Linear Dynamic Model Research Articles

An Improved Model-Free Adaptive Nonlinear Control and Its Automatic Application

In order to enhance the performance of model-free adaptive control (MFAC) in solving the control problem caused by interference and improve the tracking speed, this paper focuses on the analysis and research of the system affected by interference using the MFAC method. This method is based on dynamic linearization technology, with system data which are represented by a full format dynamic linearization (FFDL) model that is very similar to actual industrial processes. In this work, a control law is derived by incorporating and assigning weights to both the output error and the output error rate (OER) as the performance index. Rigorous proofs are provided to establish convergence and stability. Considering the inherent complexity of actual systems, this paper also presents the MFAC-OER scheme for multiple-input–multiple-output (MIMO) systems. Furthermore, the effectiveness and practicality of the improved control strategy are evaluated through numerical arithmetic examples and control processes involving water level regulation in a circulating fluidized bed (CFB). Comparisons with conventional MFAC and PID control methods show that the enhanced control method is capable of quickly and accurately tracking the desired signal. Additionally, it exhibits superior anti-interference characteristics and is able to respond in a timely manner to changes in the operating conditions of the circulating fluidized bed system. As a result, it ensures the normal operation of the coal saver and water supply pipe without damage.

Islamic Trade Financing and Poverty Mitigation: An Econometric Estimation Informing Islamic Development Bank Member Countries Considering Their Entrepreneurial Ecosystems

This paper is an analysis of the empirical effect of entrepreneurial ecosystems on the relationship between Islamic Trade financing (TF) and poverty trends in the context of Islamic Development Bank (IsDB) member countries. The study is an empirical analysis seeking to find out whether there is a statistical effect of the entrepreneurial ecosystems, measured as Global Entrepreneurship Index (GEI) scores, on the relationship between the Islamic Trade Financing dollar amounts by the IsDB (TF) and the GNI Per Capita, PPP, of all Islamic Development Bank (IsDB) member countries for the years 2009 to 2019. The research is designed as a desk-top longitudinal, correlation, employing the linear dynamic panel data GMM model as an estimator of the empirical relationships between the key variables of the study. The study results show that there is a significant positive relationship between the TF dollar amounts from the IsDB and the GNI Per capita, PPP, and that the ascending entrepreneurial ecosystem positively affects this relationship in the IsDB member countries. Therefore, countries that receive higher Islamic Trade Financing dollar amounts from the IsDB (IF), generally have more GNI Per capita, PPP (less poverty) than their counterparts, but ascending entrepreneurial ecosystem levels raises this positive relationship. It is, therefore, recommendable for IsDB member countries and all other interested countries to formulate policies that facilitate Islamic Trade financing to mitigate poverty and improve their entrepreneurial ecosystems for economic progress. This paper develops policy conversations that guide political discourse on policy topics of poverty mitigation and their relation to financing trade following Islamic products and services, while improving the entrepreneurial environment, thus generating information on policy choices of an Islamic financing nature.

Numerical analysis of high-speed railway slab tracks using calibrated and validated 3D time-domain modelling

Concrete slabs are widely used in modern railways to increase the inherent resilient quality of the tracks, provide safe and smooth rides, and reduce the maintenance frequency. In this paper, the elastic performance of a novel slab trackform for high-speed railways is investigated using three-dimensional finite element modelling in Abaqus. It is then compared to the performance of a ballasted track. First, slab and ballasted track models are developed to replicate the full-scale testing of track sections. Once the models are calibrated with the experimental results, the novel slab model is developed and compared against the calibrated slab track results. The slab and ballasted track models are then extended to create linear dynamic models, considering the track geodynamics, and simulating train passages at various speeds, for which the Ledsgård documented case was used to validate the models. Trains travelling at low and high speeds are analysed to investigate the track deflections and the wave propagation in the soil, considering the issues associated with critical speeds. Various train loading methods are discussed, and the most practical approach is retained and described. Moreover, correlations are made between the geotechnical parameters of modern high-speed rail and conventional standards. It is found that considering the same ground condition, the slab track deflections are considerably smaller than those of the ballasted track at high speeds, while they show similar behaviour at low speeds.

State-space model construction and dynamic analysis for a space thermionic nuclear reactor

The space nuclear reactor (SNR) is an important energy pattern for spacecrafts due to its high power density, long life and stabilization. A linear dynamic model is necessary to analyze the dynamics and design the appropriate control system analytically. A simplified nonlinear model based on conservation equations including point reactor kinetics model, thermal-hydraulic model, coolant model and heat pipe and radiator model was constructed. The state-space linear model was derived using perturbation theory. To verify the linear model, comprehensive comparisons were made in both time domain and frequency domain. The dynamic characteristics with respect to reactor power and reactivity were obtained in frequency and time domains. It is concluded that the reactor is an unstable system with a large time delay and strong nonlinearity at different power levels. This study provides solid foundation for future control system design.

Hybrid-timescale dispatch of heat and electricity integrated system considering dynamic heat flow

In the electricity and heat integrated energy system, the transmission speed of heat energy is much slower than that of electricity. While the ignorance of the dynamic characteristic of heat system during the dispatch process often fails to satisfy the heat load. To address this issue and further improve the flexibility of the system, a dynamic linear model of heat pipeline is proposed, and a hybrid-timescale optimal dispatch strategy is established in this paper. Firstly, based on the finite difference method, the partial differential equation of heat pipeline is converted into a linear equality constraint. Then, a hybrid-timescale optimal dispatch model is established. By considering different dynamic characteristics of heat and power system, the calculation of dynamic linear model and dispatching of devices of power and heat system are realized under three different timescales. Finally, by using MATLAB, the effectiveness of the proposed dynamic model is validated compared with the conventional steady-state model and measured data. A series of simulation results verify that the proposed hybrid-timescale optimal dispatch strategy considering dynamic model helps the system to increase the supply rate of the heat load by 100% and decrease the economic cost,CO2andNOxemission by 0.79%, 0.2%, and 18.9%, respectively.

Intensification of heatwaves in China in recent decades: Roles of climate modes

Modes of climate variability can affect weather extremes, posing intractable challenges to our environment. However, to what extent climate modes can modulate heatwaves in China under a warming background remains poorly understood. Here, we examine the changes in heatwave intensity in seven distinct regions: three East, two middle, and two west regions over China and systematically explore the impacts of climate modes, by analyzing observations and performing model experiments using a Bayesian dynamic linear model and an atmospheric general circulation model (AGCM). Abrupt increases in heatwave intensity are detected across China during a transition period of 1993–2000, and the intensification remains robust in northern and western China after the warming trend being removed. The combined impacts of the El Niño–Southern Oscillation (ENSO), Atlantic Multidecadal Oscillation (AMO), and Indian Ocean Dipole (IOD) explain 62.35–70.01% of the observed heatwave intensification in East I, Middle I, West I, and West II regions. Decadal changes of atmospheric circulations associated with the negative phase transition of the Interdecadal Pacific Oscillation (IPO), which is highly correlated with the decadal variability of ENSO, combined with the positive phase transition of the AMO around the mid-1990s increase surface air temperature and enhance atmospheric internal variability and climate modes’ impacts, resulting in the abrupt increase of heatwaves in the past two decades. These results highlight the importance of the concurrent phase transitions of decadal climate modes in regulating heatwaves.

Integrating machine learning and environmental variables to constrain uncertainty in crop yield change projections under climate change

Robust crop yield projections under future climates are fundamental prerequisites for reliable policy formation. Both process-based crop models and statistical models are commonly used for this purpose. Process-based models tend to simplify processes, minimize the effects of extreme events, and ignore biotic pressures, while statistical models cannot deterministically capture intricate biological and physiological processes underpinning crop growth. We attempted to integrate and overcome shortcomings in both modelling frameworks by integrating the dynamic linear model (DLM) and random forest machine learning model (RF) with nine global gridded crop models (GGCM), respectively, in order to improve projections and reduce uncertainties of maize (Zea mays L.) and soybean (Glycine max [L.] Merrill) yield projections. Our results demonstrated substantial improvements in model performance accuracy by using RF in concert with GGCM across China’s maize and soybean belt. This improvement surpasses that achieved using DLM. For maize, the GGCM+RF models increased the r values from 0.15 to 0.61–0.64–0.77 and decreased nRMSE from approximately 0.20 to 0.50–0.13–0.17 compared with using GGCM alone. For soybean, the models increased r from 0.37 to 0.70–0.54–0.70 and decreased nRMSE from 0.17 to 0.35–0.17–0.20 compared with using GGCM alone. The main factors influencing maize yield changes included chilling days (CD), crop pests and diseases (CPDs), and drought, while for soybean the primary influencing factors included CPD, tropical days (based on exceeding a maximum temperature), and drought. Our approach decreased uncertainties by 33–78% for maize and by 56–68% for soybean. The main source of uncertainty for GGCM was the crop model. For GGCM+RF, the main source of uncertainty for the 2040–2069 period was the global climate model, while the main source of uncertainty for the 2070–2099 period was the climate scenario. Our results provide a novel, robust, and pragmatic framework to constrain uncertainties in order to accurately assess the impact of future climate change on crop yields. These results could be used to interpret future ensemble studies by accounting for uncertainty in crop and climate models, as well as to assess future emissions scenarios.

Atmospheric Dispersion Modeling Using a Stochastic Wind Model

In this work, we propose a stochastic wind field based on the Bayesian dynamic linear model to account for the wind flow field in the transient advection-diffusion partial differential equation (PDE). The resulting dispersion model accounts for the time variation in the wind field and meteorological variables, allowing the simulation of a transient regime. The main advantage of using such a wind field model over a Fourier series to fit wind time series is its potential to make predictions. In addition, a suitable methodology is necessary to solve the resulting dispersion model. In this work, we use a finite element formulation appropriate to solve transient advection-diffusion PDEs. We verify the accuracy of the proposed methodology by reproducing a case study considering a field tracer experiment. The model evaluation against experimental data shows the good performance of the proposed dispersion model.

Dynamic modeling and control of a solar sail considering large-amplitude nonlinear vibration

The solar sail spacecraft utilizes Solar Radiation Pressure (SRP) to achieve propulsion, which can realize long-term acceleration without fuel consumption, making it an appealing option for deep space exploration and polar-sitter observation. However, because of the large-scale and high-flexibility of the membrane structure, the solar sail poses challenging dynamic and control issues. Precise dynamic modeling and effective control design can provide theoretical guidance for the efficient functioning of solar sail, which are of great significance and urgently need to be studied. In this paper, a dynamic model that can describe the large-amplitude vibrations of the solar sail is established. Additionally, an attitude tracking controller and a cable-based nonlinear vibration controller are designed to control the attitude motion and flexible vibration of the solar sail. The results show that the established nonlinear rigid-flexible coupling dynamic model is more accurate compared to the linear dynamic model. The polynomial fitting method can characterize the nonlinear dynamics of the solar sail extremely effectively while cutting the program runtime to 5% of the original. Two controllers designed in this paper can make the solar sail maneuver follow the desired trajectory accurately while reducing the sail surface vibration by more than 40%. The research outcomes of this paper can provide references for the dynamic study and active control of large-scale membrane spacecraft.

Forecasting models for the electricity consumption of the cement industry in Brazil

The consumption of electric energy in the Brazilian industrial sector has been investigated over the past few years. This interest is related to sector development, energy planning, and energy efficiency. Thus, forecasting models are important for decision-making. The objective of this work is to compare different forecasting models applied to monthly data on electric energy consumption in the cement industry in Brazil. Therefore, the Holt-Winters method, the Seasonal ARIMA model, the dynamic linear model, and the autoregressive neural network model were used. Through the considered accuracy metrics, the Seasonal ARIMA model showed the best predictive performance for the analyzed period.

Dynamic ICAR Spatiotemporal Factor Models

We propose a novel class of dynamic factor models for spatiotemporal areal data. This novel class of models assumes that the spatiotemporal process may be represented by some few latent factors that evolve through time according to dynamic linear models. As the dimension of the vector of latent factors is typically much smaller than the number of subregions, our proposed class of models may achieve substantial dimension reduction. At each time point, the vector of observations is linearly related to the vector of latent factors through a matrix of factor loadings. Each column of this matrix may be seen as a vectorized map of factor loadings relating one latent factor to the vector of observations. Thus, to account for spatial dependence, we assume that each column of the matrix of factor loadings follows an intrinsic conditional autoregressive (ICAR) process. Hence, we call our class of models the Dynamic ICAR Spatiotemporal Factor Models (DIFM). We develop a Gibbs sampler for exploration of the posterior distribution. In addition, we develop model selection through a Laplace-Metropolis estimator of the predictive density. We present two case studies. The first case study, which is for simulated data, demonstrates that our DIFMs are identifiable and that our proposed inferential procedure works well at recovering the underlying data generating process. Finally, the second case study demonstrates the utility and flexibility of our DIFM framework with an application to the drug overdose epidemic in the United States from 2015 to 2021.

Unconstrained feedback controller design using Q-learning from noisy process data

This paper develops a novel model-free Q-learning based approach to estimate linear, unconstrained feedback controllers from noisy process data. The proposed method is based on an extension of an available approach developed to estimate the linear quadratic regulator (LQR) for linear systems with full state measurements driven by Gaussian process noise of known covariance. First, we modify the approach to treat the case of an unknown noise covariance. Then, we use the modified approach to estimate a feedback controller for linear systems with both process and measurement noise and only output measurements. We also present a model-based maximum likelihood estimation (MLE) approach to determine a linear dynamic model and noise covariances from data, which is used to construct a regulator and state estimator for comparisons in simulation studies. The performances of the model-free and model-based controller estimation approaches are compared with an example heating, ventilation, and air-conditioning (HVAC) system. We show that the proposed Q-learning approach estimates a reasonably accurate feedback controller from 24 h of noisy data. The controllers estimated using both the model-free and model-based approaches provide similar closed-loop performances with 3.5 and 2.7% losses respectively, compared to a perfect controller that uses the true dynamic model and noise covariances of the HVAC system. Finally, we give future work directions for the model-free controller design approaches by discussing some remaining advantages of the model-based approaches.

Hysteresis Modeling and Compensation for a Fast Piezo-Driven Scanner in the UAV Image Stabilization System

The fast piezo-driven scanner (FPDS) compensates for vibrations in the unmanned aerial vehicle (UAV) image stabilization system. However, the hysteresis nonlinearity reduces the positioning accuracy of the FPDS. To address this challenge, this paper presents a novel weighted polynomial modified Bouc–Wen (WPMBW) model cascaded with a linear dynamic model to describe counterclockwise, asymmetric, and rate-dependent hysteresis loops of an FPDS. The proposed approach utilizes the weighted polynomial function to describe the asymmetric characteristic and the linear dynamic model to capture the rate-dependent behavior. By modifying the last two terms in the classical Bouc–Wen (CBW) model, the modified BW model directly characterizes the counterclockwise hysteresis loops with fewer parameters, circumventing the algebraic-loop problem that arises in the inverse CBW model. The pseudorandom binary sequence (PRBS) input is employed to decouple the linear dynamic model from the WPMBW model. The sinusoidal input is then applied to stimulate the hysteresis phenomenon, and the parameters of the WPMBW model are estimated by the particle swarm optimization (PSO) toolbox. Experimental results on a commercial FPDS show that the proposed model is superior to the CBW and traditional asymmetric BW models in modeling accuracy and feedforward hysteresis compensation.

Prescribed performance control for nonlinear parameter-varying systems with an application to turbofan engine

In this paper, we investigated the problem of the prescribed performance control for the turbofan engine described by nonlinear parameter-varying (NPV) systems formulation. NPV systems for describing the turbofan engine are set up. Compared with the existing dynamic linear models and nonlinear models, the turbofan engine NPV model can show its dynamics time-varying features and nonlinearity. Meanwhile, based on the turbofan engine NPV system, a prescribed performance tracking controller is designed by error mapping function, and a class of state-and-parameter-dependent (SAPD) control synthesis conditions are formulated. These SAPD conditions can be effectively solved by sum-of-squares technique, and steady-state and transient performance of tracking error can be ensured. Finally, simulation results on the turbofan engine have been given to verify the feasibility and effectiveness of the prescribed performance tracking control scheme.

A composite weighted human learning network and its application for modeling of the intermediate point temperature in USC

Ultra supercritical power plant (USC) is a complex system associated with nonlinearity, uncertainties and multivariable couplings. Generally, it is difficult to build an accurate model to approximate the dynamic behavior of USC. This paper presents a novel composite weighted human learning optimization network (CWHLO) to tackle the above-mentioned problem. Firstly, by fully using of the statistic characteristic of the history operating data, K-means clustering algorithm is applied to partition the raw date, which extremely reduces the operating nonlinearity. Then, an improved real-coded human learning optimization (HLO) is adopted to built linear models in local regions. Different from conventional methods, the advantage of the proposed CWHLO is that the nonlinear model of the object is effectively replaced by a real-time dynamic linear model, which is more suitable for other control methods. Finally, the CWHLO model is compared with the traditional recursive least square method (RLS), and four other meta-heuristic algorithms, to show the advantages in approximating the dynamic behavior of USC.

TDI-like multi-slit hyperspectral imaging for enhanced throughput via the Kalman filter.

The time-delay integration (TDI) technique is increasingly used to improve the signal-to-noise ratio (SNR) of remote sensing and imaging by exposing the scene multiple times. Inspired by the principle of TDI, we propose a TDI-like pushbroom multi-slit hyperspectral imaging (MSHSI) approach. In our system, multiple slits are used to significantly improve the throughput of the system, thereby enhancing the sensitivity and SNR through multiple exposures of the same scene during pushbroom scan. Meanwhile, a linear dynamic model for the pushbroom MSHSI is established, where the Kalman filter (KF) is employed to reconstruct the time-varying overlapped spectral images on a single conventional image sensor. Further, we designed and fabricated a customized optical system that can operate in both multi-slit and single slit modes to experimentally verify the feasibility of the proposed method. Experimental results indicate that the developed system improved SNR by a factor of about 7 compared to that of the single slit mode, while demonstrating excellent resolution in both spatial and spectral dimensions.

Event‐triggered model‐free adaptive control for nonlinear systems with output saturation

AbstractIn this article, an original event‐triggered model‐free adaptive control is presented for nonlinear systems with output constraints. Firstly, a compact form dynamic linear model for this nonlinear system is established. Based on the output saturated data, the pseudo partial derivative (PPD) parameter is designed to identify the linear model. Then, a novel event‐triggered mechanism is inserted into the controller. The mechanism will be activated only if the event‐triggered error satisfies the predefined condition; Otherwise, it keeps the last triggering state. The purpose is to alleviate the transmission burden and save the communication resources. Varying from the existing studies, the main innovation of this article is that the controller is reconstructed in virtue of saturated data and event‐triggered condition. The present algorithm convergence is proved. Finally, the simulation study illustrates the feasibility of the plan.

Simultaneity in binary outcome models with an application to employment for couples

Two of Peter Schmidt’s many contributions to econometrics have been to introduce a simultaneous logit model for bivariate binary outcomes and to study estimation of dynamic linear fixed effects panel data models using short panels. In this paper, we study a dynamic panel data version of the bivariate model introduced in Schmidt and Strauss (Econometrica 43:745–755, 1975) that allows for lagged dependent variables and fixed effects as in Ahn and Schmidt (J Econom 68:5–27, 1995). We combine a conditional likelihood approach with a method of moments approach to obtain an estimation strategy for the resulting model. We apply this estimation strategy to a simple model for the intra-household relationship in employment. Our main conclusion is that the within-household dependence in employment differs significantly by the ethnicity composition of the couple even after one allows for unobserved household specific heterogeneity.

Environmental-economic efficiency for carbon neutrality: the role of eco-innovation, taxation, and globalization in OECD countries

ABSTRACT The aim of this paper is to examine the relationship between three determinants – environmental patents, environmental taxation and trade globalization – and the environmental-economic efficiency of 29 OECD countries between 2005 and 2020. Using the Global Malmquist-Luenberger index, this research computes the environmental productivity growth and its main drivers – the catch-up and the frontier shift terms. Besides, dynamic panel linear models are applied to investigate how the three institutional variables affect the dynamics of the computed efficiency indices. Results are as follows: firstly, eco-innovation is the most relevant factor in boosting the environmental productivity growth, pushing forward the technological frontier, and spurring the catch-up term. Secondly, environmental taxation is an ineffective policy instrument in promoting the sustainable growth and technological frontier advancements, having positive impacts only on the catch-up term. Thirdly, trade globalization reveals to hinder the sustainable growth and its two main drivers.

A flexible two-piece normal dynamic linear model

We construct a flexible dynamic linear model for the analysis and prediction of multivariate time series, assuming a two-piece normal initial distribution for the state vector. We derive a novel Kalman filter for this model, obtaining a two components mixture as predictive and filtering distributions. In order to estimate the covariance of the error sequences, we develop a Gibbs-sampling algorithm to perform Bayesian inference. The proposed approach is validated and compared with a Gaussian dynamic linear model in simulations and on a real data set.