Dynamic Control Model Research Articles

Stability of Traffic Equilibria in a Day-to-Day Dynamic Model of Route Choice and Adaptive Signal Control

Adaptive traffic signal control has the potential to promote the efficient use of road intersections, thus contributing to the effectiveness of urban traffic management schemes. However, the reaction of drivers to repeatedly updated signal settings and the ensuing route choice dynamics may trigger the emergence of various kinds of network instability. In this study, the joint evolution of traffic flows and adaptive signal settings in a road network is investigated at the level of day-to-day dynamics with an explicit focus on the stability issue. We show how a Logit form signal control policy can be used, in interaction with route choice, to counter the emergence of instabilities possibly arising as a consequence of various behavioral factors and network conditions. After providing a general formulation of the model as a discrete time, deterministic nonlinear dynamical system, an explicit analysis of fixed-point stability is carried out for a simple network. Numerical results obtained from the implementation of the model on two example networks are presented in order to support the analytical findings of this study. We conclude that, in an integrated traffic management and information system, a properly calibrated adaptive signal control policy has the potential to offset the destabilizing effect of highly accurate driver information supplied by navigational aids. Our findings also suggest that the Logit-like control policy performs better than the Equisaturation signal setting method, in terms of average intersection delay at equilibrium, for all levels of driver information and travel demand tested in the experiment.

Method to obtain parameters k2 , k3 for dilution rate observer in AM2 model of the anaerobic digestion process in a batch reactor

ABSTRACT Anaerobic digestion (AD) is a process of biochemical decomposition by anaerobic bacteria. Organic solid waste, subjected to anaerobic digestion, produces organic fertilizer and biogas, composed of methane, carbon dioxide, and other gases. The AM2 model describes the AD system and is the most widely used model for dynamic behavior analysis and control of AD processes. One of the most critical variables for system analysis is the concentration of volatile fatty acids (VFA). The behavior of the concentration of VFA depends on the dilution rate. This, in turn, depends on the performance parameters of producing and consuming VFA (k2 and k3). This paper presents a deterministic method to calculate the k2 and k3 parameters, from the dilution rate dynamics and Olson method for indirect validation of the parameters based on the direct validation of the concentration of VFA. Model results for VFA concentration showed an error of <0.4%. An error of <1.98% was found between the theoretical model and the experimental data. Overall, a global error of <2.37% gives a reliability to the proposed method to determine k2 and k3.

A queueing model of dynamic pricing and dispatch control for ride-hailing systems incorporating travel times

A queueing model of dynamic pricing and dispatch control for ride-hailing systems incorporating travel times

Towards better IT governance: A comprehensive IT internal control model for information technology firms

Purpose- The primary objective of this study is to enhance IT governance within the information technology sector by developing a comprehensive IT Internal Control Model. This model is designed to address the unique and evolving challenges in IT internal control processes, providing a structured approach to risk optimization, resource optimization, and benefit management. By introducing this model, the study aims to offer IT firms a robust internal control model that can be adapted to their specific operational needs, promoting a more effective, efficient, and resilient IT governance structure. Methodology- A thorough literature review, incorporating a wide array of international sources, forms the foundation of this study. The research involved designing an IT Internal Control Model, informed by the processes observed at THY Technology. This model was then applied across 12 distinct operational processes, and feedback from stakeholders was systematically gathered through surveys to evaluate its practicality and impact. Findings- The feedback from stakeholders was predominantly positive, underscoring the model’s efficacy in bolstering IT governance. The model was praised for its thoroughness and adaptability, successfully addressing a broad spectrum of IT internal control issues while remaining flexible enough to be tailored to various IT environments. Conclusion- This study highlights the importance of dynamic and adaptable IT internal control model in the fast-paced technology sector. The developed IT Internal Control Model opens new avenues for research into effective governance practices and provides a valuable blueprint for IT firms seeking to enhance their governangfce and control mechanisms in a digitally-driven world. Keywords: IT internal control model, internal control, IT governance, IT compliance, continuous improvement JEL Codes: M15, M19, M42

Voltage control method for multi-energy system based on the coupling of renewable energy hydrogen production and storage

Renewable energy power generation combined with hydrogen storage, is an important way to realize the deep integration of hydrogen energy and renewable energy in multi-energy systems. In the multi-energy system of renewable energy power generation and hydrogen production in AC/DC hybrid. Due to the fluctuation of heat load and electric hydrogen production load in the system and the multi-time scale characteristics of energy flow transmission of heat storage and hydrogen storage. It will lead to the difficulty of power coordination in the whole Multi Energy System (MES). In order to solve the problem of power imbalance in MES, this paper first establishes the hybrid power flow equation of MES cluster composed of AC/DC and hydrogen energy transmission channels. The reactive voltage fluctuation characteristics of the AC/DC system under the fluctuation of energy exchange, system energy conversion, and renewable energy output of the MES cluster interconnection channel are analyzed. Secondly, a multi-energy flow model of the multi-energy system is established. The main factors affecting the voltage-reactive power of the AC system are analyzed. A reactive power model based on electro-thermal conversion, hydrogen storage, and multi-energy transmission channels is established. Then, a generation algorithm of the safe and stable boundary of pipeline pressure is proposed to maintain the pressure of hydrogen production and storage system within a safe range. The robust optimization control method is used to control the coupling of multi-energy sources. Finally, based on the actual operation data of the multi-energy power system. The simulation model of reactive power dynamic optimization control for multi-energy interconnected systems is established. The simulation results show that the control method proposed in this paper is adopted. Through the coordinated distribution of energy among multi-energy systems, the voltage stability level of the system can be effectively improved.

A Terramechanics-based Dynamic Model for Motion Control of Unmanned Tracked Vehicles

A Terramechanics-based Dynamic Model for Motion Control of Unmanned Tracked Vehicles

Ideonamic: An integrative computational dynamic model of ideomotor learning and effect-based action control.

According to ideomotor theory, actions are represented, controlled, and retrieved in terms of the perceptual effects that these actions experientially engender. When agents perform a motor action, they observe its subsequent perceptual effects and establish action-effect associations. When they want to achieve this effect at a later time, they use the action-effect associations to preactivate the action by internally activating the effect representation. Ideomotor theory has received extensive support in recent years. To capture this particular effect-based view on action control and goal-directed behavior, we developed IDEONAMIC, an integrative computational model based on dynamic field theory that represents the specific components of the action control process as dynamic neural fields. We show that IDEONAMIC applies conveniently to different types of experimental ideomotor settings, simulates key findings, generates novel predictions from the dynamics of data, and allows reapproaching the underlying cognitive mechanisms from a computational point of view. We encourage the application of IDEONAMIC to more types of ideomotor settings to gain insights into effect-based action control. The model is available at https://osf.io/hbc6n. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Game Theory-Inspired Motivation and Blockchain-Enhanced Agent Version Evolution Management and Case Study Design

The evolution of versions must be secure and traceable, particularly in asset-sharing systems managed by intricate agents, to bolster the adaptability and service potential of intelligent workshop collaborative frameworks. This article presents a conceptual framework that integrates a blockchain and data space-based hybrid version management model within a customized Industry 4.0 platform. It aims to enhance collaborative management of agent assets by leveraging low-code configuration and implementing incentives for confidentiality. (1) Design a BPMN-driven smart contract Swarm module for user-oriented visualization frameworks. The module is supported by an automated BPMN model and utilizes distributed blockchain technology to facilitate trusted dynamic interactions between multi-stakeholder and Agent version activities; (2) The core activity container framework integrates two-layer distributed container modules from IPFS and industrial data space. By designing interoperable containers based on private and shared versions, and deploying a trust-based dynamic access control model based on game theory, it implements an agent version activity trusted service based on reward and punishment mechanisms; (3) The paper conducted a test case demonstrating the integration of multiple open-source software to create an intelligent agent workshop service prototype built upon the AAS.

Dynamic safety management model for rail traffic control

In rail transport, increasing emphasis has been placed in recent years on improving safety levels. Therefore, more requirements and legal documents require risk analyses to be carried out at various stages of investment implementation. One of the leading legal documents that introduce the obligation to monitor risk is Directive (EU) 2016/798 of the European Parliament and of the Council of 11 May 2016 on railway safety and Commission Implementing Regulation (EU) No 402/2013 of 30 April 2013 on the common safety method for risk evaluation and assessment and re-pealing Regulation (EC) No 352/2009. Additionally, for traffic control systems, the requirements of CENELEC standards are mandatory. These documents present the subject of safety level and show its relation with the safety targets defined in the railway system, including the different ways of measuring them. Methods are also available to analyse the safety level of railway system components in detail, both at the level of individual components, subsystems, and the whole national railway system. However, after conducting an in-depth analysis of the literature, the authors of the article indicate that these methods are not consistent with each other. There is no method defined to present the direct relation of the safety level of the components of the system on the achievement of safety targets for the national railway system. The research and analysis aimed to define an approach, a method that would meet all legal requirements but at the same time would allow to clearly and reliably determine the safety level of the railway system. To define a unified approach, the authors of the article propose to develop a model of a dynamic object - a railway system safety model, which has also been verified on accurate safety data in rail transport in recent years. This model organises the process of safety management on railways and allows to determine values influencing the achievement of safety targets on an assumed level.

Reliable Cost Prediction and Control for Intelligent Manufacture: A Key Performance Indicator Perspective

Article Reliable Cost Prediction and Control for Intelligent Manufacture: A Key Performance Indicator Perspective Hang Geng 1,*, Alireza Mousavi 2, Nikolaos Grigorios Markatos 2, Kai Chen 1, and Xuan Gou 1 1 School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China 2 Department of Computer Science, Brunel University London, Uxbridge, Middlesex, UB8 3PH, United Kingdom * Correspondence: Hang.Geng@uestc.edu.cn Received: 10 July 2023 Accepted: 11 October 2023 Abstract: Intelligent manufacturing is facing significant challenges in adapting to the ever-changing equipment, instrumentation, process and economics. Such a trend together with the pressure to reliably control and contain production costs means that frequent adjusting decisions are required to adapt to incessant volatility imposed on manufacturing systems. Under this circumstance, cost-effective and quality-guaranteed manufacturing strategies would be the most logical route to reducing production costs. In this paper, a novel dynamical cost prediction and control (CPC) model is proposed to support collective decision-making in intelligent manufacturing, where the model output is the real-time prediction of possible manufacturing costs, while the inputs are generic manufacturing key performance indicators covering inventory, product quality, production efficiency, resource utilisation and environmental impact. This proposed CPC model distinguishes itself from existing ones for its capability to translate manufacturing data (at both the physical level and operation management level) into financial metrics that contribute to forming a common language between engineering, financial and administrative departments of an enterprise. The case study about the assembly line of optoelectronic devices demonstrates that, although different enterprise departments have different priorities, our CPC model helps them to achieve certain consensus on intended production that finally creates satisfactory profitability for the company at controlled manufacturing costs.

Dynamic PDC and HMM-based asynchronous output feedback strict dissipative control for nonlinear singular incomplete jumping systems with actuator saturation

This paper investigates the asynchronous output feedback strict dissipative control for nonlinear singular incomplete jumping systems with actuator saturation via dynamic parallel distributed compensation technique and hidden Markov model mechanism. Dynamic parallel distributed compensation technique is used to design the asynchronous fuzzy dynamic output feedback controller and hidden Markov model is employed to describe the asynchronous phenomenon between the singular Markov jump system modes and the dynamic output feedback controller modes. Novel conditions of stochastic admissibility and strict dissipativity for the fuzzy closed-loop singular Markov jump systems are derived based on singular value decomposition technique and improved L-K functional. The asynchronous dynamic output feedback controller gains are developed in terms of linear matrix inequalities and the minimization optimization scheme is proposed by established ellipsoid. Single-link robot arm simulation results illustrate the practicability and effectiveness of the proposed methods.

A two‐stage robust optimal traffic signal control with reversible lane for isolated intersections

AbstractThe integrated design of traffic signal control (TSC) and reversible lane (RL) is an effective way to solve the problem of tidal congestion with uncertainty at isolated intersections, because of its advantage in making full use of temporal‐spatial transportation facilities. Considering the contradiction between the dynamic TSC scheme and the fixed RL scheme in one period, a two‐stage optimization method based on improved mean‐standard deviation (MSD) model for isolated intersections with historical and real‐time uncertain traffic flow is proposed. In the first stage, applying the same‐period historical data of multiple days, a robust optimal traffic signal control model with reversible lane based on MSD model (MSD‐RTR model) is put forward to obtain the fixed RL scheme and the compatible initial TSC scheme. A double‐layer nested genetic algorithm (DN‐GA) is designed to solve this model. In the second stage, applying real‐time period data and multi‐day same‐period historical data, a robust optimal dynamic traffic signal control model based on MSD model (MSD‐RDT model) is put forward to obtain the dynamic TSC scheme. Three modes which reflect the different weights of historical period and real‐time period in this MSD‐RDT model are presented to improve the model stability, and a multi‐mode genetic algorithm (MM‐GA) is designed. Finally, a case study is presented to demonstrate the efficiency and applicability of the proposed models and algorithms.

Learning Latent Dynamics for Autonomous Shape Control of Deformable Object

In recent years, the methods of loading and transporting rigid objects have become more and more perfect. However, in the process of transportation, the shape control of deformable objects has attracted extensive attention because deformable objects have been widely used in intelligent tasks such as packing and sorting cables before transportation. Restricted by the super-degrees of freedom and nonlinear dynamic models of deformable objects, planning the action trajectories to control the shape of deformable objects is a challenging task. In this work, we use contrastive learning to solve the shape control problem of deformable objects. The method jointly optimizes the visual representation model and dynamic model of deformable objects, maps the target nonlinear state to linear latent space which avoids model inference for deformable objects in infinite-dimensional configuration spaces. Furthermore, to extract effective information in the latent space, we construct an encoder with a multi-branch topology to improve the representation ability of the model. Experimentally, we collect dynamic trajectory data for random shape control task involving cloth or rope in a simulated environment. Then we apply it to train the proposed offline method to obtain latent dynamic models for shape control of deformable objects. In comparison with other baseline methods, our proposed method achieves substantial performance improvements.

Study on dynamic control system of tunnel variable air volume ventilation based on first-order delay and predictive compensation control model

Using a variable air volume ventilation system is an efficient way to save energy in the tunnel. To address the issues of low control parameter accuracy and high operating energy consumption, the transient flow field and ventilation resistance characteristics were analyzed using conservation equations, and the first-order delay mathematical model of tunnel ventilation control was established by taking tunnel and jet fan parameters into account. The airflow dynamic control model's related parameters and transfer function were also solved. The overshoot, steady value, and adjustment time of the control system were evaluated using simulation and experiment. In full-scale experiments, the required wind speed was converted into the frequency of the jet fans to achieve variable air volume ventilation control. The results show that the variable air volume ventilation control system has the characteristics of nonlinear, and the first-order delay time transfer function is related to the 1/2-th power of ventilation resistance and pressure. The control system performs well, and the response parameters are all within the error range. Moreover, the discussion results of the comparative tests also illustrated that the purpose of dynamic wind speed adjustment could be achieved through frequency conversion control.

Trajectory optimization method based on ellipse model for dynamic motion control of piezoelectric transducer in an optical resonance cavity

External-cavity diode laser (ECDL) can continuously change the output wavelength by scanning its optical resonance cavity (ORC), which is a piezoelectric transducer (PZT) actuated mechanical system. However, because of the inherent hysteresis characteristic of PZT and limited system bandwidth, the dynamic response of the ORC system exhibits both the nonlinearity and vibration while high-frequency triangular wave scanning, thereby resulting in the degradation of the output wavelength performance of the ECDL. As a result, a trajectory optimization method is presented to improve the scanning characteristics of the ORC system. Firstly, the hysteresis behavior of PZT is described by the elliptic model to generate a compensation voltage for the PZT. Secondly, the cusp region of the compensation voltage is optimized by a frequency domain objective function to greatly reduce the high-order harmonic components of the compensation voltage. Finally, an optimal voltage trajectory for driving the ORC system is obtained. The experimental results demonstrate the validity of our proposed trajectory optimization method for simultaneously suppressing the nonlinearity and vibration of the ORC system within the scanning frequency of 100 Hz.

A Dynamic Control Model of the Blades Position for the Vertical-Axis Wind Generator by a Program Method

This article discusses the construction of a dynamic model for controlling the position of the blades of a vertical-axis wind generator using an automatic approach; a method is presented that relates the rotation of the motor to the position of the blades, which allows the optimization of the operation of the control system. In the research process, an automatic approach is used, which makes it possible to carry out numerical calculations that predict the behavior of the system at various values of motor rotation. The model allows us to analyze the dependence of the position of the blades on the rotation of the motor and determine the optimal parameters of the mathematical control model. The main goal of our study is to develop a mathematical model of the mechanism for further adjustment of the wind turbine blade position control system depending on the wind speed.

Dynamic real-time energy saving control of pressure-swing distillation based on artificial neural networks

Parameter variation can increase economic costs and energy consumption in distillation process when disturbances occur. In this work, a dynamic optimization control model based on ANNs was developed. Composition disturbances in pressure-swing distillation with and without heat integration to separate isoamyl alcohol and toluene were regarded as the research object. The economic cost and energy consumption were effectively reduced by predicting the most optimal operating parameters during the disturbance process and controlling the process in real time. In the absence of heat integration, the energy consumption of the control structure based on ANNs (CS2) is reduced by 1.68% and 1.70% respectively compared to traditional control structure (CS1) during ± 20% composition disturbance. In partial heat integration, the energy consumption of the control structure based on ANNs (CS4) is reduced by 3.24% and 2.68% compared to traditional control structure (CS3). TAC is saved by 3.51% and 3.31% when reaching stability.

An adaptive robust path-tracking control algorithm of multi-articulated and redundantly actuated virtual track train

Multi-articulated and redundantly actuated virtual track trains (VTTs) are inevitably exposed to unknown exogenous disturbances and various operating conditions. To address the complex over-actuated tracking-control problem, this study proposes a decoupling control scheme for an all-wheel independent drive and all-wheel active steering VTT composed of multiple modules, wherein the adaptive robust path-tracking and longitudinal speed control algorithms of the VTT are decoupled. An appropriate control-oriented nonlinear dynamic model for VTT path-tracking control is formulated using Lagrange’s equations. To compensate for the model uncertainties and environmental disturbances without priors and parameter estimations, a robust control algorithm using radial basis function neural network and sliding mode is proposed. The asymptotic stability of the algorithm is proved, and its adaptation law is derived using the Lyapunov theory. Moreover, a novel traction/braking control algorithm based on the wheel speed distribution model and double closed-loop control algorithm is suggested. Using a multi-body dynamic model of the VTT with seven modules, simulations are performed to evaluate the effectiveness and superiority of the proposed path-tracking algorithm. The obtained results indicate that the proposed algorithm could realise the expectable performance of the VTT path tracking and longitudinal speed control. Moreover, the proposed control algorithm has good scalability for multi-articulated and redundantly actuated VTT.

An Optimal Operation Strategy of Regenerative Electric Heating Considering the Difference in User Thermal Comfort

Regenerative electric heating has gradually become one of the main forms of winter heating with the promotion of “coal to electricity” project. By fully exploiting its regulating capacity, it can effectively achieve a win–win situation of “peak shaving and valley filling” on the grid side and “demand response” on the customer side. In order to meet the different heating demands of users, a regenerative electric heating optimization and control strategy is proposed, taking into account the difference in users’ thermal comfort. Firstly, the reasons for the difference in user thermal comfort are analyzed, and the differentiated preference factors are calculated based on the maximum likelihood estimation method to design differentiated heating schemes. Then, a dynamic optimization and control model for regenerative electric heating with comfort and economic evaluation indicators is established and solved by using quantum genetic algorithm. Finally, a numerical example is used for simulation analysis. The research results show that the strategy proposed in this paper can take into account the comfort of customers and the economy of peaking and low load shifting, so that the operation of regenerative electric heating can respond to the different needs of different customer groups, and realize flexible adjustment at any time of the day.

Synchronous oscillation characteristic and finite-time function projection synchronization control method for microgrids

Due to the nonsynchronization of each microsource node in a microgrid, the physical parameters of each node are not uniform. Therefore, unstable oscillation will occur for the whole microgrid system. To solve the oscillation problem caused by the nonsynchronization of each microsource node in the microgrid, a hierarchical control structure microgrid model is researched in this paper. Based on the theory of chaotic synchronization and finite time, a new microgrid synchronous oscillation characteristic and finite-time function projection synchronization control method is proposed. First, combining complex networks and microgrids, a nonlinear dynamic model of microgrid hierarchical control is established, and the abilities of the small-world network model and the formal network model to adjust for asynchronous oscillations are verified by comparing the two models. Then, aiming at the unsynchronized oscillations caused by external system noise, a new finite-time function projection synchronization control method is proposed. According to the verification results, the method can control external disturbances, avoiding system oscillation.