Improve System Dynamics Research Articles

Multi-agent reinforcement learning for integrated manufacturing system-process control

The increasing complexity, adaptability, and interconnections inherent in modern manufacturing systems have spurred a demand for integrated methodologies to boost productivity, improve quality, and streamline operations across the entire system. This paper introduces a holistic system-process modeling and control approach, utilizing a Multi-Agent Reinforcement Learning (MARL) based integrated control scheme to optimize system yields. The key innovation of this work lies in integrating the theoretical development of manufacturing system-process property understanding with enhanced MARL-based control strategies, thereby improving system dynamics comprehension. This, in turn, enhances informed decision-making and contributes to overall efficiency improvements. In addition, we present two innovative MARL algorithms: the credit-assigned multi-agent actor-attention-critic (C-MAAC) and the physics-guided multi-agent actor-attention-critic (P-MAAC), each designed to capture the individual contributions of agents within the system. C-MAAC extracts global information via parallel-trained attention blocks, whereas P-MAAC embeds system dynamics through permanent production loss (PPL) attribution. Numerical experiments underscore the efficacy of our MARL-based control scheme, particularly highlighting the superior training and execution performance of C-MAAC and P-MAAC. Notably, P-MAAC achieves rapid convergence and exhibits remarkable robustness against environmental variations, validating the proposed approach’s practical relevance and effectiveness.

Innovative model predictive control for HVDC: circulating current mitigation and fault resilience in Modular Multilevel converters

This study presents an advanced Model Predictive Control (MPC) technique designed to mitigate Circulating Current (CC) in HVDC systems equipped with Modular Multilevel Converters (MMC). This MPC strategy eliminates the need for traditional PI regulators and pulse width modulation, improving system dynamics and control accuracy. It excels in managing output currents and mitigating voltage fluctuations in sub-module capacitors. Moreover, the paper introduces a novel communication-free Fault Ride-Through (FRT) method that makes a DC chopper redundant, enabling rapid recovery from disturbances. To reduce the computational burden of standard MPC algorithms, an aggregated MMC model is proposed, significantly decreasing the computational complexity. Simulation studies validate the new MPC algorithm’s capability in regulating AC side current, reducing CC, and ensuring capacitor voltage stability under varying conditions. The findings indicate that the proposed MPC controller outperforms traditional PI and PR-based methods, offering enhanced dynamic response, decreased steady-state error, and lowered converter losses, which contribute to smoother DC link voltages. Future research will focus on system scalability, renewable energy integration, and empirical validation through hardware-in-the-loop testing.

Rapid Prototyping and Hardware-In-the-Loop Verification of Enhanced Sliding Mode Control of an Asynchronous Machine Using a Xilinx System Generator and an FPGA-Zynq Board

This article describes rapid prototyping using the Xilinx system generator (XSG) of a direct field oriented control (DFOC) strategy based on improved super twisting sliding mode controllers (ISTSMCs) of an asynchronous machine to succeed a hardware implementation on a field programmable gate array (FPGA) board. The main goal is to enhance regulation loops governing rotor speed, rotor flux, and stator current components within the classical DFOC structure. These ISTSMCs replace integral proportional controllers, resulting in improved system dynamics, faster speed and torque responses, and heightened robustness against load and rotor resistance variations, surpassing other control methods. Additionally, the article aims to implement this DFOC-ISTSMC method on an FPGA board to reduce control system sampling time and loop delays, leveraging the FPGA’s parallel processing capabilities. The hardware architecture is designed using XSG in the MATLAB/Simulink environment, enabling effective simulation, testing, discrete algorithm creation, and VHDL code generation for FPGA implementation via the hardware-in-the-loop (HIL) process. Assessment involves digital simulation studies and HIL processes using XSG with a Xilinx FPGA Zynq 7000 under MATLAB/Simulink, demonstrating improved system performance, reduced time delays, and efficient FPGA utilization. This approach validates the effectiveness of the proposed DFOC-ISTSMC algorithm in enhancing control strategy for asynchronous machines.

Design of Lead, Lag and Lead-Lag Compensators Based on Frequency Response Approach

In the control systems, the compensators are widelyused to improve system dynamics, characteristics of the openloop system and the stability. The degree of convergence of theoutput waveform for any compensator depends upon theproper selection and tuning of the compensator. The dynamicstructure and the control properties of lead, lag and lead-lagcompensators are presented in this work. A simple methodbased on the frequency domain is adopted to design the threedifferent compensators. The parameters which have effect oneach compensator performance were studied and explainedhow to choose them to achieve the optimal performance. Thecompensator algorithms are implemented with MATLABsimulation in order to control the DC motor motion. The resultsand the comparative analysis confirm the effectiveness of theproposed three compensators and improve the systemresponse.

Descriptive design structure matrices for improved system dynamics qualitative modeling

AbstractQualitative modeling approaches can be useful in system information collection, model analysis, and formal model development. This is difficult when the number of elements and their interactions in the system is large. System dynamicists need additional tools and methods to conceptually model these large tightly coupled systems. We propose and test the Descriptive Design Structure Matrix (DDSM) as a qualitative system dynamics modeling tool and approach for systems with many elements and more interactions that can reasonably be modeled individually using traditional system dynamics methods. A DDSM consists of four parallel and internally consistent matrices that describe system interactions with binary relations, nontechnical text, technical text, and literature support. By including and documenting system information in multiple forms, DDSMs facilitate multiple stages of system dynamics modeling, improve modeler communication with system participants and domain experts, and improve model rigor. DDSM construction is described. A case study of the 2014 flooding in Kashmir is used to illustrate and test a DDSM and its application. Due to their compact format, DDSMs provide a useful visual communication aid, intuitive reasoning tool, and foundation for formal system dynamics modeling and analysis. © 2024 The Authors. System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society.

A Novel Hybrid IGSA-BPSO Optimized FOPID Controller for Load Frequency Control of Multi-source Restructured Power System

Background: An investigation of Automatic Generation Control (AGC) for a two-area, multi-source, interconnected power system under deregulation is presented in this article. For a more realistic approach, physical constraints namely Generation Rate Constraints (GRC) and Time Delay (TD) are incorporated into the system. Objective: This article proposed a novel hybrid Improved Gravitational Search Algorithm – Binary Particle Search Optimization (IGSA-BPSO) optimized Fractional Order Proportional-Integral- Derivative (FOPID) controller to regulate the frequency of a multi-area multi-source (thermalhydro- gas) interconnected power system in a deregulated environment. Methods: Integral Time Multiplied by Absolute Error (ITAE) is used as the objective function to be minimized by optimization techniques for getting optimum parameters of FOPID controllers installed in each area. Results: To inspect the efficacy of the suggested method, the dynamics of the system are investigated for poolco, bilateral and contract violation cases and the comparative results are also presented and analyzed. The supremacy of the recommended technique is studied by comparing with other well-known techniques namely GSA and PSO. Conclusion: The robustness of the proposed system is examined by sensitivity analysis after variations in different system parameters. In this paper, the AC-DC tie-line model is incorporated for the AGC mechanism. Dynamic load changes condition is also tested and verified. The study found that the proposed controller provides improved system dynamics in all competitive electricity market contract situations under varied system uncertainties.

Risk path prediction and early warning method of LNG unloading system based on improved kinetic model

The LNG unloading system is an important transit station for LNG storage, processing and outgoing transmission. The main processes include ship unloading, storage, gasification and outgoing transmission, BOG condensation, flare venting and other processes, which involve intensive personnel operation and safety management activities. Once LNG is leaked, frostbite will happen to personnel and fire and explosion will occur when encountering ignition sources, which will cause serious consequences that are difficult to bear. The risk path of the static safety evaluation method is based on qualitative judgment, and the dynamic evolution direction of the LNG unloading system risk factors is not accurately predicted, so how to accurately predict and warn the risk path during the risk evolution is a key issue. In this paper, based on the improved system dynamics model, a propagation method with risk entropy as the information carrier is established to identify two different types of dynamic risk nodes, energy diffusion and energy conduction. Compared with the traditional dynamics model, the prediction accuracy of dynamic risk nodes of the improved model is increased by 22%, 20% and 14% for the unloading process, storage process and loading and transfer process, respectively. The prediction of the path of energy-conducting type risk evolution direction is increased by 29.3%, 32.4% and 30%, respectively. The simulation results are beneficial to the dynamic risk control and accident prevention of LNG unloading system, and provide new ideas for natural gas energy safety and security.

Analysis on the resource and environmental carrying capacity of coal city based on improved system dynamics model: a case study of Huainan, China.

As the major energy bases, numerous coal cities in China are facing severe challenges in terms of resources and environment. In order to overcome the disadvantages of static evaluation, this study selected Huainan city, a typical coal city in China, as the case, and combined with the improved SD (system dynamics) model, analyzed its RECC (resource and environmental carrying capacity) systematically and dynamically. Firstly, a SD model of RECC system including resource-environment and society-economy subsystem was constructed. Then, the control parameters were determined objectively according to the analysis results of BP-DEMATAL model. Thirdly, we designed 18 simulation scenarios based on orthogonal test to dynamically predict the development trend of RECC in different conditions. Results show that: (1) From 2019 to 2030, the RECC of Huainan is generally on the rise. (2) In all simulation scenarios, test 12 is the most effective way of improving RECC. (3) The factors with the greatest influence on the simulation results are GDP, output value of secondary production, total expending on environmental protection, and coal production. This study provides a reference for the analysis method of RECC and the sustainable development of coal cities.

An Adaptive Active Disturbance Rejection Control Strategy for Speed-Sensorless Induction Motor Drives

Speed-sensorless control of induction motor drives based on the extended state observer (ESO) attracts much popularity due to its key characteristics, e.g., satisfactory estimation performance, and high robustness against disturbances. However, the conventional ESO generally uses fixed high gains to achieve fast convergence. This may bring about the concern of noise sensitivity. Moreover, a proportional-integral (PI)-type speed controller is mostly adopted in speed-sensorless control of induction motor drives, which may cause unsatisfactory system dynamics. To address these, an adaptive active disturbance rejection control (ADRC) strategy is proposed for speed-sensorless induction motor drives in this article. In this scheme, a third-order adaptive ESO (AESO) is first used to estimate the speed, the phase, and the overall disturbance. Then, a speed controller based on the state error feedback control rate (SEFCR) is designed to improve system dynamics. In practice, disturbances like dc offsets may further challenge estimation performance, and hence, a closed-loop flux observer (CLFO) is employed to deal with this issue. Additionally, a parameter sensitivity analysis of the CLFO is also provided to evaluate the performance of the proposed scheme. Finally, extensive experimental tests have been carried out to validate the effectiveness of the proposed.

Frequency coordination and harmonic suppression strategy based on composite virtual impedance in hybrid energy storage system

Under the traditional droop control, hybrid energy storage system cannot take advantage of the respective merits of battery and supercapacitor for frequency coordination, which shortens system life. Because of the single-phase inverter load, the second harmonic current is generated in the front-stage converter when the output power pulsates at twice the output voltage frequency. This paper proposes a strategy for frequency coordination and second harmonic current suppression based on composite virtual impedance. Under this strategy, the system equivalent output impedance exhibits resistance and inductance in low-frequency part, dominated by the battery side. The battery reacts slowly to absorb the low-frequency power fluctuations and provide the energy required by load at steady state. It is capacitive in the high-frequency range, with the supercapacitor side dominating. The supercapacitor quickly absorbs the high-frequency part of power fluctuation to suppress the impact of load mutation on DC bus and improve system dynamics. Meanwhile, the strategy realises that the inductor branch impedance is significantly larger than the capacitor branch impedance at double frequency, suppressing the second harmonic current and increasing the system efficiency. Experimentation confirms the effectiveness of the control strategy.

An Optimization Analysis Model of Tourism Specialized Villages Based on Neural Network and System Dynamics

With the rapid development of tourism, professional tourism villages emerge one after another, which has become the focus of the tourism industry. At present, there are some problems in tourism professional villages, such as imperfect management and inaccurate prediction of future development, which affect the rational allocation of tourism resources. In order to improve the distribution of tourism resources and better predict the development of tourism professional villages, it is necessary to make comprehensive judgment and analysis, especially the analysis of indicators such as the prediction and development judgment of tourism professional villages. This paper discusses the optimization analysis of the agglomeration of tourism specialized villages by backpropagation (BP) neural network and system dynamics model, analyzes the system structure of the agglomeration factors of tourism specialized villages, and promotes the intelligent integration of the agglomeration factors. The development of clusters of professional villages promotes data integration among resources, economy, society, and other elements and presents the characteristics of big data. As the level of concentration of professional villages increases, the complexity of the associated factors also increases, which increases the difficulty and effectiveness of tourism analysis. In view of this situation, taking mountain tourism as the research object, this paper proposes an improved system dynamics model based on BP, extracts features from cross factor (resource, economic, and social) data, and optimizes the relationship between professional village agglomeration and various factors. The MATLAB simulation results show that based on the improved system dynamics analysis, the simplification rate of (resources, economy, and society) data can be controlled at more than 24%, the degree of agglomeration is more than 95%, and the construction time of the relationship map of related factors is less than 40 s. Therefore, the analysis method proposed in this paper is suitable for the calculation of the agglomeration of tourism professional villages in the mountain area and can meet the needs of the development of tourism professional villages in the mountain area.

Profitability prediction in thermal power enterprise based on the improved system dynamics model

This paper tries to improve the system dynamics model and to establish the model of the thermal power enterprise profitability by using Newton interpolation method to optimize the nonlinear function, MATLAB to fitting equation and data analysis; and through the VENSIM to present a simulation. The model is dissertated and validated by some thermal power enterprise's financial data. This model can provide enterprises with management decision-making reference.

Power generation control of restructured hybrid power system with FACTS and energy storage devices using optimal cascaded fractional‐order controller

AbstractThis work presents power generation control of a two‐area hybrid restructured power system, initially integrated with renewable energy source (RES) and electric vehicle (EV). For further analysis, additional inclusion of FACTS and energy storage devices in the two‐area hybrid restructured power system is to be analyzed for improved system dynamics. The hybrid restructured power system is a complex nonlinear system that brings light to the major problem of system dynamic control due to insufficient damping under varying loading circumstances. To solve this problem, a robust control approach with rapid acting controllable and storage devices are an immediate need for advanced power system. Motivated from the fact that cascaded fractional order controllers exhibit better performance in comparison to classical controllers, this article proposes an advanced cascaded fractional‐order ID‐fractional‐order PD (cascaded FOID‐FOPD) controller for system performance enhancement of hybrid restructured power system. The maiden application of satin bower bird optimizer technique to optimize the secondary controller gains is employed. The proposed controller's superiority is demonstrated by comparing the results to the other existing controllers. The impact of RES's generation and EVs on system dynamics for cascaded FOID‐FOPD controller is also explored. Furthermore, the sensitivity analysis is done to reflect that optimized gains of cascaded FOID‐FOPD controller are supportive enough for variations in RESs, load perturbations, capacity ratio, and disco participation matrix.

Utilization of electric vehicles in combined voltage‐frequency control of multi‐area thermal‐combined cycle gas turbine system using two degree of freedom tilt‐integral‐derivative controller

AbstractThis paper conveys the unified control pattern of frequency and voltage of interconnected multi‐area power system through the automatic load frequency control (ALFC) loop and automatic voltage regulator (AVR) loop. Each area includes a single‐stage reheat thermal plant, combined cycle gas turbine (CCGT) plant, and electric vehicles (EVs). Pertinent generation rate constraint for thermal and CCGT plant and governor dead band for the thermal plant is considered. A maiden attempt has been made to introduce the two degree of freedom tilt‐integral‐derivative (2DOFTID) controller for both the ALFC and AVR loops. In this study, a meta‐heuristic algorithm called Harris Hawks optimization (HHO) is implemented to optimize the controller parameters. Study divulges that performance of the 2DOFTID controller predominates when compared with TID and proportional‐integral‐derivative with filter (PIDF) controller apropos time‐domain indices. By juxtaposition, the superiority of the proposed algorithm and performance index is observed. Also, improved system dynamics were independently exhibited by the influence of the lumped EV model and AVR. For the predominance of the 2DOFTID controller, different patterns of disturbances such as random and sinusoidal varying load perturbation have been investigated. Eventually, the sensitivity studies were accomplished to determine the sturdiness of the proposed controller for substantial variations in the system parameters.

An Improved System Dynamics Model to Evaluate Regional Water Scarcity from a Virtual Water Perspective: A Case Study of Henan Province, China

An accurate and practically useful evaluation of regional water scarcity is a necessary procedure in scarcity monitoring and threat mitigation. From the perspective of virtual water, this study proposed an improved system dynamics model to evaluate regional water scarcity (WS), including a case study of Henan province, China. We enhanced the existing system dynamics model of WS evaluation from a virtual water perspective by (1) defining WS as the ratio of the consumption-based blue water footprint to water availability, in order to compare the water requirements that need to be met to satisfy the local demand of goods and services with water supply; (2) integrating the economic growth, trade, and water use efficiency in the tertiary industry (e.g., accommodation, food and beverage services) into the model, in order to improve the accuracy of WS assessment and help find more specific measures to reduce WS by factor adjustment; (3) distinguishing the product use structure matrix, as well as the sectoral direct water use coefficient, in local regions from that in other domestic regions and foreign countries, and identifying the regional use structure matrices of products from these three kinds of regions, in order to increase the calculating veracity; and (4) displaying performances of the society, economy, and environment in WS reduction, in order to offer a more comprehensive reference for practical policy decisions. The case study results show that Henan has been suffering from, and in the near future could continue to face, water scarcity, with an average of 2.19 and an annual rise of 1.37% during 2008–2030. In the scenario comparison of current development, production structure adjustment, technology upgrade, and trade structure adjustment in supply-side structural reform of Henan from 2019 to 2030, WS could be reduced by updating production structures into less production of agricultural products or other sectors with a high production-based water footprint (with the smallest average WS of 2.02 and the second smallest total population and GDP, i.e., gross domestic production), technology enhancement in water saving, purification and pollution control (with the second smallest average WS of 2.04 and the largest total population, GDP and total available water resources). Furthermore, for the agricultural products or other sectors with high domestic/international virtual water outflow (inflow), if we reduce (increase) their percentage of outflow (inflow) in the industry involved, WS will increase only more slightly than that when we keep the current development trend, with the smallest total population. Potential measures for alleviating WS should be taken comprehensively, with priorities being identified according to the socioeconomic and environmental performance. Our model can be useful for practical policymaking and valuable for relevant research worldwide.

Power dispatching techniques as a finite state machine for a standalone photovoltaic system with a hybrid energy storage

Standalone photovoltaic system (SPVS) is usually embedded with an energy storage unit to overcome the intermittency of photovoltaic (PV) generation as well as to address load variations in off-grid operation. In SPVS energy systems, batteries can serve as the long term energy storage and contributing to the large portion of the energy demand but to overcome the load intermittency, it necessitates a fast response energy storage embedded with the battery as a hybrid energy storage (HES) for dynamic loads (e.g., Electric Vehicle loads, emergency power management). In this work, Lead-Acid (LA) battery and supper capacitor (SC) array are used as the HES. HES helps not only in increasing more utilization of PV generated power but also in improving system dynamics and stability for power intensive loads operation. This paper presents innovative power management and control strategies for a HES connected in DC coupled architecture. The presented architecture can also be used for DC micro grids. Power management strategies are developed in a hierarchical architecture as an event driven finite state machine. Primary control is implemented using current mode control and voltage mode control adapted at the bi-directional DC-DC converters and the voltage source inverter connected to the HES, respectively. The performance of the developed power management strategies for the HES is analyzed for typical load dynamics.

Development of a flow control valve with digital flow compensator

Flow control valves typically use mechanical pressure drop compensator or dynamic flow meter to lessen the impact of pressure drop on outlet flow. However, there are some disadvantages, such as complex mechanical structure and small flow capacity. In this paper, a kind of digital flow compensator with bilinear interpolation algorithm is presented to compensate the pressure drop, in which the pressure drop and the desired outlet flow are the two input parameters. A two-stage proportional flow control valve with the proposed compensator is investigated. Pressure drop across the metering orifice of the valve is measured and fed back to the proposed compensator. If the detected pressure drop has deviated from the threshold, then the compensator will generate a compensation signal to adjust the poppet opening of the valve, which ensures that the output flow is independent of the pressure drop. Performances of the valve with the proposed compensator are investigated by simulation and experiment. Results show that it has a reasonable static control characteristics. In addition, there is no dead-zone in its steady flow curve; pressure drop have little impact on its output flow. Its dynamics will be affected by pressure drop and input voltage. Increasing pressure drop can improve system dynamics under constant input signal conditions. On the other hand, increasing input signal can shorten the poppet's closing time, but it will result in the longer opening time and the greater overshoot in the opening stage.

Study of differential search algorithm based automatic generation control of an interconnected thermal-thermal system with governor dead-band

An attempt has been made to the effective application of a recently introduced, powerful optimization technique called differential search algorithm (DSA), for the first time to solve load frequency control (LFC) problem in power system. In this paper, initially, DSA optimized classical PI/PIDF controller is implemented to an identical two-area thermal-thermal power system and then the study is extended to two more realistic power systems which are widely used in the literature. To assess the usefulness of DSA, three enhanced competitive algorithms namely comprehensive learning particle swarm optimization (CLPSO), ensemble of mutation and crossover strategies and parameters in differential evolution (EPSDE), and success history based DE (SHADE) are studied in this paper. Moreover, the superiority of proposed DSA optimized PI/PID/PIDF controller is validated by an extensive comparative analysis with some recently published meta-heuristic algorithms such as firefly algorithm (FA), bacteria foraging optimization algorithm (BFOA), genetic algorithm (GA), craziness based particle swarm optimization (CRPSO), differential evolution (DE), teaching-learning based optimization (TLBO), particle swarm optimization (PSO), and quasi-oppositional harmony search algorithm (QOHSA). A case of robustness and sensitivity analysis has been performed for the concerned test system under parametric uncertainty and random load perturbation. Furthermore, to demonstrate the efficacy of proposed DSA, the system nonlinearities like reheater of the steam turbine and governor dead band are included in the system modeling. The extensive results presented in this article demonstrate that proposed DSA can effectively improve system dynamics and may be applied to real-time LFC problem.

Model-based active control of a continuous structure subjected to moving loads

Modelling of a structure is an important preliminary step of structural control. The main objectives of the modelling, which are almost always antagonistic are accuracy and simplicity of the model. The first part of this study focuses on the experimental and theoretical modelling of a structure subjected to the action of one or two decelerating moving carriages modelled as masses. The aim of this part is to obtain a simple but accurate model which will include not only the structure-moving load interaction but also the actuators dynamics. A small scale rig is designed to represent a four-span continuous metallic bridge structure with miniature guiding rails. A series of tests are run subjecting the structure to the action of one or two minicarriages with different loads that were launched along the structure at different initial speeds. The second part is dedicated to model based control design where a feedback controller is designed and tested against the validated model. The study shows that a positive position feedback is able to improve system dynamics but also shows some of the limitations of state- space methods for this type of system.

Support Vector Machine Identification of Subspace Hammerstein Models

Abstract — In this work a new method for identifying subspace Hammerstein systems based on Support vector machine regression is presented. It has been developed by modifying a least-square support vector machine based approach presented earlier. The new algorithm exploits the properties of generic SVM which LS-SVM based algorithm lacks. These properties are robustness in the presence of outliers and sparseness of solution. The proposed algorithm is reduced to include the least number of quadratic programming problems needed to estimate the system matrices and nonlinearity which in turn will reduce the computation complexity of the algorithm. Index Terms — Hammerstein models, subspace identification, support vector machines. I. I NTRODUCTION For researchers and practitioners, modeling is an essential instrument to realize and improve system dynamics [1]. In physical modeling, similarity is used to simulate the real system. For example, an analog computer may be used to build something that behaves almost like the original system. However, to model a complicated engineering system, one must use different method, such as pure mathematical representation, as an analog computer representation would be huge and complicated [2]. Such mathematical representations can be found from fundamental principles, for instance: Newton’s laws, Kirchhoff’s laws, conservation laws, etc. However, this approach is inappropriate for complicated systems [3]. System identification, which is the science of deriving mathematical procedures that form a suitable mathematical model of a system from available input and output data, is good modeling candidate for complex systems. It has caught the attention of researchers and practitioners for many years [4], [5]. In the last two decades, subspace identification theory [6], [7] has attracted researchers’ interest because of its efficiency in identifying state-space models for high order, multiple input, multiple