on the price changes are regarded as time-varying state variables to be sequentially estimated under a state-space framework. Thereby, this work develops a new factor decomposition method on price changes from a perspective of different traders’ demand and supply to analyze the current situations and potential risks in financial markets. In empirical experiments, it is shown that the implementation of particle filtering algorithm makes it possible to replicate market price changes. Further, new signals based on the estimated states are developed, which are applied to proactive risk management in financial investment. Especially, it has been found that the demands of yield curve-based traders subtracting those of trend-followers could be a promising signal of stock market crashes, which has successfully enhanced simple buy-and-hold strategy of SP, as well as constant proportion strategies.

Stability and Resilience—A Systematic Approach

Stability and resilience are two crucial concepts to the proper functioning and understanding of the behavior of both natural and man-made systems exposed to perturbations and change. However, although the two have covered a similar territory within dynamic systems, the terminology and applications differ significantly. This paper presents a critical analysis of the two concepts by first collating the wealth of modern stability concept literature within dynamics systems and then linking it to resilience thinking, defined as adaptation where the system has the ability to respond perturbations and change through passive and active feedback structures. A lumped mass and simple pendulum, two simple linear and nonlinear dynamic systems following a state-space approach from modern control systems theory, are used to support the analysis and application. The research findings reveal that the two overarching categories of engineering resilience and socio-ecological resilience (extended ecological resilience) are in fact a reinvention of a closed-loop system dynamic stability with different types of active feedback mechanisms. Additionally, structural stability describes some vital aspects of social–ecological resilience such as critical thresholds where, under change, a system loses the ability to return to the starting form or move to another suitable form through active feedback mechanisms or direct management actions.

A dynamic ensemble method for residential short-term load forecasting

In this paper, a dynamic ensemble method is proposed to forecast the residential short-term load accurately. The main idea is to utilize the state-space approaches to dynamically adjust the weight coefficients used to combine the base models. The dynamic ensemble method is divided into two stages. In the first stage, the least square method is utilized to estimate the weight coefficients. In the second stage, the particle filter is employed to exclude the estimation error in the first stage and update the weight coefficients dynamically to improve the ensemble accuracy. Then, three heterogeneous models (i.e., vector autoregression model, Gaussian process regression model, and the long short-term memory neural network) are employed as the base models and combined based on the weight coefficients to forecast the residential load. The numerical tests are conducted considering two scenarios in two public datasets to investigate the performance of the proposed method. The first scenario assumes the base models are accurate and stable, while the second scenario considers the dynamics of the base models. Root mean square error, mean absolute error, and mean absolute percentage error are used to evaluate the performances. These indicators show that the proposed method outperforms in both scenarios and datasets.

Short-term forecast improvement of maximum temperature by state-space model approach: the study case of the TO CHAIR project

Short-term forecast improvement of maximum temperature by state-space model approach: the study case of the TO CHAIR project