System Dynamics Research Articles

A compensation method for PMSM sensorless control with parameter identification considering SMO observation error

AbstractSensorless control of permanent magnet synchronous motor (PMSM) can increase the reliability of electric actuators of more electrical aircraft. Numerical online parameter estimation method will enhance the performance for sensorless control methods based on sliding mode observer (SMO). However, numerical online parameter identification methods typically assume zero rotor position estimation error to ensure full rank of the identification equation set. This assumption actually leads to errors in parameter identification, resulting in steady‐state errors in rotor position estimation. This paper focuses on a compensation method for PMSM online parameter identification that takes into account rotor position estimation error. This method is used for sensorless control based on SMO, using model reference adaptive systems for online parameter identification, and no additional parameter adjustment required. Validation is conducted on a PMSM experimental platform with maximum load of 15kW. The experimental results of 1000 r/min steady state operation proved the effectiveness of the method, and the dynamic experiments with 5 and 15kW loads proved that the method has no significant adverse effect on the system dynamics.

Hopf bifurcation and patterns formation in a diffusive two prey-one predator system with fear in preys and help

Recognizing the relationship between the spatial patterns in species concentrations and ecological heterogeneity is crucial for understanding demographics and species governance in a given domain, as ecological patterning processes are believed to be imitated in real ecosystems. In this present article, we have considered a two-prey-one-predator system with Holling type-II functional response where both the preys have fear of predator and compete in the absence of predator. Additionally, we have assumed that both the prey help each other when the predator attacks them. We have incorporated self-diffusion in this system under Neumann boundary conditions. The existence and stability conditions of different equilibria are discussed. We have studied the Hopf bifurcation around positive offset and derived the stability and direction of periodic solution. We have done a series of numerical simulations to ensure our theoretical finding. Diffusion-driven instability conditions are obtained. Different instability regions and fascinating patterns, such as spots, mixtures, and stripes, are depicted. Spatiotemporal dynamics also reflects that prey populations are located in isolated areas of low population concentration with increasing level of fear, whereas an increase in handling time forms lower-density stripes. But with increasing help, prey becomes more concentrated in some stripe regions. In the Hopf-Turing region, it is observed that the diffusion coefficient of predators can stabilize the system. Overall, pattern creation in predator-prey systems can help anticipate long-term system behaviour and analyze the influence of ecological factors on system dynamics.

Mathematical Modeling of Immune Dynamics in Chronic Myeloid Leukemia Therapy: Unraveling Allergic Reactions and T Cell Subset Modulation by Imatinib

This mathematical model delves into the dynamics of the immune system during Chronic Myeloid Leukemia (CML) therapy with imatinib. The focus lies in elucidating the allergic reactions induced by imatinib, specifically its impact on T helper (Th) cells and Treg cells. The model integrates cellular interactions, drug pharmacokinetics, and immune responses to unveil the mechanisms underlying the dominance of Th2 over Th1 and Treg cells, leading to allergic manifestations. Through a system of coupled delay differential equations, the interplay between healthy and leukemic cells, the influence of imatinib on T cell dynamics, and the emergence of allergic reactions during CML therapy are explored.

Complex Dynamics and Bifurcations Analysis of Discrete-Time Modified Leslie–Gower System

This work introduces a discrete modified Leslie–Gower prey–predator system with Holling type-II functional response. The persistence of the discrete model under certain conditions is discussed. The conditions assuring the existence of fixed points are derived and nonlinear dynamics of system are explored at these fixed points. It has been shown that the system exhibits transcritical bifurcation and flip bifurcation at semi-trivial fixed point under certain bifurcation values. In addition, the center manifold and bifurcation theories are employed to attain the conditions for existence of flip and Neimark–Sacker bifurcations at coexistence fixed point. The system is found to exhibit periodic solutions along with bifurcations leading to wide range of chaotic dynamics. The numerical simulations are performed to confirm the analytical analysis.

Interpretable and efficient RUL prediction of turbofan engines using EM-enhanced Bi-LSTM with TCN and attention mechanism

Remaining useful life (RUL) prediction for turbofan engines is important in prognostics and health management (PHM) for the maintenance and operation of critical equipment. With continuous innovations in deep learning techniques, the complexity of models continues to increase, but the interpretability and comprehensibility of the prediction results become particularly important in industrial applications. Therefore, in this study, an improved bidirectional long and short-term memory network (Bi-LSTM) based interpretable hybrid deep learning model for RUL prediction of turbofan engines is proposed, which ingeniously integrates time series convolutional networks (TCNs), expectation maximization (EM), Bi-LSTMs, and attention mechanisms. By capturing time-series features at different levels, the model adapts to the complex dynamics of turbofan engine performance evolution in an efficient and cost-effective manner. Experimental validation on the C-MAPSS dataset demonstrated that the model significantly outperforms other methods in terms of RUL prediction performance, especially in improving prediction accuracy and coping with the degradation of complex system dynamics. The largest contribution of key metrics to the model is validated through consistent results from multiple interpretable tools, providing comprehensive and consistent support for understanding and trusting prediction results in industrial applications. This study further enhances the robustness of the model and the reliability of the interpretable results by delving into the dynamic relationships between the properties of the different life stages, which not only reveal the importance of these characteristics in engine life prediction but also provide more comprehensive information about the engine performance variations by observing the dynamic relationships.

Unknown system dynamics estimator based control for sub‐synchronous oscillation of direct‐drive wind farms

Unknown system dynamics estimator based control for sub‐synchronous oscillation of direct‐drive wind farms

Forecasting urban passenger transportation emissions: a green and new energy approach

ABSTRACT Passenger transportation significantly contributes to urban carbon dioxide emission. Decreasing these emissions is essential to achieving low-carbon development. This research focused on Xi’an City to enhance previous research works by considering factors such as road greening, online ride-hailing, and use of vehicles with new energy sources. Using system dynamics method, a carbon dioxide emission prediction model was developed for urban passenger transportation in Xi’an. The developed model estimated total emissions from 2012 to 2021 and projected carbon dioxide emission reduction plans through six different scenarios for the time period of 2023 to 2032. The analysis identified private cars as the main contributors to carbon emission in urban passenger transportation. Single scenario simulations indicated that passenger traffic in Xi’an would not achieve carbon emission peak by 2030, but multi-scenario simulations showed it would. Specific recommendations for carbon emission reduction in urban passenger transportation were provided, focusing on travel patterns, energy sources, and promotion of green initiatives.

Using system dynamics to support a functional exercise for pandemic preparedness and response

AbstractIn pandemic preparedness and response, a Functional Exercise (FX) is used to simulate a situation as close to a real‐life event as possible without the deployment of resources. Participants are drawn from public health emergency operations centres, and work through a scenario script to test possible responses to a novel pathogen outbreak. This paper summarises the role of system dynamics modelling in the design and implementation of a functional exercise, which involved the Dutch and German national public health institutes in March 2023. The findings confirm the value of the system dynamics method in integrating disease and hospital models, and also highlights how well the method aligns with modern software development processes. The paper concludes with a discussion of what worked well, and presents areas for future enhancements of management flight simulators to support functional exercises. © 2024 The Author(s). System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society.

Frequency switching leads to distinctive fast–slow behaviors in Duffing system

The slowly forced Duffing system has been found to exhibit unique fast–slow behaviors in descending frequency switching scheme, the typical of which is the sliding bursting, which is instructive for understanding the dynamics of ubiquitous frequency switching systems in scientific research and practical engineering. This paper is devoted to further refining the fast–slow dynamics of the slowly forced Duffing system with another commonly encountered frequency switching scheme, i.e., the ascending frequency switching, characterized by switching the frequency synchronously according to the increase or decrease of state variable values. Taking the forcing amplitude as an example, this paper provides a theoretical way to fully summarize the fast–slow behaviors in frequency switching systems with the variation of parameter values based on the proposed superposition analysis of one- and two-parameter bifurcations of subsystems. As a result, two typical bifurcation structures and eight threshold windows with distinct switching vector fields therein are identified, inducing up to ten different bursting patterns. Among them, several novel fast–slow dynamics, such as multiple jumps hysteresis loop formed by boundary equilibrium bifurcations and the switching failure phenomena, are presented and investigated. In particular, the underlying mechanism of threshold modulation, i.e., the evolution of unconventional bifurcations, is also found. These findings contribute to complementing and contrasting the existing studies on the fast–slow dynamics of frequency switching systems.

Adaptive second-order backstepping control for a class of 2DoF underactuated systems with input saturation and uncertain disturbances

An adaptive second-order backstepping control algorithm is proposed for a kind of two degrees of freedom (2DoF) underactuated systems. The system dynamics is transformed into a nonlinear feedback cascade system with an improved global change of coordinates. Fully taking the cascade structure into consideration and in order to simplify the design process, each step in the backstepping process is designed for a second-order subsystem. Two neural networks are applied to approximate system unknown functions and two adaptive laws are designed to estimate the upper bound of the sum of approximation error and external disturbances. To overcome the explosion problem of complexity, a second-order filter is applied to produce the virtual control and its second-order derivative that is needed in the next backstepping step. Two auxiliary dynamic systems are proposed and integrated into the backstepping process to eliminate the effects of filtering error and input saturation. The system stability is analyzed by the Lyapunov stability theory and verified by numerical simulations with two 2DoF benchmark underactuated systems: the translational oscillator with a rotational actuator (TORA) and the inertial wheel pendulum (IWP).

A complete shortest independent loop set algorithm for model structure analysis

AbstractThis article looks at the well‐known shortest independent loop set algorithm, which is used to identify an independent loop set in system dynamics models. It addresses the cases where the algorithm fails to find a complete set due to its limitation to geodetic cycles and introduces a modification that has the potential to identify a complete independent loop set in every case while maintaining the efficiency of the original algorithm. The modified algorithm uses second‐shortest paths to complete the loop set. Special attention is given to the creation of the second‐shortest path matrix required for the modified algorithm, and to the application of the algorithm to a model where the independent loop set created by the shortest independent loop set algorithm was previously shown to be incomplete. © 2024 System Dynamics Society.

Adaptive second-order backstepping control for a class of 2DoF underactuated systems with input saturation and uncertain disturbances

An adaptive second-order backstepping control algorithm is proposed for a kind of two degrees of freedom (2DoF) underactuated systems. The system dynamics is transformed into a nonlinear feedback cascade system with an improved global change of coordinates. Fully taking the cascade structure into consideration and in order to simplify the design process, each step in the backstepping process is designed for a second-order subsystem. Two neural networks are applied to approximate system unknown functions and two adaptive laws are designed to estimate the upper bound of the sum of approximation error and external disturbances. To overcome the explosion problem of complexity, a second-order filter is applied to produce the virtual control and its second-order derivative that is needed in the next backstepping step. Two auxiliary dynamic systems are proposed and integrated into the backstepping process to eliminate the effects of filtering error and input saturation. The system stability is analyzed by the Lyapunov stability theory and verified by numerical simulations with two 2DoF benchmark underactuated systems: the translational oscillator with a rotational actuator (TORA) and the inertial wheel pendulum (IWP).

A complete shortest independent loop set algorithm for model structure analysis

AbstractThis article looks at the well‐known shortest independent loop set algorithm, which is used to identify an independent loop set in system dynamics models. It addresses the cases where the algorithm fails to find a complete set due to its limitation to geodetic cycles and introduces a modification that has the potential to identify a complete independent loop set in every case while maintaining the efficiency of the original algorithm. The modified algorithm uses second‐shortest paths to complete the loop set. Special attention is given to the creation of the second‐shortest path matrix required for the modified algorithm, and to the application of the algorithm to a model where the independent loop set created by the shortest independent loop set algorithm was previously shown to be incomplete. © 2024 System Dynamics Society.

Enhanced resilience in urban stormwater management through model predictive control and optimal layout schemes under extreme rainfall events

Optimizing the layout of urban stormwater management systems is an effective method for mitigating the risk of urban flooding under extreme storms. However, traditional approaches that consider only economic costs or annual runoff control rates cannot dynamically respond to the uncertainties of extreme weather, making it difficult to completely avoid large accumulations of water and flooding in a short period. This study proposes an integrated method combining system layout optimization and Model Predictive Control(MPC)to enhance the system's resilience and effectiveness in flood control. An optimization framework was initially built to identify optimal system layouts, balancing annual average life cycle cost (AALCC) and resilience index. The MPC was then applied to the optimal layout selected using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, aiming to alleviate inundation cost-effectively. The adaptability of MPC to varying sets of control horizons and its efficacy in managing the hydrograph and flood dynamics of urban drainage system were examined. Conducted in Yubei, Chongqing, this study revealed patterns in optimal layout fronts among various extreme design rainfalls, showing that peak position rate and return period significantly influence system resilience. The contribution of MPC to the optimal system layout was particularly notable, resulting in improved instantaneous and overall flood mitigation. The application of MPC increased the resilience index by an average of 0.0485 and offered cost savings of 0.0514 million yuan in AALCC. Besides, our findings highlighted the importance of selecting an optimal set of control horizons for MPC, which could reduce maximum flood depth from 0.43m to 0.19m and decrease conduit peak flow by up to 14% at a flood-prone downstream location.

Dynamic evaluation of distribution channels in a fresh food supply chain from a sustainability and resilience approach

Research on agrifood supply chains has grown in recent years; however, little attention has been given to the effect of the choice of distribution channel on the sustainable and resilient performance of the network. This research proposes a system dynamics model to evaluate different distribution channels for a fresh food supply chain in a developing country. Among the main results is that the use of food hubs in a channel distribution produces long-term environmental, social and resilience benefits for the entire network, while short supply chains are the most economically viable alternative.

Exploring the Nexus between Food Systems and the Global Syndemic among Children under Five Years of Age through the Complex Systems Approach

The intricate relationship between food systems and health outcomes, known as the food-nutrition-health nexus, intersects with environmental concerns. However, there’s still a literature gap in evaluating food systems alongside the global syndemic using the complex systems theory, especially concerning vulnerable populations like children. This research aimed to design a system dynamics model to advance a theoretical understanding of the connections between food systems and the global syndemic, particularly focusing on their impacts on children under five years of age. The framework was developed through a literature review and authors’ insights into the relationships between the food, health, and environmental components of the global syndemic among children. The conceptual model presented 17 factors, with 26 connections and 6 feedback loops, categorized into the following 5 groups: environmental, economic, school-related, family-related, and child-related. It delineated and elucidated mechanisms among the components of the global syndemic encompassing being overweight, suffering from undernutrition, and climate change. The findings unveiled potential interactions within food systems and health outcomes. Furthermore, the model integrated elements of the socio-ecological model by incorporating an external layer representing the environment and its natural resources. Consequently, the development of public policies and interventions should encompass environmental considerations to effectively tackle the complex challenges posed by the global syndemic.

Exploring the Nexus between Food Systems and the Global Syndemic among Children under Five Years of Age through the Complex Systems Approach.

The intricate relationship between food systems and health outcomes, known as the food-nutrition-health nexus, intersects with environmental concerns. However, there's still a literature gap in evaluating food systems alongside the global syndemic using the complex systems theory, especially concerning vulnerable populations like children. This research aimed to design a system dynamics model to advance a theoretical understanding of the connections between food systems and the global syndemic, particularly focusing on their impacts on children under five years of age. The framework was developed through a literature review and authors' insights into the relationships between the food, health, and environmental components of the global syndemic among children. The conceptual model presented 17 factors, with 26 connections and 6 feedback loops, categorized into the following 5 groups: environmental, economic, school-related, family-related, and child-related. It delineated and elucidated mechanisms among the components of the global syndemic encompassing being overweight, suffering from undernutrition, and climate change. The findings unveiled potential interactions within food systems and health outcomes. Furthermore, the model integrated elements of the socio-ecological model by incorporating an external layer representing the environment and its natural resources. Consequently, the development of public policies and interventions should encompass environmental considerations to effectively tackle the complex challenges posed by the global syndemic.

Comparative analysis of omnichannel and multichannel networks: a system dynamics approach

Comparative analysis of omnichannel and multichannel networks: a system dynamics approach

Modeling of impact of operations and maintenance on safety, availability, capacity, and cost of Railways-A System dynamics approach

the transport infrastructure, particularly the railway infrastructure plays a vital role in the delivery of freight and the transportation of people. The ability and reliability of the railway infrastructure to deliver goods and transport people are challenged by train derailments and collisions caused by infrastructure breakdowns. Lack of maintenance has been identified as one of the causes of infrastructure breakdowns leading to accidents. The current paper proposes that if the railway infrastructure safety, availability, capacity, and cost are modeled using system dynamics, the impact of infrastructure operation and maintenance on safety can be predicted more accurately. The paper follows systems thinking approach that aims to understand the railway infrastructure as a system, by defining the system structure, system component relationships, and system behavior. The impact on railway infrastructure is modeled using system dynamics by developing causal loop diagrams and stock and flow diagrams which define the system structure, and system component relationships, and models the system behavior of safety, availability, capacity, and cost.

Attraction by pairwise coherence explains the emergence of ideological sorting.

Political polarization has become a growing concern in democratic societies, as it drives tribal alignments and erodes civic deliberation among citizens. Given its prevalence across different countries, previous research has sought to understand under which conditions people tend to endorse extreme opinions. However, in polarized contexts, citizens not only adopt more extreme views but also become correlated across issues that are, a priori, seemingly unrelated. This phenomenon, known as "ideological sorting", has been receiving greater attention in recent years but the micro-level mechanisms underlying its emergence remain poorly understood. Here, we study the conditions under which a social dynamic system is expected to become ideologically sorted as a function of the mechanisms of interaction between its individuals. To this end, we developed and analyzed a multidimensional agent-based model that incorporates two mechanisms: homophily (where people tend to interact with those holding similar opinions) and pairwise-coherence favoritism (where people tend to interact with ingroups holding politically coherent opinions). We numerically integrated the model's master equations that perfectly describe the system's dynamics and found that ideological sorting only emerges in models that include pairwise-coherence favoritism. We then compared the model's outcomes with empirical data from 24,035 opinions across 67 topics and found that pairwise-coherence favoritism is significantly present in datasets that measure political attitudes but absent across topics not considered related to politics. Overall, this work combines theoretical approaches from system dynamics with model-based analyses of empirical data to uncover a potential mechanism underlying the pervasiveness of ideological sorting.