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Novel coronavirus respiratory disease COVID-19 has caused havoc in many countries across the globe. In order to contain infection of this highly contagious disease, most of the world population is constrained to live in a complete or partial lockdown for months together with a minimal human-to-human interaction having far reaching consequences on countries' economy and mental well-being of their citizens. Hence, there is a need for a good predictive model for the health advisory bodies and decision makers for taking calculated proactive measures to contain the pandemic and maintain a healthy economy. This paper extends the mathematical theory of the classical Susceptible-Infected-Removed (SIR) epidemic model and proposes a Generalized SIR (GSIR) model that is an integrative model encompassing multiple waves of daily reported cases. Existing growth function models of epidemic have been shown as the special cases of the GSIR model. Dynamic modeling of the parameters reflect the impact of policy decisions, social awareness, and the availability of medication during the pandemic. GSIR framework can be utilized to find a good fit or predictive model for any pandemic. The study is performed on the COVID-19 data for various countries with detailed results for India, Brazil, United States of America (USA), and World. The peak infection, total expected number of COVID-19 cases and thereof deaths, time-varying reproduction number, and various other parameters are estimated from the available data using the proposed methodology. The proposed GSIR model advances the existing theory and yields promising results for continuous predictive monitoring of COVID-19 pandemic.

This paper presents a dynamical analysis of a tethered aerostat using a multibody simulation tool called SimscapeMultibody™. Tethered aerostats are lighter than air vehicles that float in the air and are connected to the ground through the tether. Atmospheric winds are a source of disturbance to the aerostat. The wind produces drag force that causes the aerostat to displace from its mean position. This phenomenon is called blow-by. Solving the mathematical model of the motion dynamics with time step is the conventional method used for simulating the flight dynamics of the aerostat. This paper discusses the application of Simscape Multibody™ for the three-degrees-of-freedom flight dynamic simulation of spherical tethered aerostat. Simscape Multibody™, an extension of MATLAB®/Simulink, is a multibody modeling tool in which physical bodies and their interactions have to be defined to simulate the dynamics. The mathematical model of the defined system is automatically generated by the SimscapeMultibody™ solver, and the system dynamics is simulated. The simulation of dynamics using multibody modeling tools provides high fidelity simulation for the tethered aerostats.

This work reviews wheel load application methods in single-wheel testbeds (SWTBs), used widely to study terramechanics. Normal force oscillations have been reported in SWTBs, and visible in time-series data when provided. This has usually been attributed to grousers, but is also visible for wheels without grousers suggesting other system dynamics (e.g. friction in the vertical axis) are the cause. Furthermore, accelerations related to periodic vertical displacements of the wheel (whether caused by grousers or otherwise) cannot explain more than a small fraction of the amplitude of normal force variations. In this work, a 4-bar mechanism was developed that balances the normal load and ensures that a constant (not oscillating) normal force is achieved throughout terramechanics experiments. System behaviour with and without the 4-bar mechanism was assessed, highlighting the need for a 4-bar mechanism or other method of balancing normal loads if a constant normal force is desired. No significant effects on average experimental results were observed due to normal force oscillation, but artifacts were observed in the time-series data. Comparisons of results with a system where wheel load is applied pneumatically and characterization of friction in each testbed provide further support to the conclusion that normal force oscillations are mainly caused by friction in the vertical axis.

Most current methodologies on fixed-time adaptive fuzzy control for uncertain nonlinear systems result in practical fixed-time stability but not fixed-time stability, or require prior knowledge of the system dynamics. To obviate such restrictions, a fixed-time adaptive fuzzy control scheme is newly proposed with the discoveries of a singularity-avoidance virtual control design, a modified class of tuning functions and a projection operator-based adaptation mechanism. Fixed-time stability is established in the sense that the tracking error asymptotically converges to a user-defined interval within a prescribed fixed time. Illustrative examples verify the approaches developed.

In this paper, we aim to solve the optimal tracking control problem for a class of nonaffine discrete-time systems with actuator saturation. First, a data-based neural identifier is constructed to learn the unknown system dynamics. Then, according to the expression of the trained neural identifier, we can obtain the steady control corresponding to the reference trajectory. Next, by involving the iterative dual heuristic dynamic programming algorithm, the new costate function and the tracking control law are developed. Two other neural networks are used to estimate the costate function and approximate the tracking control law. Considering approximation errors of neural networks, the stability analysis of the proposed algorithm for the specific systems is provided by introducing the Lyapunov approach. Finally, via conducting simulation and comparison, the superiority of the developed optimal tracking method is confirmed. Moreover, the trajectory tracking performance of the wastewater treatment application is also involved for further verifying the proposed approach.

Structural damage in offshore wind jacket support structures are relatively unlikely due to the precautions taken in design but it could imply dramatic consequences if undetected. This work explores the possibilities of damage detection when using low resolution data, which are available with lower costs compared to dedicated high-resolution structural health monitoring. Machine learning approaches showed to be generally feasible for detecting a structural damage based on SCADA data collected in a simulation environment. Focus is here given to investigate model uncertainties, to assess the applicability of machine learning approaches for reality. Two jacket models are utilised representing the as-designed and the as-installed system, respectively. Extensive semi-coupled simulations representing different operating load cases are conducted to generate a database of low-resolution signals serving the machine learning training and testing. The analysis shows the challenges of classification approaches, i.e. supervised learning aiming to separate healthy and damage status, in coping with the uncertainty in system dynamics. Contrarily, an unsupervised novelty detection approach shows promising results when trained with data from both, the as-designed and the as-installed system. The findings highlight the importance of investigating model uncertainties and careful selection of training data.

This article addresses the decentralized control problem of continuous-time nonlinear large-scale interconnected systems by the means of echo state network (ESN). The interconnected terms between the subsystems are treated as the disturbances added to the system dynamics, then the control problem is solved by the proposed robust controllers. By designing a cost function for the nominal subsystems, the robust controller is obtained by solving optimal controllers. The stability of the interconnected systems is proved by a composite Lyapunov function. The online adaptive dynamic program (ADP) method is employed to solve the transformed optimal problem, where a single ESN is utilized to approximate the critic cost and control policy. The optimal control policies of all the isolated subsystems are obtained simultaneously. The closed-loop stability of the feedback nominal systems is also proved, in a uniformly ultimately boundedness (UUB) manner. In the simulation, a large-scale interconnected system with four subsystems is provided to verify the effectiveness of the proposed decentralized controllers.

Cubic model of flux-controlled memristor model is presented in this work and the model is analyzed at various fractional orders. Analytic computations with a sinusoidal source shows that the v - i loop can have up to two more additional intersection points aside from the origin and are located in the 1 st and 3 rd quadrants respectively. The locations of the intersection points and the area of the v - i loops are both dependent on the excitation signal frequency and fractional orders. Floating and Grounded emulator circuits for the cubic models are designed incorporating a fractional order integrator designed from the 2 nd -order rational approximation of s - α . PSPICE simulations verify the correctness of the numerical analysis and calculations. An application example shows that the fractional-order cubic model can generate chaos in Chua’s circuit and produced an increase in the system dynamics.

This paper designs a novel online event-based near-optimal control scheme for multi-player nonzero-sum (NZS) games with partially unknown system dynamics. With the introduction of event-triggered mechanism (ETM), the repetitive computing burdens and communication loads of the signals are largely alleviated. Under the event-triggered mechanism, the framework of identifier-critic is structured to solve the coupled Hamilton–Jacobi equations (CHJEs) for NZS games. A feedforward neural network (FNN) is employed to construct an identifier to learn the unknown dynamics of the nonlinear system. The critic network for each player utilizes a modified tuning law, which is composed of three terms, to seek for the approximated optimal control schemes. Besides the conventional term derived from the gradient descent method, the second term derived from the experience replay (ER) technique utilizes the historical state data to update the critic network weight vector so as to remove the persistence of excitation (PE) condition. In addition, a novel term is added to stabilize the closed-loop systems. Owing to the stabilizing term, there is no need for the initial stabilizing control. In theory, the uniform ultimate boundedness (UUB) properties of the system states and the critic network weight errors are proved by utilizing Lyapunov theorem. Furthermore, the minimal intersample time is proved to be lower bounded with a positive constant which means that the Zeno behavior is excluded during the learning phases. Finally, we show the validity of the designed algorithm via simulating two examples.

This work proposes a novel frequency regulation paradigm for multi-area interconnected power systems. The developed approach capitalizes on phasor measurement units (PMUs) advanced monitoring to overcome design limitations imposed by legacy supervisory control and data acquisition (SCADA) systems. For this, a novel measurement-based controller integrating primary and secondary regulation is proposed. First, taking advantage of PMUs synchronized measurements, novel centralized corrective actions are developed using dynamic data aggregation. These actions provide a foreknowledge of generators expected steady-state during the system dynamics, significantly improving the system frequency rebound and steady-state realization while ensuring null area control errors (ACE). Next, a new local power system stabilizing perspective is developed. This controller provides local counterweighting actions allowing for faster corrective actions without compromising the system dynamic response, i.e., mitigating oscillations and overshoots. The proposed approach design considers monitoring and communication system non-idealities, such as: latency, processing and waiting time, measurements synchronization and sampling resolution. In addition, stability and steady-state objectives are mathematically demonstrated, and comparative simulation case-studies developed. Obtained results indicate a meaningful improvement of power systems frequency regulation and reliability, including faster steady-state realization and rebound, mitigation of oscillations and overshoot, improved service capacity and higher reliability to cascade events.

Frequency responses are nonidentical between the positive and the negative frequency range for symmetric three-phase systems modeled with complex transfer functions, which shows unbalanced stability margins. The unbalanced stability margins limit the system performance, as the system dynamics are restricted by the worst one. This article extends the discussion of unbalanced stability margins to asymmetric three-phase systems, i.e., three-phase grid-tied inverters, which are described by transfer matrices. The reason for unbalanced stability margins is analytically investigated, and different impacts are analyzed. It is found that both the control delay and asymmetric control loops can lead to the unbalanced stability margins. To improve the performance limited by the worst stability margin, a margin balancing control is proposed. By implementing a phase correction term, the worst stability margin can be increased by reducing the best one. As a result, the overall stability margin of the system can be improved. The design procedure is elaborated based on a 2-MW inverter system. Verifications are presented based on a 1-kW prototype including simulations, experimental results, and Nyquist analyses. This method applies to all types of grid-tied inverters with multiple control loops.

Most of the papers published so far on fractional discrete systems are related to theoretical results on their chaotic behaviors or to applications based on the mathematical modeling of the chaotic phenomena. No paper has been published to date regarding the hardware implementation of hyperchaotic fractional maps. This manuscript makes a contribution to the topic by presenting the first example of hardware implementation of hyperchaotic fractional maps. In particular, by exploiting the Grunwald–Letnikov difference operator, the paper introduces a new version of the fractional Grassi–Miller map. The system dynamics are analyzed via bifurcation diagrams and Lyapunov exponents, showing that the conceived map is hyperchaotic when the fractional order μ belongs to the interval [ 0 . 966 , 1 ] . Finally, a hardware implementation of the fractional map is illustrated, with the aim to concretely highlight the presence of hyperchaos in physical systems described by fractional difference equations. Arduino, an open-source platform, has been used to illustrate the simplicity as well as the feasibility of the implementation.

Substantial advantages of brushless doubly fed induction machine (BDFIM) bring up it as an interesting alternative for conventional solutions. In order to better analyze BDFIM performance, it is required to employ a more precise model of BDFIM. In this article, an approach, which completely relies on dynamic modeling in arbitrary reference frame, is proposed to consider both iron loss and main flux saturation effects in two-axis model of BDFIM. In this regard, the dynamic and static inductances are derived by using the magnetizing flux curve. If the cross-saturation is ignored, the time derivative of magnetizing inductances will yield incorrect simulation results. Therefore, to increase the accuracy, the proposed model contains terms that describe the cross-saturation effects. Due to the close relation between torque and power, the torque expression is also achieved in the presence of iron loss and magnetic saturation using expression of electrical input power. The simulation and experimental results are finally given to show the performance of the proposed model.

Persistent actuator failures can cause uncertain system dynamics mutation including minimum and nonminimum phase switching, which motivates us to develop a robust adaptive control scheme using a control-separation-based linear quadratic (LQ) design. It is shown that such a system can be expressed by a parameterized time-varying input–output model plus an actuator failure dependent signal and a persistent signal which captures the effect of the system dynamics mutation. A control-separation LQ design has the specific desired functions for output regulation and failure compensation as well, and is applied and analyzed for solving the persistent actuator failure compensation problem. Both nominal control design for system known and adaptive control design for system unknown are developed. The desired control performance is ensured by a robust adaptive control technique and is fully characterized in terms of a small in the mean output regulation, that is, the mean value of the system output is bounded by the average number of actuator failures over time and the average actuator failure value. Such desired system performance is shown by a complete analytical study and demonstrated by an illustrative simulation study.

This paper proposes a novel frequency regulation outlook focused on the improvement of islanded microgrids resilience. The proposed method takes advantage of power systems enhanced situation awareness enabled by distribution-level phasor measurement units (D-PMUs) monitoring to overcome current design boundaries that limit an effective harnessing of flexible resources potential to support microgrids resilience. For this, capitalizing on D-PMUs high resolution, low latency and time synchronized measurements, a beforehand assessment of the power mismatch leading the microgrid to frequency nadir condition is achievable during the early stages of the system dynamics. Based on this information, new measurement-based control modules are designed to address individual stages of the frequency regulation process in a dedicated manner, respectively arrest, rebound and recovery stages. These controllers provide a significant improvement of microgrid's frequency arrest and rebound ability, while ensuring a smooth recovery process toward the system steady-state realization. The proposed approach stability is mathematically demonstrated and comparative case-studies developed considering monitoring and communication systems non-idealities. Obtained results indicate the proposed controller superior ability to address single and cascade large-scale disturbances, while simultaneously providing an improved frequency regulation performance and greater economic benefit for flexible resource support.

The complex interactions between renewables and AC/DC networks have brought new oscillation issues. To effectively monitor and suppress these oscillations, the observability and controllability of the oscillatory modes at different locations of the system must be investigated. To achieve this, the paper first defines the concept of modal observability and controllability using the responses of unit-pulse perturbations. Then an eigenvalue-decomposition based method is proposed to calculate these two indexes with the impedance network model (INM) as the input. The obtained modal observability(controllability) has many important applications and one of them is to facilitate the aggregation of the INM. The paper investigates this application by selecting an optimal aggregation port to ensure that the aggregated impedance can reflect the complete system dynamics. Finally, the proposed method is verified on a simple passive circuit and a practical wind power system that experienced actual subsynchronous resonance incidents. Theoretical results as well as EMT simulations demonstrate the effectiveness of the method.

Under the background of China’s continuous promotion of urbanization, urban underground integrated pipeline corridor has become an inevitable trend of future urban integrated management. After the completion of the pipeline corridor, how to effectively manage its risks in operation and maintenance management has become a topic at this stage. In this paper, through the combination of the classical AHP method and DSM method, based on a large number of literature studies, the risk relationship system of the integrated pipe corridor is constructed. AnyLogic software is applied to simulate the system dynamics, analyze the impact of dynamic changes of each risk factor on the risk accident of the integrated pipe corridor, carry out uncertainty reasoning from multiple perspectives, and realize the evaluation and analysis of the accident risk of the integrated pipe corridor. The results of the study could provide targeted support tools for integrated pipeline corridor risk operation and maintenance management.

In multi-infeed hybrid AC-DC (MIDC) systems, the asynchronous interconnection between regional grids, complicated system dynamics and possible emergency faults have enormous effects on the frequency stability. To address the frequency instability problems in emergency situations, this paper proposes a decentralized emergency frequency control (EFC) strategy based on coordinated droop for the MIDC system. First, a P-f droop control for LCC-HVDC systems is introduced, and the coordinated droop mechanism among LCC-HVDC systems and generators is designed. Then, to appropriately allocate the power imbalance among the LCC-HVDC systems and generators, an optimal emergency frequency control (OEFC) problem is formulated, and the optimal droop coefficients are selected in a decentralized way, which can deal with various control objectives. A Lyapunov stability analysis shows that the closed-loop equilibrium is locally asymptotically stable considering the LCC-HVDC dynamics. The effectiveness of the proposed emergency control strategy is verified through simulations.

This paper proposes an adaptive neural network-based backstepping controller that uses rigid graph theory to address the distance-based formation control problem and target tracking for nonlinear multi-agent systems with bounded time-delay and disturbance. The radial basis function neural network (RBFNN) is used to overcome and compensate for the unknown nonlinearity and disturbance in the system dynamics. The effect of the state time-delay of the agents is alleviated by using an appropriate control signal that is designed based on specific Lyapunov function and Young's inequality. The adaptive neural network (NN) weights tuning law is derived using this Lyapunov function. An upper bound for the singular value of the normalized rigidity matrix is introduced, and uniform ultimate boundedness (UUB) of the formation distance error is rigorously proven based on the Lyapunov stability theory. Finally, the performance and effectiveness of the proposed method are validated through the simulation results on nonlinear multi-agent systems. Comparisons between the proposed distance-based method and an existing, displacement-based method are provided to evaluate the performance of the suggested method., Published in: IEEE Control Systems Letters ( Volume: 5 , Issue: 3 , July 2021 ) Page(s): 791 - 796 Date of Publication: 01 July 2020 Electronic ISSN: 2475-1456 INSPEC Accession Number: 19785039 DOI: 10.1109/LCSYS.2020.3006187 Publisher: IEEE

Early identification of business opportunities is critical for technology-based manufacturers seeking to develop new sustainable business models (SBMs) for future competitive advantages. However, there exists an insufficiency of identifying business opportunities compared to previous studies which have focused mainly on technology opportunities and service opportunities. To fill this research gap, this study proposes a new systematic approach to identify business opportunities for new SBMs based on information relating to the manufacturers' technologies and patents. To illustrate, an example in the mining machinery industry was examined as a case study. The results demonstrated that 255 patent documents relating to the product were collected. Next, latent Dirichlet allocation was used to generate 26 business topics, which were categorized into the 9 building blocks of the business model canvas (BMC). Then, generative topographic mapping (GTM) was applied to identify 13 vacuums and related technology-driven business opportunities on the basis of BMC-based patent-business vectors. Finally, dynamic business modelling was conducted, which integrated sustainable BMCs and system dynamics in order to evaluate and rank these business opportunities. The proposed approach can promote consensus building between the technology and business planning departments on developing technology-driven SBMs in both public and private sectors.

The definition of Circular Economy (CE) has evolved over time. It includes intervention options for reducing consumption, improving the efficiency of production, introducing recycling and reuse for materials management, including new business models geared at waste prevention. With the use of Systems Thinking and the creation of Causal Loop Diagrams (CLDs) we explore how CE strategies are related to territorial dynamics and how the outcomes of such strategies can support sustainable development. We first reviewed the literature, to identify the main drivers of change (i.e. feedback loops) triggered by CE interventions. We then applied the same systemic approach to six case studies across Europe in synergy with ESPON CIRCTER project. This allowed us to review, validate and improve the general systemic approach and further explore the role that specific territorial characteristics can play in the identification, selection and effective implementation of CE interventions. We find that some of the feedback loops emerged from the case studies are not found in the literature. On the one hand, new balancing loops have emerged, representing localized challenges to the implementation of CE strategies. On the other hand, new dynamics related to behavioural change have also emerged, which lead to self-reinforcing mechanisms in the case studies analysed, creating a stronger will for the implementation of CE interventions. The main result of our research is comprehensive CLD that can be used to assess and compare different CE strategies, fully considering the complexity of the CE and its various outcomes across social, economic and environmental indicators.

The hybridization of power systems offers the low-emission and energy-efficient marine vessels. However, it increases the complexity of the power system. Design, analysis, control, and optimization of such a sophisticated system require reliable and efficient modeling tools to cover a wide range of applications. In this work, a typical DC hybrid power system model is developed using a bond graph modeling approach. Necessary component models of varying degrees of fidelity are developed and integrated to build a system model with reasonable accuracy. The developed system model, along with the rule-based energy management system, is used to simulate the entire system and to investigate the load-sharing strategies as well as the system stability in various operating scenarios. Moreover, the simulation results are validated with experimental results conducted on a full-scale laboratory setup of the DC hybrid power system and with a ship load profile. The results show that the system model is capable of capturing the fundamental dynamics of the real system. The hybrid power system model is further used to analyze the bus voltage deviation from its nominal value. The computational efficiency presented by the system is fairly good as it can simulate faster than real-time. The developed system model can be used to build up a comprehensive simulation platform for different system analysis and control designs. © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

This article formulates a new type of synchronization problem for heterogeneous multiagent systems (MASs), called autonomous synchronization. In this problem, neither the synchronized agent dynamics nor the synchronized states are specified a priori , instead, they are autonomously determined by the inherent properties and the initial states of agents, thus providing an MAS with more degrees of adaptability and higher synchronization efficiency. To achieve autonomous synchronization, a novel dynamics update law and a synchronizing control law are proposed and the sufficient solvability conditions are explicitly revealed. The results are supported by rigorous theoretical analysis and numerical simulation, in both continuous-time and discrete-time settings.

Providing Infrastructure as a Service ( IaaS ) is becoming increasingly competitive due to its growing world market. IaaS requires significant physical resources, e.g., network connections, bandwidth, load balancers, and servers. IaaS providers of any size and reach, but especially private and public regional ones, have to manage the capacity of their resources properly to reduce costs and meet other business goals. Comprehensive capacity management by IaaS providers involves objective and subjective analyses and it is thus, challenging. Complexity arises, for instance, as one tries to factor management behavior in analyses of business impact and infrastructure performance to produce indicators in support of interventions related to service capacity. IaaS managers frequently rely on their own expertise, experience, and knowledge to identify whether an apparent capacity deficiency may be due to an infrastructure problem or to an unexpected demand burst. This article presents a business-driven simulation model based on System Dynamics (SD), Balanced Scorecard (BSC) and Analytic Hierarchy Process (AHP) concepts to support capacity management decisions by IaaS providers in real-life operating scenarios. A case study in a real-world, public regional IaaS provider illustrates the model’s support to non-trivial decisions that are aligned with managers’ experiences and business expectations.

This paper presents a systematic methodology to optimise the geometry of a three-body hinge-barge wave energy converter, to maximise the energy extraction of the device in given sea states and in site-specific wave climates. To that end, a 5-degree-of-freedom mathematical model is proposed to describe the system dynamics in two-dimensional space and a two-layer optimisation is designed to find the optimal design and control variables. The inner-layer optimisation is used to find the optimal control parameters of the power take-off system and the outer-layer optimisation, which uses a genetic algorithm, is employed to find the optimal design parameters of barge lengths and of optimal ballast positioning. In the case study, this methodology is applied to a 1:20 scale prototype of the McCabe Wave Pump device. Numerical results indicate that the optimal dimensions of the device, under given sea states, can be found efficiently and accurately, and that there appears to be no obvious benefit in the use of three barges, over a two-barge system.

There are many external factors influencing the development of the banking industry, but few literatures discuss the effects of multiple factors on the banking industry. In order to better promote the healthy and stable development of China's banking industry, this paper builds a simulation model based on system dynamics and uses Vensim PLE to systematically discuss the impact of money supply, supervision level, financial innovation, and GDP on bank profits. The simulation results find that money supply, financial innovation, and regulatory levels have a negative effect on profits, but GDP can promote profits.

A novel dynamic modeling methodology for meteorological sensitive load forecasting of smart grids is proposed by using Fisher information theory and online support vector regression (OSVR) technology to improve load prediction accuracy in this paper. According to the changes in the operating state of the smart grids and streaming data characteristics, the OSVR model is employed to implement accurate online training algorithms to avoid retraining the entire training data whenever a sample is added to or removed from the training set. On the other hand, the paper originally utilizes Fisher Information theory to address the introduction of weather factors into the meteorological sensitive load prediction model and feature selection for it. We also present a practical and concise implementation of the proposed methodology. We demonstrate the application of the proposed methodology in the meteorological sensitive load prediction for the local utilities in different periods and compared it with the traditional method. The results indicate that the forecast model constructed by the proposed methodology can obtain the superior prediction performance among the conventional SVR models.

In this paper, a new speed control algorithm for a permanent magnet DC motor which does not require implementation of the angular speed sensor is presented. Three steps are performed to develop the control system: design of speed tracking control algorithm assuming the speed measurement; design of speed observer; design of sensorless speed control algorithm based on the principle of separation. Information about speed is taken from the speed observer using the motor current value. The stability of the composite system dynamics consisting of three subsystems (the speed regulation loop, current regulation loop, and speed observer) is analyzed. The feedback gains tuning procedure for decoupling of three subsystems is given. The simulation results show that the dynamic performance of the designed system is similar to the performance of the system with angular speed measurement. The resulting closed-loop system has structural robustness properties with respect to parametric and coordinate disturbances. References 12, figures 2.

In this paper, the design of a fractional-order hyperbolic adaptive neuro-fuzzy backstepping sliding mode controller (HANFBSMC) has been addressed for a class of fractional-order chaotic systems with time-varying delays in their states, control inputs, disturbances and uncertainties. In the proposed controller, adaptive rules are used both in a neuro-fuzzy estimator to estimate the unknown system dynamics, and in updating uncertainty bounds of system. The robust part of the proposed hybrid controller includes backstepping sliding mode controller, in which, the hyperbolic tangential fractional-order sliding surfaces are employed to prevent large tracking errors. Employing backstepping control strategy also extends the flexibility of controller to deal with higher order systems and under more extensive design issues. Adaptive rules based on Lyapunov stability analysis are also employed for tuning of relating robust control parameters according to the estimated upper bounds of the uncertainties. Analysis of stability of this controller has been performed via Lyapunov–Krasovskii theorem and Barbalat's lemma. Besides, finite time reaching to sliding surfaces has been proved. Finally, out performance of the proposed controller has been reflected via stabilization of a fractional-order hyper-chaotic system with time varying delays in its states as well as fractional-order Chen system with time delay in its inputs and states, where both systems experience unknown uncertainties and disturbances.

The Covid-19 pandemic has spawned numerous dynamic modeling attempts aimed at estimation, prediction, and ultimately control. The predictive power of these attempts has varied, and there remains a lack of consensus regarding the mechanisms of virus spread and the effectiveness of various non-pharmaceutical interventions that have been enforced regionally as well as nationally. Setting out in data available in the spring of 2020, and with a now-famous model by Imperial College researchers as example, we employ an information-theoretical approach to shed light on why the predictive power of early modeling approaches have remained disappointingly poor.

The recycling of secondary resources is complicated as consumers, recyclers and governments are all involved in this process. In developing countries, compared to legal recyclers, illegal recyclers not only have cost advantages but also create serious pollution. Inappropriate management policies may cause disorder in the recycling market or inefficient fiscal management. This paper takes China’s lead-acid batteries (LABs) from 2000 to 2015 as an example to construct a model of a secondary resource recovery system based on heterogeneous groups and analyzes the environmental and economic impacts of used LAB recycling. By simulating the implementation of different tax cuts, subsidies and regulatory policies by the government in each year, we observe the overall environmental impact of the recycling industry. This study finds that when the total amount of government expenditures is limited, the optimal policy combination emphasizes tax reduction and subsidy policies during the growth period of the recycling industry and uses more funds for supervision in the mature period. Under the balance of fiscal revenues and expenditures, the optimal policy combination can reduce the number of illegal recycling companies and waste lead emissions from the recycling market by 97.9% and 45.8%, respectively. Compared to research conducted using mathematical models and system dynamics models, this result is more in line with the actual situation, and the content is more intuitive. The government needs to adopt different policy combinations in different periods according to the state of the recycling market so that the recycling of secondary resources can achieve the optimal effect.

This paper presents tailor-made neural model structures and two custom fitting criteria for learning dynamical systems. The proposed framework is based on a representation of the system behavior in terms of continuous-time state-space models. The sequence of hidden states is optimized along with the neural network parameters in order to minimize the difference between measured and estimated outputs, and at the same time to guarantee that the optimized state sequence is consistent with the estimated system dynamics. The effectiveness of the approach is demonstrated through three case studies, including two public system identification benchmarks based on experimental data., Comment: arXiv admin note: text overlap with arXiv:1911.13034

In this study, an adaptive neuro-observer-based optimal control (ANOPC) policy is introduced for unknown nonaffine nonlinear systems with control input constraints. Hamilton–Jacobi–Bellman (HJB) framework is employed to minimize a non-quadratic cost function corresponding to the constrained control input. ANOPC consists of both analytical and algebraic parts. In the analytical part, first, an observer-based neural network (NN) approximates uncertain system dynamics, and then another NN structure solves the HJB equation. In the algebraic part, the optimal control input that does not exceed the saturation bounds is generated. The weights of two NNs associated with observer and controller are simultaneously updated in an online manner. The ultimately uniformly boundedness (UUB) of all signals of the whole closed-loop system is ensured through Lyapunov’s direct method. Finally, two numerical examples are provided to confirm the effectiveness of the proposed control strategy.

In this paper, a novel scheme for the tracking problem of nonlinear systems is proposed. First, as a new technology of neural network in control field, linear differential inclusion is used to approximate the nonlinear term for the entire system. Based on the equivalent linear system, tracking reference signal is given and a new augmented system is built. According to the mentioned value function, two reinforcement learning algorithms are proposed to design the optimal control law. Notice that the online algorithm does not involve the system dynamics and tracking dynamics. In the simulation section, the model of trolley system is given to prove the effectiveness and accuracy of the scheme proposed in this paper.

This paper aims to explore the impact of government subsidy policies on strengthening the shared-bikes industry in China by simulating the operation mode of Mobike using system dynamics methodology. First, we introduce four subsidy policies: equalization subsidy, stage subsidy, growth subsidy, and back-slope subsidy, and establish a system dynamics model to characterize the bike-sharing operation dynamics system considering these four government subsidies. Subsequently, we analyze the impact of the four subsidy policies on enterprise-operating activities. The simulation results indicate that different subsidy policies have different incentive objectives and characteristics. The back-slope subsidy has more positive effects on enterprise operations in the short term but requires higher cost. The influence of growth subsidy and stage subsidy on enterprises can last longer, which requires the government to timely adjust the subsidy amount. The impact of equalization subsidy on enterprises is more stable, reducing government subsidy while ensuring enterprises’ regular operation.

To study the electromagnetic dynamics of grid-connected voltage source converter (VSC)-based devices during asymmetrical grid fault ride-through, this article presents a generic dynamic modeling method for asymmetrical-faulted power grids. In the generic case, the asymmetrical impedances and the grounding inside a faulted grid introduce complicated couplings among the harmonic-contained sequence components. By employing complex vectors, the dynamics of each sequence, especially that of zero sequence, can be depicted clearly and the complicated couplings can be decomposed. With dynamic composite sequence networks figured out, the characteristics of asymmetrical-faulted grids can be studied further. The presented modeling approach is validated by simulation, and its effectiveness is demonstrated by a case study of small-signal stability analysis for an exemplified grid-connected VSC injecting positive and negative sequence reactive currents to the faulted grid.

Model-based design (MBD) in systems engineering is a well-accepted technique to abstract, analyze, verify, and validate complex systems. In MBD, we design a mathematical model of the system to virtually execute and test systems via model simulations to understand the system dynamics better. Computing model simulations has their challenges; one is to ensure that the simulation trajectory preserves the model semantics. Besides, computing many simulation trajectories over a long time-horizon must be time efficient for rapid respond to system engineers. In this work, we address these challenges in simulating models of cyber-physical systems (CPS), particularly systems possessing mixed discrete–continuous dynamics. We focus on the subclass of CPS’s hybrid automata models, where Jump predicates are restricted to polygonal constraints and present a numerical simulation engine that can efficiently compute many random simulations in parallel by exploiting the parallel computing capability in modern multicore processors. Our simulation engine implements a lock-free parallel breadth-first-search (BFS)-like algorithm and is implemented in the model-checking tool XSpeed. In addition, an application of our simulation engine in property verification of CPS models has been illustrated on two benchmarks. Some model coverage metrics have been defined that users of the tool can specify to set the desired thoroughness of testing with simulations. We demonstrate the performance gains of our simulation engine over SpaceEx and CORA, the modern model checkers and simulators for affine hybrid systems.

International audience; Implementing circular economy (CE) practices can lead to both environmental savings and competitive advantages for companies. While transitioning from a linear production system to a closed-loop system is not straightforward, adequate methodology and tools can support industrialists in this sustainable shift. This paper proposes a multi-tool approach to systematically identify, classify, and assess the contribution of influence parameters and action levers to close the loop on products and key materials. Industrial ecology and model-based engineering tools are combined to ensure a systemic analysis and evaluation. The developed multi-tool approach combines, in a stepwise methodology, material flow analysis, fuzzy cognitive mapping, structural analysis, and system dynamics, to model and qualify the impact of potential and promising CE strategies. To illustrate each step of this multi-tool methodology, a case study is carried out on a real-world industrial product: a catalytic converter, which contains a non-negligible amount of platinum, considered as a critical raw material by the European Commission. New insights to close the loop on platinum from catalytic converters are thus provided and discussed. Notably, the connections between key action levers to close the loop on platinum are identified and highlighted, including regulations to limit the number of exports, mandatory recycling and reuse rate, end-users behaviors, based on regulatory constraints and financial motivations, and platinum price fluctuation. These findings could help to generate and fine-tune an ad hoc system dynamics model to evaluate the impact of key action levers through more specific scenarios. For instance, the broader implication of this multi-tool methodology could support an original equipment manufacturer in the evaluation of potential CE strategies, through the simulation of selected action levers on the circularity and sustainability performance of their value chain. Ultimately, it could provide quantitative insights to relevant prospective questions, such as, what if a take-back scheme is proposed to augment the collection rate by a given percentage, or what if the design is modified for easy disassembly.

In the context of learning from demonstration (LfD), trajectory policy representations such as probabilistic movement primitives (ProMPs) allow for rich modeling of demonstrated skills. To reproduce a learned skill with a real robot, a feedback controller is required to cope with perturbations and to react to dynamic changes in the environment. In this letter, we propose a generalized probabilistic control approach that merges the probabilistic modeling of the demonstrated movements and the feedback control action for reproducing the demonstrated behavior. We show that our controller can be easily employed, outperforming both original controller and a controller with constant feedback gains. Furthermore, we show that the proposed approach is able to solve dynamically changing tasks by modeling the demonstrated behavior as Gaussian mixtures and by introducing context variables. We demonstrate the capability of the approach with experiments in simulation and by teaching a 7-axis Franka Emika Panda robot to drop a ball into a moving box with only few demonstrations.

Oxygen therapy plays a vital role to recover a patient from severe hypoxia as well as to minimize the risk of hypoxia in a critical situation. Based on this therapeutic technique, this article presents an application of backstepping control for the oxygenation in a cardiopulmonary system. A nonlinear multi-compartment system with unknown hysteresis is used as a human model in this study. With no a priori knowledge of the underlying system dynamics, a radial basis function (RBF) network is integrated into a closed-loop subsystem and trained to identify the unknown nonlinear functions. Consequently, a backstepping controller is designed based on the Lyapunov stability theorem for regulating oxygenation. The theoretical framework and simulation are presented and demonstrated in terms of stability and control performance under the presence of simulated physiological changes, possibly caused by pathophysiological effects in the cardiopulmonary system i.e. critically ill patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

In this brief, a new disturbance compensator based switching function is proposed for disturbed discrete-time systems. A sliding mode controller is developed based on this switching function. A high order disturbance compensator is embedded in the new switching function to achieve accurate disturbance rejection. Rigorous analysis on system dynamics of the corresponding closed-loop system is performed where it is shown that the proposed switching function is invariant to disturbances and has the ability to achieve the ideal quasi-sliding mode (QSM). In other words, the proposed method enables the convergence of the system trajectory and keeps it on the sliding surface independent of disturbances, which is rarely achieved in the discrete-time sliding mode control (DSMC). Moreover, the presented method is capable of delivering a ${\mathcal O}$ ( hn ) control accuracy of the system states, which is much higher than most previous methods. Simulation results on a MAGnetic LEViation system illustrate the main characteristics and performance of the proposed method.

Nowadays, in the context of the coronavirus crisis, the issue of ensuring the sustainable development of heavy industries is acute. However, theoretical and analytical researches alone are not sufficient for this, and economic science needs to develop fundamentally new approaches to the study of the development of industrial sectors. This article is devoted to the creation and testing of a simulation model for the development of individual sectors of the economy. The object of research is the metallurgical industry, as well as related ore mining, mechanical engineering and production of finished metal products. The theoretical basis of the research is a systematic approach that combines the theory of industry markets, economic growth, industrial economics, system dynamics and mathematical economics. The main research methods used are system analysis, statistical analysis to identify trends in changes in the main economic indicators, econometric modeling to build production functions, as well as mathematical modeling of macroeconomic systems. As a result, a simulation model developed in system dynamics notation is proposed, which makes it possible to evaluate the development of individual industries taking into account various changes. This model is built on the basis of the three-sector model of the national economy, where separate adjacent industries connected by dynamic feedback loops are identified as structural elements. The paper details the structure of the simulation model based on first-order dynamic equations, balance equations and nonlinear production functions. The simulation model allowed us to predict a number of scenarios for the development of metallurgical industries, taking into account changes in the labor force and investment in fixed assets. The results of the work can be used for forming proposals on industrial policy, monitoring the condition and efficiency of individual industries. © 2021 National Research University, Higher School of Econoimics. All rights reserved.

The complex and changeable external social and economic environment has a significant impact on the sustainable development of the closed-loop supply chain In particular, the occurrence of uncertain emergencies increases the risk of interruption of the closed-loop supply chain, making it insufficient to analyze its complex changes from the perspective of material flow alone Based on this analysis, the paper constructs a closed-loop supply chain material flow and capital flow coupling system composed of manufacturers, sellers and recyclers to explore the impact of material flow sudden interruption on the closed-loop supply chain system when an uncertain emergency occurs In this paper, based on the closed-loop supply chain system coupled with logistics and capital flow, a system dynamics simulation model was established by using Vensim simulation software to analyze the impact of COVID-19 epidemic on manufacturers, sellers and recyclers under five scenarios The results show that when COVID-19 outbreaks occur, the material flow of each main enterprise in the closed-loop supply chain is more easily influenced than the capital flow At the same time, it can be found that the recyclers in the main enterprises of the closed-loop supply chain are more easily influenced by the material flow The model constructed in this paper has applicability and can be used for related studies of closed-loop supply chain under other emergencies, but the scene design should be carried out according to the characteristics of emergencies themselves © 2021 Production Engineering Institute All rights reserved

Purpose The purpose of this paper is to study the preload range of tendon-driven manipulator and the relationship between preload and damping. The flexible joint manipulator (FJM) with joint flexibility is safer than traditional rigid manipulators. A FJM having an elastic tendon is called an elastic tendon-driven manipulator (ETDM) and has the advantages of being driven by a cable and having a more flexible joint. However, the elastic tendon introduces greater residual vibration, which makes the control of the manipulator more difficult. Accurate dynamic modeling is effective in solving this problem. Design/methodology/approach The present paper derives the relationship between the preload of the ETDM and the friction moment through the analysis of the forces of cables and pulleys. A dynamic model dominated by Coulomb damping is established. Findings The linear relationship between a decrease in the damping moment of the system and an increase in the ETDM preload is verified by mechanics analysis and experiment, and a curve of the relationship is obtained. This study provides a reference for the selection of ETDM preload. Originality/value The method to identify ETDM damping by vibration attenuation experiments is proposed, which is helpful to obtain a more accurate dynamic model of the system and to achieve accurate control and residual vibration suppression of ETDM.

Such a timely phenomenon in the modern world as digitalization can become a tool for achieving sustainable development goals. But it is new, and its benefits and threats are not well understood. This problem can be partially solved by creating economic and mathematical models for assessing the impact of digitalization on sustainable development. Nonetheless, most of the existing models in this field are devoted to defining the impact of digitalization on the economic aspects of countries' activities, and environmental ones are either ignored or presented approximately, with significant abstraction. Despite this, the objective of the paper is to develop and implement an economic and mathematical model, which in a generalized form can be used for different countries of the world, subject to its certain adaptation and detailing of national indicators. This approach allows to take into account the difference in the social and economic situation of countries and levels of their digitalization, which makes it possible to draw more valid conclusions based on the results of estimations. The proposed model is built on the basis of the system dynamics method, which takes into account the path-dependence, and is implemented on the example of Ukraine. With its help, two computational experiments were carried out: an inertial one (a forecast for 5 years, provided that all the current patterns of digitalization of the country's economy are preserved) and a scenario, in which patterns of investing in digital capital in Ukraine change (to the patterns of European countries – Spain and Hungary), while maintaining the rest conditions unchanged. Using this model, it was defined that digital equipment and technologies as part of the environmental capital of the Ukrainian industry, as well as non-digital equipment and technologies, have an extremely small impact on reducing energy consumption and do not contribute to a significant reduction in the air pollution. Provided that the current situation persists (the inertial scenario), emissions of pollutants into the air in 2024 may even grow by 0.8% compared to 2019. During the experiment on investment patterns’ replacement in Ukraine with the patterns of Hungary and Spain, it was found that despite the change in the nature and (in the case of Hungary) the direction of investments in digital equipment and technologies, which significantly affected their amount (both in manufacturing and environmental capital), while the rest conditions for the functioning of industry remain unchanged, the indicators of value added, energy consumption, employees’ sickness rate and air pollution level stay practically the same as before such a replacement. Therefore, the "blind" copying of the digitalization practices of other countries, while maintaining unchanged other conditions, without taking into account the peculiarities of the national institutional environment, the degree of development of science and technology, is inappropriate, since it does not lead to an improvement in the situation in Ukraine. The general conclusion is that digitalization by itself is not able to reverse the current unfavorable trends in development of Ukraine for the better. It is necessary to achieve fundamental changes in the growth of the real sector of the economy on an innovative basis, in the amount and structure of investments, in the propensity of economic entities to invest, which is now at a low level, and also to form an integral industrial-digital ecosystem, similar to European ones, but taking into account the heterogeneity of European economies and their experience, as well as the characteristics of the current state and dynamics of development of the technical, technological and institutional environment of Ukraine.

The paper proposes an effective nonlinear integral backstepping approach strategy (IBS) through the use of a new adaptive power control algorithm for variable-speed wind turbine generators (VS-WTGs) to optimize the power extracted from the WTGs. The suggested approach is applied to one of the most frequently used maximum power control methods called as tip speed ratio (TSR) during partial-load operation considering the effects of the variations in wind velocity profile and unmodeled system dynamics such as external disturbances. The proposed method of control is known to scale-back the mechanical stress at the level of the GT shafts (generator and turbine). Furthermore, the IBS approach is relatively simple, which significantly reduces the online computational time and cost. Some numerical simulation results prove, under different wind speed models, that the proposed AC-TSR-IBS method works well on the level of the improved efficiency and the rapid system response compared to conventional TSR-PI method.

The frequent occurrence of various sudden natural disasters in the world has caused heavy losses to human beings. It is very important to forecast the demand of emergency materials in order to protect people's safety and property. The purpose of this study is to use IACO-BP algorithm to forecast the demand of emergency supplies in the case of flood disaster. In this study, the flood disaster situation in recent years published by the Ministry of Water Resources of China is selected as the experimental data set. The data are sorted and analyzed by a variety of theoretical comprehensive operation, qualitative and quantitative research method, analytic hierarchy process and system dynamics method. The improved ant colony optimization algorithm is used to model the emergency material demand, and the population, flood level, flood level of the disaster area are analyzed. As the network input, the material situation outputs the material demand, so as to forecast. The results show that the iteration times of IACO-BP algorithm are 11 and the running time is 3S. The fluctuation of IACO-BP algorithm is the least and the most stable among the three algorithms. The material satisfaction degree predicted by IACO-BP algorithm is improved by 15% from the original 80.9%. It is concluded that this algorithm is very accurate and efficient in the prediction of emergency material demand, which can better assist the disaster situation. This study contributes to the prediction of emergency material demand for emergency disaster.

Construction project managers usually involve with selecting the most suitable subcontractors (SCs) to improve the overall quality of the construction process. In this paper, a system dynamics-base...

This article investigates the impact of phase-locked loop (PLL) systems along with virtual inertia on system dynamics of a parallel AC/HVDC tie-line integrated two-area automatic generation control...

This brief proposes an invariant manifold based output-feedback sliding mode control (SMC) strategy for systems with mismatched disturbances to achieve asymptotic tracking and disturbance rejection. Different from the existing output-feedback SMC methods, the invariant manifold is employed to transform the multiple mismatched disturbances into matched ones, which can provide full system dynamics for controller design to improve accuracy. An observer is developed to estimate unmeasurable states, then an output-feedback sliding mode controller is proposed. Moreover, the switching gain of the controller adaptively changes with the estimation error, which reduces chattering in SMC. Experiments on a converter-driven DC motor system verify the superiority of the proposed method.