Time-varying Parameters Research Articles

An Adaptive Sliding-Mode Observer-Based Fuzzy PI Control Method for Temperature Control of Laser Soldering Process

Abstract In the process of electronic packaging, laser soldering exhibits advantages such as non-contact, minimal thermal affected zone, and rapid soldering process. Additionally, the precision of temperature control in laser soldering process is crucial as it determines the quality of the solder joints. However, the uncertain time-variant parameters and the unknown time-varying bounded disturbances make it challenging for high-precision temperature control to achieve high-quality solder joints. To aimed at the parametric uncertainties and disturbances, an adaptive sliding-mode observer-based fuzzy PI (ASMFPI) control method is proposed. In ASMFPI, an adaptive sliding-mode algorithm-based observer is designed to estimate temperature variations caused by uncertainties and disturbances, aiming to improve the accuracy of the temperature model. An observer-based compensation is employed to enhance the temperature control precision. Then, a fuzzy PI control algorithm is adopted to guarantee the track performence and robustness of the closedloop system. Experiments and comparisons are conducted on a laser soldering machine to validate the effectiveness of the proposed method. The results demonstrate that the designed observer significantly improves the accuracy of the thermodynamic model. Furthermore, comparisons of ASMFPI, well-tuned PI, and feed-forward PI methods indicate that ASMFPI exhibits superior temperature track performance compared to the other two methods.

Adaptive practical prescribed-time control for uncertain nonlinear systems with time-varying parameters

In this paper, adaptive practical prescribed-time (PPT) control is proposed for a class of uncertain nonlinear systems with time-varying parameters and unmodeled dynamics. By constructing a novel time-varying scaling function and utilizing nonlinear mapping, the PPT control is successfully resolved. The dynamical uncertainties resulting from unmodeled dynamics are estimated by employing an auxiliary available signal, and the unknown continuous terms are handled by the aid of radial basis function neural networks (RBFNNs). A novel adaptive control method is developed by introducing the compensating signals and dynamic surface control as well as practical prescribed-time control. All the signals involved are proved to be semi-globally uniformly ultimately bounded, and the tracking error could enter the pre-specified convergence region within a pre-specified time. The robotic manipulator system is used to demonstrate the effectiveness of the proposed control approach.

An adaptive variable-parameter dynamic learning network for solving constrained time-varying QP problem

To efficiently solve the time-varying convex quadratic programming (TVCQP) problem under equational constraint, an adaptive variable-parameter dynamic learning network (AVDLN) is proposed and analyzed. Being different from existing varying-parameter and fixed-parameter convergent-differential neural network (VPCDNN and FPCDNN), the proposed AVDLN integrates the error signals into the time-varying parameter term. To do so, the TVCQP problem is transformed into a time-varying matrix equation. Second, an adaptive time-varying design formulation is designed for the error function, and then, the error function is integrated into the time-varying parameter. Furthermore, the AVDLN is designed with the adaptive time-varying design formulation. Moreover, the convergence and robustness theorems of AVDLN are proved by Lyapunov stability analysis, and Mathematical analysis demonstrates that AVDLN possesses a smaller upper bound on the convergence error and a faster error convergence rate than FPCDNN and VPCDNN. Finally, the validity of AVDLN is demonstrated by simulations, and the comparative results prove that the proposed AVDLN has a faster convergence speed and smaller error fluctuation.

Understanding dynamic return connectedness and portfolio strategies among international sustainable exchange-traded funds

The rapid growth of sustainable investing has led to the global expansion of environmental, social, and governance (ESG) investment products. Existing literature on sustainable investing focuses primarily on corporate social responsibility theory and risk assessment, with relatively little research on ESG investment value and portfolio strategies. Using data from six worldwide ESG exchange-traded funds (ETFs) between 2020 and 2023, we examine return connectedness and portfolio performance by employing a time-varying parameter vector autoregressive (TVP-VAR) and portfolio approaches. The findings reveal that European ETF plays a dominant role in the worldwide ESG system due to the market size and market maturity. Specifically, the European ETF can substantially reduce portfolio volatility. Moreover, the results show that minimum variance and risk-parity portfolios outperform the other portfolio strategies during periods of geopolitical turmoil. These results provide valuable insights for improving the resilience of ESG markets and enhancing sustainable investment strategies.

Integrating complexity in population modelling: From matrix to dynamic models

Matrix models are widely used in population ecology studies and are valuable for analysing population dynamics, although they are limited in the use of time-varying parameters. This limitation can be overcome by dynamic models. In this study, we revisit a previously published study on a matrix model of a population of the box jellyfish Carybdea marsupialis (L. 1758) in the Western Mediterranean. A dynamic model integrating the transition matrix of the original model is developed in STELLA Architect with the following improvements: (1) Sensitivity study of the reliability of the methodology for calculating the transition matrix and estimation of the errors of the fitting parameters; (2) Closure of the jellyfish life cycle by adding the polyp stage. This will make it possible to simulate scenarios of ecological interest over several years such as a decline in food supply, jellyfish removal strategies, changes in drift currents and changes in substrate availability for planulae to settle. (3) The inclusion of more biological reality. In particular, a temporal pattern of strobilation is added, which improves the fit of the model to the field data.

Dynamic and asymmetric connectedness among fossil energies and stock markets of the Belt and Road countries under shocks from extreme events

This study investigates the dynamic and asymmetric return connectedness between fossil energies of crude oil and natural gas, and stock markets of the Belt and Road countries (the B&R stock markets) under shocks from extreme events (e.g. the COVID-19 pandemic and the Russo-Ukrainian war) from 2019 to 2023 by using the time-varying parameter vector autoregression model (TVP-VAR) with the asymmetric connectedness indicator and multilayer spillover networks. We find that: (i) risk spillover between fossil energies and the B&R stock markets is more sensitive to negative information on price changes than positive information, and the asymmetry of the connectedness is much larger during the periods with exogenous shocks induced by extreme events of the COVID-19 pandemic and the Russo-Ukrainian war. (ii) the level of risk spillover between fossil energies and the B&R stock markets has significantly increased after the outbreak of extreme events, and the global shock from the COVID-19 pandemic has more widespread and greater impact on the risk spillover than the geopolitical shock from the Russo-Ukrainian war. (iii) fossil energies perform as risk receivers throughout the full sample period, and risks are primarily transferred from high-income countries to low-income countries within the B&R stock markets, this phenomenon is also intensified by the extreme shocks. Our findings provide valuable guidance and have economic implications for both investors and policymakers worldwide to diversify and manage the risks within the global fossil energy market and the B&R stock markets.

The Volatility Spillover in Metaverse Token Market: TVP-VAR Model Application

In this study, the volatility spillover between metaverse tokens is investigated to guide investors. In the research, price data of Decentraland, StarLink, Axie Infinity, Radio Caca, The Sandbox, Internet Computer, My Neighbor Alice and Enjin Token, for the period 12.14.2021-10.22.2023 analyzes with the time-varying parameter vector autoregressive (TVP-VAR) model developed by Antonakakis et al. (2019). As a result of the research, it determine that Radio Caca and Axie Infinity only receive volatility; My Neighbor Alice and Enjin Token only spread volatility; StarLink, Decentraland and Internet Computer are metaverse tokens that both receive and spread volatility.

Adaptive sliding mode-based feedback linearization control for floating offshore wind turbine in region II

ABSTRACT Wind turbine systems are highly nonlinear and time-variable. Under external interference, the normal and stable operation of the system is seriously affected. In addition, the inertia of the wind turbine causes a serious lag in speed tracking. These effects are even more severe for floating offshore wind turbines (FOWT). To solve these problems, this paper proposes a new optimal torque control form, and further improves and optimizes it. Firstly, the feedback linearization is used to eliminate the nonlinear part of the system and the time-varying parameters, and a new torque control form is obtained. Then, the adaptive sliding mode is used to further optimize the torque controller to enhance the robustness of the system. Finally, adaptive sliding mode-based feedback linearized optimal torque control (ASMFLOTC) was obtained. ASMFLOTC was applied to the FOWT system to verify the effectiveness of its maximum power point tracking (MPPT) control. The results show that ASMFLOTC can better track the reference speed, effectively reduce the relative error, and improve the utilization rate of wind energy. And from the results, the platform motion of the proposed controller is not significantly different from that of other controllers. The proposed controller does not exacerbate the platform motion while increasing the output power. This shows its feasibility.

Cross-scale time-varying prediction model of oxygen diffusion in unsaturated concrete

The oxygen diffusion coefficient is a key factor in predicting the service life of reinforced concrete structures. However, cross-scale time-varying predictive modelling of oxygen diffusion in unsaturated concrete has not been carried out. In this study, firstly, the concrete pore structure is simplified as the Menger sponge model, and the effects of pore parameters, hydration degree and fractal dimension on the five oxygen diffusion mechanisms in unsaturated concrete are considered; Secondly, the pore parameters, degree of hydration and fractal dimension are analyzed with respect to time, taking into account the time-varying pore microstructure of concrete; Lastly, a cross-scale time-varying prediction model for oxygen diffusion in concrete under unsaturated state is established that includes the time-varying parameters and takes into account the five diffusion mechanisms; Finally, a cross-scale time-varying prediction model of oxygen diffusion in concrete under unsaturated condition is established with time-varying parameters and considering five diffusion mechanisms. The results show that the oxygen diffusion coefficient decreases with the increase of hydration degree, water saturation, and fractal dimension. This study will provide a theoretical basis for predicting the life of reinforced concrete in the marine environment.

COVID-19 reinfection processes: mathematical modeling, data fitting with time-varying parameters, and optimal control studies

COVID-19 reinfection processes: mathematical modeling, data fitting with time-varying parameters, and optimal control studies

Online public opinion prediction based on a novel conformable fractional discrete grey model

PurposeAs social networks have developed to be a ubiquitous platform of public opinion spreading, it becomes more and more crucial for maintaining social security and stability by accurately predicting various trends of public opinion dissemination in social networks. Considering the fact that the dissemination of online public opinion is a dynamic process full of uncertainty and complexity, this study establishes a novel conformable fractional discrete grey model with linear time-varying parameters, namely the CFTDGM(1,1) model, for more accurate prediction of online public opinion trends.Design/methodology/approachFirst, the conformable fractional accumulation and difference operators are employed to build the CFTDGM(1,1) model for enhancing the traditional integer-order discrete grey model with linear time-varying parameters. Then, to improve forecasting accuracy, a base value correction term is introduced to optimize the iterative base value of the CFTDGM(1,1) model. Next, the differential evolution algorithm is selected to determine the optimal order of the proposed model through a comparison with the whale optimization algorithm and the particle swarm optimization algorithm. The least squares method is utilized to estimate the parameter values of the CFTDGM(1,1) model. In addition, the effectiveness of the CFTDGM(1,1) model is tested through a public opinion event about “IG team winning the championship”. Finally, we conduct empirical analysis on two hot online public opinion events regarding “Chengdu toddler mauled by Rottweiler” and “Mayday band suspected of lip-syncing,” to further assess the prediction ability and applicability of the CFTDGM(1,1) model by comparison with seven other existing grey models.FindingsThe test case and empirical analysis on two recent hot events reveal that the CFTDGM(1,1) model outperforms most of the existing grey models in terms of prediction performance. Therefore, the CFTDGM(1,1) model is chosen to forecast the development trends of these two hot events. The prediction results indicate that public attention to both events will decline slowly over the next three days.Originality/valueA conformable fractional discrete grey model is proposed with the help of conformable fractional operators and a base value correction term to improve the traditional discrete grey model. The test case and empirical analysis on two recent hot events indicate that this novel model has higher accuracy and feasibility in online public opinion trend prediction.

Dynamic Connectedness Among Alternative and Conventional Energy ETFs Based on the TVP-VAR Approach

This study investigates risk transmission in the US energy instrument market to determine if certain factors, such as crude oil and natural gas, influence this market and whether stock or energy investment portfolios track their behavior. To investigate volatility spillover, the VAR-based connectedness approach is applied. This approach facilitates the measurement of interdependence across a network of variables, providing insights into aggregate, directional, and net interdependence. The use of the time-varying parameter vector autoregression (TVP-VAR) approach, as developed by Antonakakis and Gabauer, avoids the problems associated with selecting rolling window sizes and the resultant loss of observations during estimations. The analysis revealed a distinction between alternative and traditional ETFs, with lower interdependence observed among the volatility of alternative energy ETFs. While most energy ETFs transmit risk within the systems analyzed, some act as risk receivers, though their net receiving/transmitting character fluctuates. The results of this study are significant for investment portfolio managers.

The Path to Sustainable Stability: Can ESG Investing Mitigate the Spillover Effects of Risk in China’s Financial Markets?

In the context of a low-carbon economic transition and escalating uncertainties in financial markets, understanding the relationship between the long-term benefits of ESG (Environmental, Social, and Governance) investments and the stability of China’s financial markets emerges as a critical issue. This paper analyzes the risk contagion mechanisms within China’s financial system from the perspective of volatility spillovers associated with ESG investments. Initially, the study employs the Time-Varying Parameter Vector Autoregression (TVP-VAR) model to calculate the variance decomposition spillover index, contrasting the dynamics and risk transmission mechanisms of market volatility between portfolios composed of ESG and conventional stocks. Building upon the analysis of risk spillover relations among financial sub-markets, the study utilizes the generalized forecast error variance decomposition method to construct a complex network of financial system risk spillovers, investigating the risk contagion characteristics within both financial systems through network topology. Empirical findings indicate a significant reduction in the risk and net spillover effects of China’s financial system when ESG stock indices replace conventional stock indices, with a notable mutation in the volatility spillover network structure during extreme risk events and even more substantial changes during the COVID-19 pandemic. Furthermore, based on volatility spillover analysis, the study computes optimal weights and hedging strategies for portfolios incorporating the ESG volatility index and other market volatility indices. The conclusions of this research are instrumental for regulatory authorities in establishing early warning mechanisms and for investors in avoiding financial investment risks.

A framework for counterfactual analysis, strategy evaluation, and control of epidemics using reproduction number estimates.

During pandemics, countries, regions, and communities develop various epidemic models to evaluate spread and guide mitigation policies. However, model uncertainties caused by complex transmission behaviors, contact-tracing networks, time-varying parameters, human factors, and limited data present significant challenges to model-based approaches. To address these issues, we propose a novel framework that centers around reproduction number estimates to perform counterfactual analysis, strategy evaluation, and feedback control of epidemics. The framework 1) introduces a mechanism to quantify the impact of the testing-for-isolation intervention strategy on the basic reproduction number. Building on this mechanism, the framework 2) proposes a method to reverse engineer the effective reproduction number under different strengths of the intervention strategy. In addition, based on the method that quantifies the impact of the testing-for-isolation strategy on the basic reproduction number, the framework 3) proposes a closed-loop control algorithm that uses the effective reproduction number both as feedback to indicate the severity of the spread and as the control goal to guide adjustments in the intensity of the intervention. We illustrate the framework, along with its three core methods, by addressing three key questions and validating its effectiveness using data collected during the COVID-19 pandemic at the University of Illinois Urbana-Champaign (UIUC) and Purdue University: 1) How severe would an outbreak have been without the implemented intervention strategies? 2) What impact would varying the intervention strength have had on an outbreak? 3) How can we adjust the intervention intensity based on the current state of an outbreak?

Modeling and Nonlinear Dynamic Characteristics Analysis of Fault Bearing Time-Varying Stiffness-Flexible Rotor Coupling System

There is a complex dynamic interaction between the aero-engine bearing and the rotor, and the resulting time-varying system parameters have an impact on the nonlinear dynamic characteristics of the rolling bearing-flexible rotor system. In this study, the interaction between the time-varying stiffness of the rolling bearing and the transient response of the flexible rotor is considered. The Newmark-β integral method is used to solve the dynamic equation, and the relationship between the time-varying characteristics of bearing stiffness and load and the dynamic characteristics of the rotor is studied. The relationship between bearing stiffness and vibration strength is analyzed, and the influence of damage size on the time domain signal energy of the disc is analyzed. The results show that the model established in this paper can accurately reflect the dynamic interaction between the bearing and the rotor. With the extension of the bearing damage, the dynamic stiffness of the bearing attenuates, the intensity of the excitation force increases, and the vibration is transmitted to the disc, which affects the motion stability and vibration response of the disc.

Causality, Connectedness, and Volatility pass-through among Energy-Metal-Stock-Carbon Markets: New Evidence from the EU

The EU carbon market serves as an innovative financial instrument with the primary objective of contributing to mitigating the impacts of climate change. This market demonstrates significant interconnectedness with fossil energy, precious metal, and financial markets, although limited research has focused on the causality, dependency, intensity and direction of time-varying spillover effects. This study examines how the energy, metal, and financial markets have an impact on the EU carbon market. It focuses on three main research questions, namely: 1) how do these markets affect each other?; 2) how do they connect?; 3) how do volatilities spillover among them? By answering these questions, the study aims to assist EU decision makers to develop effective carbon policies, help investors manage risks and promote practices that are consistent with the EU's climate goal. To achieve these objectives, this paper proposes a novel methodological approach that combines the most recent econometrics methods, such as Directed Acyclic Graph analysis, Canonical Vine Copula models, and Time-Varying parameter Vector Auto Regressive models with Stochastic Volatility with the use of a comprehensive sample of daily data from April 26, 2005 to December 31, 2022. The major findings of this study demonstrate that causality predominantly runs from energy, metal, and financial markets to the EU carbon market. The dependency structure, although varying across different sub-periods, shows a strong relationship observed between oil, coal, silver, copper, EuroStoxx600, and CO2 market. Additionally, the oil and copper futures prices exhibit the highest dependence on EUA prices. Furthermore, the study establishes that the EU carbon market is a net receiver of shocks from all other markets, with the energy, metal, and financial markets significantly influencing volatility in EUA prices. The time-varying spillover effect is most pronounced with a one-day lag, and the duration of the spillover effects ranges from 2 to 15 days, gradually diminishing over time. These results have the potential to increase the understanding of the EU carbon market and offer practical guidance for policymakers, investors, and companies involved in this domain.

Type-2 fuzzy adaptive robust fault-tolerant control for manipulators with disturbance observer

A configuration of interval type-2 (IT2) fuzzy adaptive fault-tolerant control strategy is designed for the position trajectory tracking of cooperated robot manipulators carrying a common object. First, a non-zero time-varying parameter is installed in IT2 FLS, then a property of universal approximation of IT2 fuzzy logic system (FLS) is proposed, where the non-zero time-varying parameter has the relation with the approximation precision of IT2 FLS. Second, for the unknown compounded disturbance, a disturbance observer is designed to estimate it and is integrated into the IT2 adaptive control. To improve the approximation accuracies, the novel IT2 FLSs with non-zero parameter are utilized to compensate for the uncertain nonlinear terms with including the uncertainties and fault components in the system, the fault-tolerant control with some adaptive laws are established using the stability theory of barrier Lyapunov function (BLF), where the approximation accuracies can be modified online using the provided adaptive laws with neglecting the number of fuzzy rules, the computation burden of the proposed control is greatly reduced because of a small number of updated laws. Finally, the simulation results are compared with those of several existing control methods, which confirms the validity of the designed control approach.

A Sequential Calibration Approach to Address Challenges of Repeated Calibration of a COVID-19 Model.

Mathematical models served a critical role in COVID-19 decision making throughout the pandemic. Model calibration is an essential, but often computationally burdensome, step in model development that provides estimates for difficult-to-measure parameters and establishes an up-to-date modeling platform for scenario analysis. In the evolving COVID-19 pandemic, frequent recalibration was necessary to provide ongoing support to decision makers. In this study, we address the computational challenges of frequent recalibration with a new calibration approach. We calibrated and recalibrated an age-stratified dynamic compartmental model of COVID-19 in Minnesota to statewide COVID-19 cumulative mortality and prevalent age-specific hospitalizations from March 22, 2020 through August 20, 2021. This period was divided into 10 calibration periods, reflecting significant changes in policies, messaging, and/or epidemiological conditions in Minnesota. When recalibrating the model from one period to the next, we employed a sequential calibration approach that leveraged calibration results from previous periods and adjusted only parameters most relevant to the calibration target data of the new calibration period to improve computational efficiency. We compared computational burden and performance of the sequential calibration approach to a more traditional calibration method, in which all parameters were readjusted with each recalibration. Both calibration methods identified parameter sets closely reproducing prevalent hospitalizations and cumulative deaths over time. By the last calibration period, both approaches converged to similar parameter values. However, the sequential calibration approach identified parameter sets that more tightly fit calibration targets and required substantially less computation time than traditional calibration. Sequential calibration is an efficient approach to maintaining up-to-date models with evolving, time-varying parameters and potentially identifies better-fitting parameter sets than traditional calibration. This study used a sequential calibration approach, which takes advantage of previous calibration results to reduce the number of parameters to be estimated in each round of calibration, improving computational efficiency and algorithm convergence to best-fitting parameter values.Both sequential and traditional calibration approaches were able to identify parameter sets that closely reproduced calibration targets. However, the sequential calibration approach generated parameter sets that yielded tighter fits and was less computationally burdensome.Sequential calibration is an efficient approach to maintaining up-to-date models with evolving, time-varying parameters.

The determinants of volatility connectedness of South African equity super sectors

The paper aims to explore the determinants of total volatility connectedness of nine super sectors on the Johannesburg Stock Exchange (JSE) market from 3rd January 2006 to 31st December 2021. These sectors are Automobile and Parts, Chemical, Telecommunication, Technology, Energy, Health, Finance, Insurance, and General Industrials. The paper applied Diebold and Yilmaz connectedness matrix and the time-varying parameter – vector autoregressive (TVP-VAR) model to determine the sectorial total volatility connectedness index (STVCI). After that, the nonlinear autoregressive distributed lag model (NARDL) was used to determine the asymmetric effects and the drivers of STVCI. It was found that the partial sum decomposition of the South African volatility index (SAVI) and Economic Policy Uncertainty Index (EPU) are the key determinants of the STVCI both in the long and short run. However, domestic market return (DMR) shows no significant asymmetric effect on STVCI. The study concluded that SAVI and EPU are the key determinants of volatility connectedness among the JSE super-sectors. The results unveil important implications for sectorial investors and policymakers on potential regulations and stability of the significant determinants of spillover risk.

System Identification for Robust Control of an Electrode Positioning System of an Industrial Electric Arc Melting Furnace

Through system identification for robust control methods and utilizing real-time experimental field data, a comprehensive mathematical model is derived that represents the dynamic performance of a single electrode positioning system (EPS) in an industrial electric arc melting furnace (EAF). This EPS is characterized by large, time-varying dynamic parameters, which fluctuate based on operating conditions, specifically as the electrode weight changes within its operational range. The system identification methodology for robust control is developed in four main steps, progressing from experimental design to model validation. This approach yields a nominal model of the actual system and provides a trustworthy estimate of the region of uncertainty of the model, bounded by models of the real system under maximum and minimum electrode weight conditions (limit operating models). The methodology generates three fourth-order time-delay models using an ARMAX structure. The results are promising, as system identification for robust control enables the derivation of mathematical models specifically tailored for designing robust controllers. These controllers significantly enhance the EPS control system’s performance and substantially reduce energy consumption and environmental emissions.