l₁-gain performance analysis and positive filter design for positive discrete-time Markov jump linear systems : a linear programming approach

This paper is concerned with the l₁-gain performance analysis and positive filter design for a positive discrete-time Markov jump linear system (MJLS) by using a linear programming (LP) approach. First, by constructing a linear stochastic Lyapunov function and introducing an ‘‘equivalent’’ deterministic positive discrete-time linear system, necessary and sufficient conditions in the form of LP are derived for stochastic stability and l₁-gain performance of the positive discrete-time MJLS. Then a sufficient conditionon the existence of the desired positive l₁-gain filter is provided. The desired positive l₁-gain filter can bedesigned by solving a standard LP problem. The pest’s structured population dynamic model is employed to illustrate the effectiveness of the proposed method

Finite-time stability and stabilization for a class of nonlinear discrete-time descriptor switched systems with time-varying delay

This paper is concerned with the finite-time stability analysis and state feedback stabilization controller design fora class of nonlinear discrete-time descriptor switched systems with time-varying delay. First, by using the implicit function theorem and constructing a switched Lyapunov functional, sufficient conditions are developed which guarantee that the nonlinear discrete-time descriptor switched system with time-varying delay is regular, causal, has a unique solution in a neighborhood of the equilibrium point, and is uniformly finite-time stable. Then a delay-dependent condition on the existence of a finite-time state feedback controller is proposed. Finally, two numerical examples are given to show the effectiveness of the proposed methods

Preface to special issue on system dynamics and control of virtually coupled trainsets

Virtual coupling (VC) in railways is an emerging technology that takes advantage of the development of Information and Communication Technologies (ICT) to achieve even higher railway network capacity. Compared with moving block systems, VC allows trains to operate with headways that are shorter than the absolute braking distance. The topic has seen tremendous interest and development in recent years. However, most research to date has focused on the application of control theories that rely on simplified models [Citation1], which may not adequately represent the complex real-world dynamics of train systems. This special issue was then proposed to attract attentions from vehicle system dynamics researchers to work on this topic. The guest editor team hoped to combine the concepts, methods and techniques of vehicle system dynamics with VC, lead to a more realistic understanding and implementation of this innovative approach to railway operation. The special issue was also aimed to foster a comprehensive discussion on developing and validating models that accurately represent the actual control and dynamics of train systems.</p

Tropical methane emissions explain large fraction of recent changes in global atmospheric methane growth rate

Large variations in the growth of atmospheric methane, a prominent greenhouse gas, are driven by a diverse range of anthropogenic and natural emissions and by loss from oxidation by the hydroxyl radical. We used a decade-long dataset (2010–2019) of satellite observations of methane to show that tropical terrestrial emissions explain more than 80% of the observed changes in the global atmospheric methane growth rate over this period. Using correlative meteorological analyses, we show strong seasonal correlations (r = 0.6–0.8) between large-scale changes in sea surface temperature over the tropical oceans and regional variations in methane emissions (via changes in rainfall and temperature) over tropical South America and tropical Africa. Existing predictive skill for sea surface temperature variations could therefore be used to help forecast variations in global atmospheric methane

Long railway track modelling – A parallel computing approach

This paper presents the development of a dynamics model for long track sections. It is based on an established short track model that utilises the Finite Element Method to describe rails and block models to describe sleepers, ballast and subballast. By implementing a parallel computing method, this innovation enables the construction of a true long track model: by segmenting the long track into shorter segments that are easier to compute. The model facilitates simulations to be run in parallel, thereby permitting simultaneous calculations of various numerical track variables. The model employs a Message Passing Interface framework to seamlessly link the track segments, handling the flow of data among the computing cores designated to each subdivided section. This strategic framework allows the long track model with the capability to simulate tracks of virtually any length, with the only constraints being the available computational resources and time. The claimed contribution about modelling capability is verified using two case studies on a 6km-long track involving different practical and conceptual train operational scenarios: emergency braking and constant braking force with constant train speed. These case studies show the flexibility and scalability of the method and its capability to handle complex track dynamic systems.</p

Physical coupling and decoupling of railway trains at cruising speeds: Train control and dynamics

This paper studied an emerging technology called Dynamic Coupling that to allow physical coupling and decoupling of railway trains at cruising speeds. A train controller and a train model were introduced and simulated using Parallel Computing. Two transitional gap references were designed (hyperbolic tangent and exponential). Two six-vehicle passenger trains coupling and decoupling on a revised real-world track section at 80 km/h were simulated. Results indicated that a certain negative (e.g. -5 mm) Reference Gap at Coupling Instant (RG@CI) and a positive (e.g. 2 mm) Reference Gap at Decoupling Instant (RG@DI) were recommended to ensure a quick coupling process and to avoid decoupling impacts. Comparatively, the exponential case was better to reduce infrastructure upgrade requirements and to achieve better comfort. The simulated train movements well matched the designed gap references; Dynamic Coupling was possible in terms of train dynamics in the simulated cases

A time headway control scheme for virtually coupled heavy haul freight trains

Virtual coupling of railway trains is an emerging technology that has the potential to significantly increase railway operational efficiency by reducing the train following distance from absolute braking distances to relative braking distances. Current research in this topic is mainly focused on passenger trains and uses distance-based headways. This paper studied virtual coupling for heavy haul freight trains and demonstrated that the distance headway scheme was challenging and sometimes impractical for heavy haul trains to achieve virtual coupling. A time-based headway scheme was then proposed to set the follower train to be a certain time behind the schedule of the leader train rather than a distance headway. The time-based headway required the follower train to reproduce the leader train’s operational status at the same track location. This also allowed the follower train to copy any optimized train driving strategies from the leader train. Demonstrative simulations were carried out without the consideration of communication errors and train localization errors. The results show that a conventional distance headway simulation had maximum distance and speed errors of 716 m (36%, reference 2 km) and 24 km/h (66%, reference 36 km/h), respectively. A time-based headway simulation reduced the maximum distance and speed errors to 0.07 m (0%, reference 2 km) and 0.1 km/h (9%, reference 1.18 km/h), respectively

Physical coupling and decoupling of railway trains at cruising speeds: Train control and dynamics

This paper studied an emerging technology called Dynamic Coupling that to allow physical coupling and decoupling of railway trains at cruising speeds. A train controller and a train model were introduced and simulated using Parallel Computing. Two transitional gap references were designed (hyperbolic tangent and exponential). Two six-vehicle passenger trains coupling and decoupling on a revised real-world track section at 80 km/h were simulated. Results indicated that a certain negative (e.g. -5 mm) Reference Gap at Coupling Instant (RG@CI) and a positive (e.g. 2 mm) Reference Gap at Decoupling Instant (RG@DI) were recommended to ensure a quick coupling process and to avoid decoupling impacts. Comparatively, the exponential case was better to reduce infrastructure upgrade requirements and to achieve better comfort. The simulated train movements well matched the designed gap references; Dynamic Coupling was possible in terms of train dynamics in the simulated cases

Tropical methane emissions explain large fraction of recent changes in global atmospheric methane growth rate

Large variations in the growth of atmospheric methane, a prominent greenhouse gas, are driven by a diverse range of anthropogenic and natural emissions and by loss from oxidation by the hydroxyl radical. We used a decade-long dataset (2010–2019) of satellite observations of methane to show that tropical terrestrial emissions explain more than 80% of the observed changes in the global atmospheric methane growth rate over this period. Using correlative meteorological analyses, we show strong seasonal correlations (r = 0.6–0.8) between large-scale changes in sea surface temperature over the tropical oceans and regional variations in methane emissions (via changes in rainfall and temperature) over tropical South America and tropical Africa. Existing predictive skill for sea surface temperature variations could therefore be used to help forecast variations in global atmospheric methane

UFLUX-GPP: A cost-effective framework for quantifying daily terrestrial ecosystem carbon uptake using satellite data

In light of climate change, scaling up in situ eddy covariance (EC) fluxes with Earth observation data has been recognized as a viable strategy for estimating the global terrestrial ecosystem carbon uptake, specifically, gross primary productivity (GPP). Nevertheless, the significant uncertainty in estimation (100–150 PgCyr-1) necessitates the refinement of upscaling algorithms and the use of appropriate satellite data. This technological advancement is particularly sought after in underprivileged regions that are most susceptible to climate crises. Unfortunately, these regions are often constrained by insufficient financial resources and software engineering skills shortages. This study aims to evaluate satellite vegetation proxies [solar-induced fluorescence (SIF); near-infrared reflectance of vegetation (NIRv)] for upscaling GPP and to propose a cost-effective GPP estimation framework called unified FLUXes-GPP (UFLUX-GPP), which can be conveniently operated on a laptop while delivering outstanding performance. The results demonstrated that moderate resolution imaging spectroradiometer (MODIS) NIRv and OCO-2 CSIF exhibited superior performance in the upscaling of EC GPP, with a coefficient of determination ( R2 ) of 0.86 and a root mean square error (RMSE) of 1.55 gCm-2d-1. The integration of multiple satellite-derived vegetation proxies holds the potential to enhance the reliability of the model ( R2=0.89 , RMSE =1.41 gCm-2d-1) with an uncertainty of 8 PgCyr-1, especially in tropical and polar regions. The UFLUX-GPP effectively preserved the ecological responses of GPP to the environment and showed promising potential for predicting future GPP. Although the spatiotemporal density of EC towers may occasionally impede the upscaling performance, UFLUX-GPP can convincingly advance a broader use of satellite remote sensing for GPP estimation