Dynamic Control Model Research Articles

Subway Multi-Station Coordinated Dynamic Control Method Considering Transfer Inbound Passenger Flow

The prominent contradiction between passenger demand and capacity in rush hours at subway stations causes inconveniences to travel and even leads to safety risks. Existing research on the cooperative control of passenger flow at stations mostly focuses on a single direction, rarely considering transfer passenger flow control. This study formulated a coordinated dynamic control strategy for multiple stations in both directions as a deterministic mathematical programming model to optimise the crowded passenger flow. The optimisation objectives were set as the warning levels of crowded passenger flow and the detention time of all passengers. The constraints included limitations on station service capacity, train capacity, and the number of people boarding trains. Additionally, considering separate control over the transfer inbound passenger flow at transfer stations, an upward- and downward-direction coordinated dynamic control model was constructed. Numerical experiments based on real-world data from the Nanjing Metro Line 1 were conducted to investigate the effectiveness of the proposed cooperative control scheme and evaluate its performance.

A Dynamic Control Model of Basic Oxygen Furnace Last Blowing Stage Based on Improved Conditional Generative Adversarial Network

A Dynamic Control Model of Basic Oxygen Furnace Last Blowing Stage Based on Improved Conditional Generative Adversarial Network

A Dynamic Model of Authoritarian Social Control

Abstract Authoritarian regimes often use targeted social control - unequal application of the law to limit expressive freedom and enforce social conformity. At the same time, their methods appear less draconian than in the past. In this model, an authority structures punishments and rewards to compel adherence to its preferred norm. The authority’s commitment is time-limited and depends on imperfectly informative signals of a citizen’ s behavior. Given two citizens with the same observed behavior, the authority imposes harsher punishments on the poorer and/or ex ante dissident individual. Lighter punishments are imposed on the wealthier citizen to prevent “overcompliance.” Wealth inequality increases over time. Some citizens become prosperous “lackeys” while others become destitute from confiscation. In stable regimes with high state capacity, the authority reduces punishments and/or increases rewards to allow citizens to accumulate wealth, leading to social conformity and balanced growth in the long run. In unstable regimes with low capacity, the citizenry splits into groups of wealthy lackeys and destitute proles.

Category-Based Administrative Access Control Policies

As systems evolve, security administrators need to review and update access control policies. Such updates must be carefully controlled due to the risks associated with erroneous or malicious policy changes. We propose a category-based access control (CBAC) model, called Admin-CBAC , to control administrative actions. Since most of the access control models in use nowadays (including the popular RBAC and ABAC models) are instances of CBAC, from Admin-CBAC , we derive administrative models for RBAC and ABAC, too. We present a graph-based representation of Admin-CBAC policies and a formal operational semantics for administrative actions via graph rewriting. We also discuss implementations of Admin-CBAC exploiting the graph-based representation. Using the formal semantics, we show how properties (such as safety, liveness, and effectiveness of policies) and constraints (such as separation of duties) can be checked, and discuss the impact of policy changes. Although the most interesting properties of policies are generally undecidable in dynamic access control models, we identify particular cases where reachability properties are decidable and can be checked using our operational semantics, generalising previous results for RBAC and ABAC α .

ANALYSIS OF SPARE PART INVENTORY CONTROL USING ECONOMIC ORDER QUANTITY (EOQ) AND CONTINUOUS REVIEW METHODS

General Background: In the concrete manufacturing industry, the reliance on machines for production activities necessitates a robust spare parts inventory management system to ensure operational continuity. Specific Background: However, fluctuating demand for spare parts often leads to overstock or stockout situations, significantly impacting inventory costs and cash flow. Knowledge Gap: Existing studies primarily focus on static inventory management approaches, neglecting the dynamic nature of spare parts demand in manufacturing environments. Aims: This research aims to optimize total inventory costs while determining efficient order and reorder point quantities by integrating the Economic Order Quantity (EOQ) method with continuous review techniques. Results: The findings reveal an optimal order quantity of 131 units and a reorder point of 18 units, resulting in a total inventory cost of IDR 2,414,609,989—an efficiency improvement of IDR 293,152,400, equivalent to an 11% cost saving compared to previous inventory management practices. Novelty: This study innovatively employs a probabilistic approach to account for demand variability, enhancing the accuracy of inventory control measures. Implications: The outcomes suggest that implementing the proposed inventory management strategy can mitigate the risks of overstocking and stockouts, ultimately fostering improved financial performance in the company. Furthermore, the research highlights the necessity for regular inventory reviews and suggests future studies to develop more dynamic inventory control models that incorporate price fluctuations for spare parts, thereby addressing potential risks associated with cost variability.

Koopman modeling for optimal control of the perimeter of multi-region urban traffic networks

The purpose of perimeter control is to regulate the transfer flow between the perimeters of the urban traffic network, so that the vehicle aggregation in each region is maintained at a desired level. It is regarded as one of the most important methods to solve traffic congestion in urban road networks. Accurate mathematical modeling of perimeter controlled traffic dynamics remains a challenge due to its high nonlinearity and uncertainty. Machine learning methods have been used to learn traffic dynamic models for perimeter control. However, these existing techniques lack generalization and interpretability. In this paper, we propose a Koopman modeling approach (i.e., a two-stage method) that uses a new eigenfunction of the Koopman operator based on a novel L0 norm approximation to construct an interpretable finite-dimensional approximation of the Koopman operator, which is a linear operator that describes how eigenfunctions evolve along the trajectory of a specific nonlinear dynamical system. The main advantage of utilizing the Koopman operator is that it can characterize the nonlinear system in a global linear lifted feature space. Furthermore, an algorithm based on the Koopman modeling method and model predictive control is proposed for real-time perimeter control of urban road networks. Some experiments are carried out to demonstrate the effectiveness of the proposed algorithm.

Towards various occupants with different thermal comfort requirements: A deep reinforcement learning approach combined with a dynamic PMV model for HVAC control in buildings

Reinforcement learning (RL) has great potential in achieving energy-efficient, comfortable and intelligent control of heating, ventilation and air conditioning (HVAC) systems. Although research on RL-based HVAC control has attracted increasing interest, current studies generally use simple building simulation as the environment to train agents, and the definition of thermal comfort is limited to a wide temperature range, which cannot meet the different thermal comfort requirements of various occupants. This study proposes a deep reinforcement learning (DRL) control framework based on the Dueling Deep Q-network (DQN) algorithm, combined with a self-designed environmental model and reward function, for HVAC control meeting different thermal comfort requirements. Specifically, based on the theory of building thermal dynamics, a nonlinear equation modified by experimental data is used for the environmental model that reflects the actual thermal change of building. Different thermal comfort requirements are considered and analysed through a dynamic predicted mean vote (PMV) model that focuses on the metabolic rate and clothing level of occupants. By systematically exploring different heating modes for occupants and control time intervals, the proposed framework demonstrates that heating energy consumption can be reduced by 4.8%-39.58% under various conditions compared to rule-based control. In addition, the study found that the HVAC control based on DRL has greater potential in saving energy when the heating demand of building is higher. Our study is helpful for researchers to make HVAC control more energy-efficient and user-friendly with the help of artificial intelligence.

Dynamic control of low-carbon efforts and process innovation considering knowledge accumulation under dual-carbon policies

Governments frequently implement carbon trading (CT) and carbon subsidy (CS) policies for encouraging enterprises to engage in low-carbon efforts (LCE) and reduce emissions, ultimately aiming for sustainable development in environmental and economic realms. In response, enterprises increase their investments in process innovation (PI) to balance revenue generation and cost reduction while simultaneously accumulating knowledge. Exploring the relation between LCE and PI is crucial to guide enterprises in effectively balancing these inputs, as well as to examine the role of government regulations on carbon emissions in motivating enterprises to enhance their efforts. This study proposes a dynamic optimal control model integrating PI and LCE within the dual-carbon policy framework, considering the effect of knowledge accumulation (KA). Moreover, it evaluates changes in inputs and benefits under profit-optimal and social welfare-optimal conditions. Finally, a comparative analysis is conducted using numerical simulation and emulation. The findings indicate complementary and substitution effects between the two inputs: the KA effect enhances input stability, and the impact of social incentives consistently outweighs that of monopoly incentives. Furthermore, CT and CS policies exhibit cross-impacts on enterprise returns and the two inputs.

Enhancing Subway Efficiency on Y-Shaped Lines: A Dynamic Scheduling Model for Virtual Coupling Train Control

Enhancing Subway Efficiency on Y-Shaped Lines: A Dynamic Scheduling Model for Virtual Coupling Train Control

Bilevel access control and constraint‐aware response provisioning in edge‐enabled software defined network‐internet of things network using the safeguard authentication dynamic access control model

Bilevel access control and constraint‐aware response provisioning in edge‐enabled software defined network‐internet of things network using the <scp>s</scp>afeguard authentication dynamic access control model

A novel dynamic predictive control model for variable air volume air conditioning systems using deep learning

Model-based predictive control has proven effective for managing indoor temperature and humidity in variable air volume (VAV) air conditioning systems. However, delays in temperature and humidity adjustments in response to changes in internal and external environmental conditions can impede high-performance operation. This study introduces a dynamic predictive control model for the multi-zone indoor temperature and humidity of VAV systems, designed to predict and control these parameters in real-time. Utilizing the RC network two-node wall structure method and backward finite difference scheme, a multi-zone building model is developed. Coupled with a deep fuzzy cognitive map (DFCM) for temperature and humidity prediction, and controlled by a PID controller, the model dynamically regulates the opening degrees of terminal air dampers and chilled water valves. Implemented on a networked control platform in a large commercial building, the model consistently maintained indoor temperature and humidity within ±0.5 °C and ±0.1 g/(kg dry air) of the target values under varying conditions, demonstrating substantial improvements in stability and operational efficiency. The research enhances the predictability and control of HVAC systems through advanced algorithmic integration, improving real-time indoor climate management in complex building environments. This method offers a practical improvement in HVAC control technologies, which could be beneficial for applications in commercial building management systems, providing a useful tool for supporting energy efficiency and sustainability in modern infrastructures.

Dynamic control method of construction cost based on fuzzy neural network

For improving the dynamic management performance of construction cost, the dynamic control model of cost of construction based on fuzzy neural network is studied. Consider the effect of resource allocation, construction progress and construction quality on the cost of project in construction stage, and build the cost control index for the project in stage of construction. The fuzzy logic model is used in selecting the main cost related indicators from project cost control index system at the construction stage as input neurons of the BP NN, and the BP neural network is employed to output the results of prediction of cost at the construction stage. The project cost prediction results are set as the data basis for the dynamic control of the project cost, determine the most optimistic cost, the most probable cost and the most pessimistic cost of project cost in stage of construction through key chain method, and set buffers to realize dynamic control of project cost in stages of construction. The evaluation results prove that the developed method will be able to accurately predict the project cost in stages of construction, and the cost saved in the construction phase of the project is more than 300000 yuan.

Control-oriented dynamic modeling and GPC for single-tower double-circulation wet flue gas desulfurization system

In China, the current situation of large-scale grid integration of renewable energy generation and the government's introduction of capacity-based electricity pricing policies have compelled coal-fired power plants to gradually enhance their load adjustment capabilities and expand their load adjustment ranges. Simultaneously, the national environmental requirements for these plants have become increasingly stringent. However, the economic control of wet flue gas desulfurization (WFGD) systems is still far from meeting practical requirements. In this paper, starting from practical engineering applications and utilizing historical operational data, considering the actual system operation process, the impact of the absorption tower variable-frequency slurry circulation pump on the concentration of sulfur dioxide (SO2) in flue gas under different slurry pH values is investigated. This led to the establishment of a dynamic model for a single-tower double-circulation wet flue gas desulfurization (SD-WFGD) system. Recognizing the significant delay and inertia characteristics of pH value control in the SO2 absorption tank, a desulfurization system pH value desulfurization capacity-based optimization control scheme for the SD-WFGD system is proposed, termed as the Direct Sulfur Control (DSC) strategy. Based on the established dynamic model, a SD-WFGD system series control system is developed using a step generalized predictive control algorithm (GPC) combined with the DSC strategy. Validation results indicate that, compared to SGPC control schemes and traditional PID control schemes, this approach reduces fluctuations in slurry supply and outlet SO2 concentration, significantly enhancing the control performance of the SD-WFGD system. This ensures the safe, stable, economical, and flexible operation of the desulfurization system against the backdrop of flexible operation in coal-fired power plants.

Vibration control of membrane structures by piezoelectric actuators considering piezoelectric nonlinearity under strong electric fields

Membrane structures, which are widely used in aerospace and civil engineering, are susceptible to large amplitude vibrations due to their low structural damping and high flexibility, leading to the degradation of their working performance. Strong electric fields are commonly needed for piezoelectric actuators to control larger amplitude vibration of membrane structures. However, the effect of piezoelectric nonlinearity due to strong electric fields on the control performance of membrane structures is still underexplored. Given the importance of piezoelectric nonlinearity due to strong electric fields, the present study aims to investigate the vibration control performance of piezoelectric actuators for membrane structures. First, based on the piezoelectric nonlinear constitutive equation, the actuation equation of the piezoelectric actuator is generated. Then, following D′Alembert's principle, a nonlinear dynamic control model of membrane structure containing actuation force is derived. Finally, active vibration control based on velocity feedback algorithms is realized. The results show that the predicted strain without considering the piezoelectric nonlinearity is 80 % smaller than the finite element structure at 500 V. Meanwhile, the errors of Root Mean Squared (RMS) control displacement without considering the piezoelectric nonlinearity are more than 24 % when vibration amplitudes achieve 10 % of the membrane thickness. The developed model provides theoretical implications for active vibration control of flexible structures with piezoelectric actuators in strong electric field environments.

Research on control strategy of PEMFC air supply system for power and efficiency improvement

Proton exchange membrane fuel cell (PEMFC) is considered as a type of clean and efficient energy device. Fuel cell air supply system is a key component to maintaining the system output efficiency. In this paper, a dynamic control model for PEMFC system oxygen excess ratio (OER) management is built, and an OER control strategy is designed based on Fractional-Order PID. An OER control index mapping the relationship between the OER and the optimal net power under different load currents is proposed to improve the output performance of the system. The results show that Fractional-Order PID can achieve high accuracy and fast response real-time OER regulation under the step current in comparison to the traditional PID and Fuzzy PID control. Furthermore, under the dynamic load current of vehicle, the average relative error of OER is 2.81 % under optimal OER control based on Fractional-Order PID and the power generation efficiency of the PEMFC system can be increased to 52.55 %.

Recursive dynamic modeling for attitude control of fully flexible spacecraft with central body and appendages

A spacecraft composed of a central body and flexible appendages is a typical rigid-flexible coupling system. Current modeling methods generally regard the central body as rigid. As the central body becomes larger and more flexible, the elastic deformation of the central body may not be ignored. In this study, considering the flexibility effect of the central body and based on the recursive kinematics, a complete coupled dynamic model of the orbit, attitude, and elastic deformation of a fully flexible spacecraft is established. Compared with traditional methods, the model has smaller generalized coordinate dimension and can more clearly reveal the kinematic relationship between the appendages and the central body. By comparing the simulation results of typical working conditions with the ADAMS software, the correctness of the built model is verified. The PD controller for the attitude of the spacecraft is used, and three models of different rigid/flexible settings are compared in detail. The dynamic response of the system in uncontrolled and controlled states are discussed. Numerical simulation results show that the flexibility of the central body poses a certain influence on the attitude response of the spacecraft, the flexible vibration of the appendages, and the attitude control accuracy.

The organization dilemma: investing in effectiveness versus efficiency?

PurposeEvery business company deals with the dilemma of how much to invest in long-term (LT) versus short-term (ST) problem (LTvST problem). LT operations increase the reputation of the company, and revenue is rewarded in the future. In contrast, ST operations result in immediate rewards. Thus, every organization faces the dilemma of how much to invest in LT versus ST activities. The former deals with the “what” or effectiveness, and the latter deals with the “how” or efficiency. The role of managers is to solve this dilemma; however, they often fail to do so, mainly because of a lack of knowledge. This study aims to propose a dynamic optimal control model that formulates and solves the LTvST problem.Design/methodology/approachThis study proposes a dynamic optimal control model that formulates and solves the dilemma whether to invest in short- or LT operations.FindingsThis model is illustrated as an example of an academic institute that wants to maximize its reputation. Investing in effectiveness in the academy translates into investing in research, whereas investing in efficiency translates into investing in teaching. Universities and colleges with a good reputation attract stronger candidates and benefit from higher tuition fees. Steady-state conditions and insightful observations were obtained by studying the optimal solution and performing a sensitivity analysis.Originality/valueTo the best of the authors’ knowledge, this paper is the first one to explore the optimal strategy when trying to maximize the short and LT activities of a company and solve the LTvST problem. Furthermore, it is applied on universities where teaching is the ST activity and research the LT activity. The insights gleaned from the application are relevant to many different fields. The authors believe that the paper makes a significant contribution to academic literature and to business managers.

Research on Inventory Control Method Based on Demand Response of Power Big Data

The supply chain functions as a complex web, interconnecting various stakeholders such as suppliers, manufacturers, wholesalers, retailers, and ultimately, end consumers. Central to effective supply chain management is the meticulous handling of inventory, a critical factor influencing both cost efficiency and service excellence throughout the entire network. The management of inventory within this context extends beyond the confines of individual enterprises, bearing significance across the entirety of the supply chain. Consequently, achieving optimal performance necessitates a cohesive, holistic approach to management, aligning with the overarching objectives of the system. Through dynamic data analysis of multiple types of power materials, a dynamic inventory control model for power materials is constructed to achieve optimal adjustment of inventory management. Ultimately, a multi-granularity inventory control method based on big data analysis of power warehousing is constructed, which effectively improves inventory management efficiency and reduces logistics management costs for enterprises. Through big data analysis of power material warehousing, the characteristics of power material demand are excavated, and a classification method for power material demand is constructed to achieve an overall inventory control strategy for power materials. The implementation results show that the controlled inventory can better meet the changing demand, thereby improving inventory management efficiency. The multi-granularity inventory control method based on big data mining of warehousing combines inventory and multi-objective optimization theories, proves the applicability and feasibility of the proposed method, effectively improves inventory management efficiency, reduces logistics management costs for enterprises, and provides practical guidance and decision-making reference for improving the intensive management level of power production and maintenance materials.

Developing Dynamic System Responses Model Based on Nanotechnology by Incorporating the Finite Element Analysis

The dynamic model of complex structure has set incredible progression with the use of finite element analysis (FEA), especially in the field of vibration control and nanotechnology. Main focus of this research is to conduct a frequency analysis in ANSYS and a state space model of the beam under the platform of MATLAB. This allowed a system to develop an active dynamic model for vibration control. To enhance test and analysis of the cantilever beam of Aluminium 6061T6, a new method has been proposed to predict the dynamic response of a cantilever beam under sinusoidal base excitation. This was achieved by creating an analytical model for the cantilever beam using moment and force equilibrium equations. The authenticity of the proposed method was made by comparing the results with experimental data. Additionally, to control vibration, a Proportional-Integral-Derivative (PID) controller was developed using the system model. The FEA in the ANSYS platform provided a cantilever beam model. Also, its mathematical modelling has been done. The proposed method utilizes a novel disturbance rejection control scheme that eliminates an unknown disturbance. Experimental results indicate that the control system results in gradually decreased beam vibration amplitude. The state space approach discussed in the work could be a valuable tool for studying the behaviours of nanomaterials.

Influence of Ductility on the Performance of Lunar Habitat Structures Under Recurrent Disturbances

This research examines how ductility affects the durability of lunar surface structures against recurring disturbances like moonquakes, micrometeorite impacts, and thermal cycles over an extended period. The structural performance at various levels of ductility was determined by adjusting material parameters and the thickness of a reference multilayered dome structure. Moonquake and micrometeorite impact-induced lateral displacements were estimated using a reduced-order model under a control-oriented dynamic computational modeling framework. The study considered the degradation of the metallic dome’s strength properties over time due to thermal cycles. Fragility curves were generated by assessing the likelihood of reaching three predefined damage levels as a result of multiple hazards. Additionally, a discounted cash flow analysis was conducted to incorporate a financial aspect into the performance comparison. The findings revealed that structures with sufficient ductility capacity have a lower probability of sustaining severe damage or collapsing within a shorter time frame. Hence, having ductile structures in lunar environments is advantageous as it allows the postponement of maintenance and repair actions, thereby conserving scarce resources for more urgent tasks. Moreover, the financial analysis demonstrated that lunar habitats with higher ductile capacities result in larger net present values, offering a higher return on the initial investment.