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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 focuses on properties of equilibria and their associated regions of attraction for continuous-time nonlinear dynamical systems. The classical Poincare–Hopf Theorem is used to derive a general result providing a sufficient condition for the system to have a unique equilibrium. The condition involves the Jacobian of the system at possible equilibria, and ensures the system is infact locally exponentially stable. We apply this result to the susceptible-infected-susceptible (SIS) networked model, and a generalised Lotka–Volterra system. We use the result further to extend the SIS model via the introduction of decentralised feedback controllers, which significantly change the system dynamics, rendering existing Lyapunov-based approaches invalid. Using the Poincare–Hopf approach, we identify a necessary and sufficient condition under which the controlled SIS system has a unique nonzero equilibrium (a diseased steady-state), and monotone systems theory is used to show this nonzero equilibrium is attractive for all nonzero initial conditions. A counterpartcondition for the existence of a unique equilibrium for a nonlinear discrete-time dynamical system is also presented.

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.

Reductions in defense expenditure require holistic and coordinated planning of two critical and interconnected defense capabilities, namely a fleet of assets and the workforce required. In this paper, we model and solve a joint problem of strategic workforce planning and fleet renewal in a military context. The joint problem studied involves addressing a trade-off among several costs (e.g., workforce, maintenance, operating, etc.) and operational readiness (availability) of the fleet. To make such trade-offs, the decisions associated with workforce planning (i.e., the recruitment and the career progression of the workforce) and fleet renewal (i.e., the timing of asset replacements) strategies have to be simultaneously considered and optimized. We develop a simulation-optimization approach by coupling a system dynamics (SD) simulation model and a genetic algorithm (GA) to solve the joint problem. In the developed approach, the GA generates candidate workforce planning and fleet renewal strategies to find the best joint strategy. Then, the candidate workforce planning and renewal strategies are passed to the SD model which simulates both the career progression of the workforce and the life-cycle of assets to evaluate the total cost. We illustrate the applicability and effectiveness of the joint model on a realistic case study motivated by the recent modernization efforts of the Royal Australian Navy. The results obtained indicate that this approach leads to a considerable cost reduction and identifies the causes of inferior performance. We also test the robustness of the optimized strategies under uncertainty by sensitivity and scenario discovery analyses to infer further insights.

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.

For accurate Finite Element (FE) modeling for the structural dynamics of aeroengine casings, Parametric Modeling-based Model Updating Strategy (PM-MUS) is proposed based on efficient FE parametric modeling and model updating techniques regarding uncorrelated/correlated mode shapes. Casings structure is parametrically modeled by simplifying initial structural FE model and equivalently simulating mechanical characteristics. Uncorrelated modes between FE model and experiment are reasonably handled by adopting an objective function to recognize correct correlated modes pairs. The parametrized FE model is updated to effectively describe structural dynamic characteristics in respect of testing data. The model updating technology is firstly validated by the detailed FE model updating of one fixed–fixed beam structure in light of correlated/uncorrelated mode shapes and measured mode data. The PM-MUS is applied to the FE parametrized model updating of an aeroengine stator system (casings) which is constructed by the proposed parametric modeling approach. As revealed in this study, (A) the updated models by the proposed updating strategy and dynamic test data is accurate, and (B) the uncorrelated modes like close modes can be effectively handled and precisely identify the FE model mode associated the corresponding experimental mode, and (C) parametric modeling can enhance the dynamic modeling updating of complex structure in the accuracy of mode matching. The efforts of this study provide an efficient dynamic model updating strategy (PM-MUS) for aeroengine casings by parametric modeling and experimental test data regarding uncorrelated modes.

Lunar resources is one of the many new putative business models that may transform space logistics. Yet it competes with Earth-based resources, in a complex trade-off involving both tech development & socioeconomic dynamics. This study models the size vs. time of a future resource ecosystem focused on water for exploration and satellite refueling, in cis-lunar space. We use a recently-developed multi-methodology concept based on System Dynamics and scenario planning, characterizing uncertainties. Top critical uncertainties include the accessibility of resource finds, and government investment into lunar resources, demarcating 2 particularly illustrative scenarios: Moonopolis and Apollo 2.0 - a rosy and a low-resources future. Concurrently, a System Dynamics model with 7 interacting systems is developed: exploration, production, demand, satellite industry, R&D, natural resources, and government. It is based on models of other industries (e.g. oil) and can express the scenarios. Uncertainties in 25 variables are estimated, and sensitivity analysis of ecosystem size is performed globally using variance-based measures, including interaction effects. These show (a) systems are tightly coupled, (b) variable importance is sensitive to the baseline. Three variables are crucial: government support to production development, production firms’ re-investment, and growth of the GEO telecom satellite industry. A lunar resources ecosystem with $32B economic impact after 20 years is plausible, given excellent government support to production capacity, high growth in GEO satellites, early demand and large initial resource discoveries. The main contributions are a novel holistic model of the dynamics of a space resources industry showing how to mix technical & socioeconomic parts, and a first case study of the methodology.

This letter discusses the eco-driving problem, considering both electric and conventional powertrains, and presents a pathway to solving it numerically using an indirect collocation method. Despite the low-order system dynamics, the piecewise fuel/efficiency map, gear shifting, and real-world traffic/road situations bring system discontinuities/switchings and pure state constraints into the problem formulation, which make the problem highly nonlinear and nontrivial to solve. This letter introduces smooth approximations to convert the original problem to an unconstrained (and penalized) smooth boundary-value problem. This approach eliminates the discussion of the switching structure and leads to a lightweight Newton-method-based solution procedure.

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.

The motivation for this work stems from the problem of scheduling requests for flow at supply points located throughout an automated network of open-water channels. The off-take flows are rigid-profile inputs to the system dynamics. In particular, the channel operator can only shift orders in time to satisfy constraints on the automatic response to changes in the load. This leads to a nonconvex semi-infinite programming problem, with a sum-separable cost that encodes the collective sensitivity of end-users to scheduling delays. The constraints encode the linear time-invariant continuous-time dynamics and limits on the state across a continuous scheduling horizon. Discretization is used to arrive at a more manageable approximation of the semi-infinite program. A method for parsimoniously refining the discretization is applied to ensure continuous-time feasibility for solutions of the approximate problem. It is then shown how to improve the cost without loss of feasibility. Supporting analysis is provided, along with simulation results for a realistic irrigation channel setup to illustrate the 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.

Accurate simulations of wastewater quality and quantity, particularly in saline and semi-arid areas, are important in agricultural water management. In this study, a system dynamics (SD) approach was proposed to simulate drainage water and groundwater salinities, water table fluctuation, and drainage discharge at field scale. The results of the SD approach were compared with results from DRAINMOD-S, a computer simulation model. For model validation, earlier experimental data from two field units were used. The field units each contained three rows of piezometers. During irrigation, daily water table fluctuation, drainage discharge, irrigation and drainage water salinity, and the salinity in each piezometer, were measured. The SD approach simulated these parameters more accurately than DRAINMOD-S for both units. HIGHLIGHTS The SD approach and DRAINMOD-S model are proposed to simulate drainage water and groundwater salinities, water table fluctuation, and drainage discharge at the field scale.; For model validation, experimental data collected from farms are used.; Adjustment between measured and predicted values, using statistical parameters.; Results showed that the SD approach simulated more accurately than the DRAINMOD-S model.

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.

This paper deals with system dynamics approach for dynamical behaviors and control synthesis of supply chain system by utilizing three-stage production-distribution model. The presented approach offers systematic tools for determining fundamental relationships between multi-echelons in the supply chain dynamics by using eigenvalues, bifurcation, and time history investigation. By exploring system dynamics on time series analysis, it is found that system performance has suffered severely from the bullwhip effect under impacts of model uncertainties and perturbed demand. The novel fractional-order sliding mode control algorithm has been presented based on adaptation mechanism, ensuring that the shipment flows are robustly stable in supply chain networks against disruptions. This is a smarter way of getting sufficient strength to sustain existing competitive market for mitigating the risks and improving the supply chain performance. The system stability has been thoroughly analyzed by using Routh-Hurwitz criterion and Lyapunov theory. Extensive numerical simulations have been conducted to obtain insights into the system behaviors and to validate effectiveness of active control policies by matching the shipment sent to customer demand, ensuring supply chains resilience. Finally, it is found that the presented approach can help decision-makers develop more efficient supply chain management system against severe market disruptions.

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.

Learning high-performance deep neural networks for dynamic modeling of high Degree-Of-Freedom (DOF) robots remains challenging due to the sampling complexity. Typical unknown system disturbance caused by unmodeled dynamics (such as internal compliance, cables) further exacerbates the problem. In this letter, a novel framework characterized by both high data efficiency and disturbance-adapting capability is proposed to address the problem of modeling gravitational dynamics using deep nets in feedforward gravity compensation control for high-DOF master manipulators with unknown disturbance. In particular, Feedforward Deep Neural Networks (FDNNs) are learned from both prior knowledge of an existing analytical model and observation of the robot system by Knowledge Distillation (KD). Through extensive experiments in high-DOF master manipulators with significant disturbance, we show that our method surpasses a standard Learning-from-Scratch (LfS) approach in terms of data efficiency and disturbance adaptation. Our initial feasibility study has demonstrated the potential of outperforming the analytical teacher model as the training data increases.

The significant challenges seen with the mathematical modeling and control of spatial parallel manipulators are its difficulty in the kinematic formulation and the inability to real-time control. The analytical approaches for the determination of the kinematic solutions are computationally expensive. This is due to the passive joints, solvability issues with non-linear equations, and inherent kinematic constraints within the manipulator architecture. Therefore, this article concentrates on an artificial neural network–based system identification approach to resolve the complexities of mathematical formulations. Moreover, the low computation time with neural networks adds up to its advantage of real-time control. Besides, this article compares the performance of a constant gain proportional–integral–derivative (PID), variable gain proportional–integral–derivative, model predictive controller, and a cascade controller with combined variable proportional–integral–derivative and model predictive controller for real-time tracking of the end-effector. The control strategies are simulated on the Simulink model of a 6-degree-of-freedom 3-PPSS (P—prismatic; S—spherical) parallel manipulator. The simulation and real-time experiments performed on the fabricated manipulator prototype indicate that the proposed cascade controller with position and velocity compensation is an appropriate method for accurate tracking along the desired path. Also, training the network using the experimentally generated data set incorporates the mechanical joint approximations and link deformities present in the fabricated model into the predicted results. In addition, this article showcases the application of Euler–Lagrangian formalism on the 3-PPSS parallel manipulator for its dynamic model incorporating the system constraints. The Lagrangian multipliers include the influence of the constraint forces acting on the manipulator platform. For completeness, the analytical model results have been verified using ADAMS for a pre-defined end-effector trajectory.

Volumetric efficiency (VE) is an extremely important index to evaluate the performance of pump and motor. Efforts have been concentrated on the causes and character of leakage loss, but few quantitative analyses of pump kinematic clearances (KCs) identification and nonlinear compression loss caused by air content are discussed. This paper clarifies the nonlinear compression loss resulted from the air content of the oil in detail and proposes an improved flow loss model of the swash-plate axial piston pump (SAPP). Then, combining the improved model with experimental results, the KCs of SAPP are identified. It has been verified that a reasonable sampling path is useful for reducing sampling numbers and guaranteeing identification accuracy. Finally, the identification and quantization of the wear fault of SAPP are realized by analyzing the estimated value of KCs. The proposed method is simple and easy to implement and shows high predictive accuracy. It provides a possible and promoting technology support for the autonomous calibration of the system parameters and the active compensation of the system control.

Purpose This study aims to propose a hybrid simulation approach for site layout and material laydown planning in construction projects considering both the project’s continuous and discrete state. Design/methodology/approach Efficient site layout planning (SLP) is a critical task at the early stages of the project to enhance constructability and reduce safety risks, construction duration and cost. In this paper, external and internal conditions affecting SLP gets identified. Then dynamic features of project conditions and project operations are analyzed by using a hybrid simulation approach combining continuous simulation (CS) and discrete event simulation (DES). Findings An efficient site layout plan regarding the project conditions results in cost efficiency. Instead of using DES or CS alone, this paper uses a hybrid simulation approach. Such a hybrid method leads to more accurate results that enable construction managers to make better decisions, such as material management variables. The proposed approach is implemented in a real construction project (i.e. earthmoving operation) to evaluate the hybrid simulation approach’s performance. Practical implications The proposed approach is implemented in a real construction project (i.e. earthmoving operation) to evaluate the performance of the hybrid simulation approach. Originality/value Although DES is used widely in construction simulation, it involves some limitations or inefficiencies. On the other hand, modeling resource interactions and capturing the construction project’s holistic nature with CS or system dynamics face some challenges. This study uses a hybrid DES and CS approach to enhance commercial construction projects’ SLP.

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.

This study intends to investigate a dynamic modeling and design of controller for a planar serial chain, performing 2-DoF, in interaction with a cable-driven robot. The under study system can be used as a rehabilitation setup which is helpful for those with arm disability. The latter goal can be achieved by applying the positive tensions of the cable-driven robot which are designed based on feedback linearization approach. To this end, the system dynamics formulation is developed using Lagrange approach and then the so-called Wrench-Closure Workspace (WCW) analysis is performed. Moreover, in the feedback linearization approach, the PD and PID controllers are used as auxiliary controllers input and the stability of the system is guaranteed as a whole. From the simulation results it follows that, in the presence of bounded disturbance based on Roots Mean Square Error (RMSE) criteria, the PID controller has better performance and tracking error of the 2-DoF robot joints are improved 15.29% and 24.32%, respectively.

This paper overviews the main results obtained over the past few years at the Department of Hydromechanical Systems Dynamics and Vibration Protection Systems, Institute of Technical Mechanics of the National Academy of Sciences of Ukraine and the State Space Agency of Ukraine, in the solution of current problems in the dynamics of liquid-propellant rocket engines (LPREs), liquid-propellant launch vehicle pogo stability, vibration protection system dynamics, the gas dynamics of aircraft gas turbine engine components, and the dynamics of hydraulic systems with cavitating elements. These results are as follows. A mathematical model of LPRE pump dynamics was developed. The model complements a hydrodynamic model of LPRE cavitating pumps by allowing a mathematical simulation of choking regimes. An approach was developed to the construction of a nonlinear mathematical model of LPRE hydraulic line filling. The approach allows one to automatically change, if necessary, the finite element partitioning scheme of a hydraulic line in the process of its filling during LPRE startup calculations. An investigation was conducted into the startup dynamics of a multiengine liquid-propellant propulsion system that consists of four staged-combustion oxidizer-rich LPRDs with account for the possibility of their nonsimultaneous startup. The maximum values of oxidizer and fuel pressure surges and undershoots at the liquid-propellant jet system (LPJS) inlet at an engine spartup and shutdown were determined and used in determining the LPJS operability at the startup and shutdown of the RD861K sustainer engine. The pogo stability of the Cyclone-4M launch vehicle was analyzed analytically using Nyquist’s criterion. A numerical approach was developed to characterizing acoustic oscillations of the combustion products in annular rocket combustion chambers with account for the configuration features of the fire space and the variation of the physical properties of the gaseous medium with the axial length of the chamber. A prototype vibration protection system was developed and made, and its dynamic tests confirmed its high efficiency in damping impact and harmonic disturbances. Approaches were developed to the aerodynamic improvement of aircraft gas turbine engine components. Topical problems in solids grinding in a liquid medium with the use a cavitation pulse technology were solved.

Reducing consumed power of a robotic machine has an essential role in enhancing its energy efficiency and must be considered during its design process. This paper deals with dynamic modeling and power optimization of a four-degrees-of-freedom flight simulator machine. Simulator cabin of the machine has yaw, pitch, roll and heave motions produced by a 4RPSP+PS parallel manipulator (PM). Using the Euler–Lagrange method, a closed-form dynamic equation is derived for the 4RPSP+PS PM, and its power consumption is computed on the entire workspace. Then, a newly introduced optimization algorithm called multiobjective golden eagle optimizer is utilized to establish a Pareto front of optimal designs of the manipulator having a relatively larger workspace and lower power consumption. The results are verified through numerical examples.

PurposeThe paper concludes with showing that in the most optimistic scenario, end-of-life (EOL) batteries will account for 86% of energy storage for wind and 36% for solar PV in 2040.Design/methodology/approachWith the growing demand for electric vehicles (EVs), the stock of discarded batteries will increase dramatically if no action is taken for their reuse or recycling. One potential avenue is to reuse them as energy storage systems (ESS) to mitigate the intermittent generation of renewable energy such as solar PV and wind. In a sense, the reliability for solar PV and wind energy can increase if energy storage systems become economically more attractive, making solar and wind systems more attractive through economies of scale.FindingsThe paper concludes with showing that in the most optimistic scenario, EOL batteries will account for 86% of energy storage for wind and 36% for solar PV in 2040.Originality/valueThe projection of scenarios can contribute to the information of policies, standards and identification of environmental promotion and promotion related to efficient management for EOL batteries.

Features of the electric power market functioning, in comparison with common properties of modern markets, are considered. It is shown that continuous reforms, which take place in many countries, are far from reaching effective solution. Sectoral peculiarities, connected with both technological complexity of the field and economic specificity of the market participants, form very special model of the market. The main way to increase effectivity of the market, to make it more competitive and, in the same time, socially responsible, is to use widely and deeply the modern information technologies, including technologies of artificial intelligence. A technological scheme (especially targeted on features of the electric power market) has been proposed in the article. First, the technologies merge methods of collection, storage, processing, and presentation of information. The collection of market data should be automated, particularly, by the so-called intellectual avatars, which operate in the market as virtual agents. A reliable distributed storage of data, based on blockchain technology, is chosen. The big data technologies of data structuring, storage, and processing are involved. Datamining methods include effective presentation of results in forms of graphs and diagrams. The second set of technologies includes core mathematical models and “digital twins.” They are aimed for data interpolation and extrapolation, in particular, for prediction of further system dynamics. The used neural networks have option of self-learning and self-development. At last, the third set is a set of user interfaces, which provides market actors by complete and adopted information and is organized in accordance with ideas of ergonomics.

PurposeWith the majority of highway projects in China having entered their operational phases, the maintenance and repair of the pavement is receiving increasing attention. One problem that needs to be addressed urgently is that of how to raise the proper funds for highway maintenance to ensure the sustainable operation of the project. To this end, the aim of this study is to investigate the capital demand for operation and maintenance of a project by means of a refinancing scheme, in order to reduce the possibility of project bankruptcy and to enhance the economic value of the project.Design/methodology/approachBased on an analysis of the dynamic complexity of the highway pavement maintenance system, a Markov model is used to predict pavement performance, and an optimal capital structure decision model is proposed for highway public–private partnership (PPP) project refinancing, using the method of system dynamics (SD). The proposed model is then applied to a real case study.FindingsResults show that the proposed model can be used to predict accurately the dynamic changes in the demand for road maintenance funds and refinancing during the period of operation, before making the optimal decision for the refinancing capital structure.Originality/valueAlthough many scholars have studied the optimal refinancing capital structure of PPP projects, the dynamic changes inherent in the demand for maintenance funds for highway PPP projects are seldom considered. Therefore, in the approach used here the influence of the dynamic change of road maintenance capital demand on refinancing is investigated, and SD is used for the optimal capital structure decision-making model of highway PPP project refinancing, to make the decision-making process more reasonable and scientific.

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.

With the rapid growth of technology and the advances in smart healthcare, patients nowadays struggle with finding suitable smart healthcare interfaces that offer the most effective and manageable personalized care. System dynamics (SD) investigates the behavior of various factors of a system and thus, can offer plausible solutions in this regard. This article presents the research ideas and conceptualization of a framework to determine the factors that influence the effectiveness of a personal healthcare response monitoring system from a systems engineering perspective. A SD model for smartphone-based healthcare monitoring is introduced. Specifically, smartphone-based heart monitoring using electrocardiogram (ECG) is studied in this article from a SD point of view. The model includes factors such as patient wellbeing and care, cost, convenience, user friendliness, in addition to other embedded ECG system design and performance metrics (e.g., accuracy, real-time response, etc.). The model has been rigorously tested and the simulation results reflect the dynamics of the model and the effectiveness of smartphone-based ECG monitoring in various scenarios. The proposed framework has the potential to facilitate visualizing the effectiveness of smartphone-based healthcare monitoring systems for users.

PurposeThis paper proposes a novel hybrid simulation approach that incorporates the structural equation model (SEM) and system dynamics (SD) to investigate the impacts of leadership dynamics on project performance under different scenarios.Design/methodology/approachSEM is used to identify and validate a correlation between leadership variables and project performance statically. On this basis, the SD model is constructed to depict a system model connecting the leadership and project performance. Different scenarios are simulated to dynamically model the variation in leadership on the evolution of project performance.FindingsResults indicate that (1) leadership can be divided into personal ability, relationship atmosphere and organizational strategy in complex construction projects; (2) personal ability, relationship atmosphere and organizational strategy positively correlate with project performance over time; and (3) L1 (stress management ability), L7 (team building) and L17 (institution support) are the leading factors influencing project performance and should be paid more attention under limited resources.Originality/valueThis research contributes to (1) the state of the knowledge by proposing a hybrid methodology that can systematically model the impacts of leadership dynamics on project performance over time and (2) the state of the practice by gaining a better understanding of the strategy of resource distribution for enhancing project performance in complex construction projects.

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.

Given the critical role played by natural gas in providing electricity, heat, and other essential services, better models are needed to understand the dynamics of natural gas networks during extreme events. This paper aims at establishing appropriate and fast simulation models to capture the slow dynamics of linepack depletion for ideal isothermal natural gas pipeline networks. Instead of solving partial differential equations (PDE) on a large scale, three alternative implicit ordinary differential equation (ODE) simulation techniques are derived and discussed. The first one is commonly used in the literature with a slack node assumption. We show that the system of equations associated with this model is degenerate when flux injections are controlled (i.e. specified) at all nodes. To recover regularity under such a condition, two novel implicit ODE models are proposed, both with different techniques for specifying boundary conditions. They are easy to derive and efficient to simulate with standard ODE solvers. More importantly, they present useful frameworks for analyzing how networks respond to system-wide mass flux imbalances. These techniques offer different alternatives for simulating system dynamics based on how sources and loads are chosen to be modeled, and they are all proven to be regular (non-degenerate) in tree-structured networks. These proposed techniques are all tested on the 20-node Belgium network. The simulation results show that the conventional model with the slack node assumption cannot effectively capture linepack depletion under long term system-wide mass flux imbalance, while the proposed models can characterize the network behavior until the linepack is completely depleted., Comment: 40 pages, accepted for publication in the Applied Mathematical Modeling (AMM) Journal

This paper explores a new control strategy to suppress the libration of a partial space elevator by applying torque control at the climber only. The control torque at the climber is generated by reaction wheels. A new dynamic model of the partial space elevator is developed by integrating the attitude dynamics of the climber into the classical two-piece dumbbell model. The influence of the climber attitude motion on the libration motion of the partial space elevator is analyzed with and without the control torque at the climber. Based on the characteristics of the system dynamics, a novel reaction wheel control strategy is proposed to suppress both the libration of the elevator and the climber attitude motion. Such a new control strategy is implemented by an optimal control scheme. The simulation results validate the effectiveness of the proposed control strategy, which provides a new alternative method to stabilize the libration of the partial space elevator for fast transfer of cargo with constant speeds. To avoid the adverse effect on the reaction wheel due to the frequent switch of rotation direction, a control strategy by two unidirectional reaction wheels is proposed to achieve the same control effect, where the reaction wheels rotate in only one direction.

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.

The air-gap eccentricity will produce unbalanced magnetic pull and cause vibrations and noises in a motor. In this study, the dynamic behavior of a synchronous motorized spindle with inclined eccentricity is investigated. A semi-analytical method is proposed to model the unbalanced magnetic pull and the electromagnetic torque of a rotor with inclined eccentricity, and the semi-analytical method is verified by the finite element method. The dynamic model of a spindle-bearing system is built by taking the centrifugal force and gyroscopic effects into account. Then, the vibration response of dynamic displacement eccentricity, inclined eccentricity including displacement eccentricity and angle eccentricity, rotating speed, and unbalanced mass eccentricity in both time domain and frequency domain are simulated and analyzed. The results show that the eccentricities can lead to fluctuations in amplitudes of the dynamic displacement response and the angle response. The frequency components of the dynamic responses are the combination of rotating frequency, VC frequency, and power frequency. It is indicated that the coupling interactions of bearing forces, unbalanced mass force, and unbalanced magnetic pull have an obvious effect on the spindle-bearing system.

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.

PurposeThis study was developed in response to the need to develop age-friendly smart living environments due to the complex demands placed on society by the ageing of the population. This study aims to analyse the potential that knowledge visualisation offers collaborative decision-making applied to the development of a multiple criteria framework supporting knowledge management (KM) through knowledge collaboration (KC) and knowledge sharing (KS) in the context of regional innovation systems (RIS).Design/methodology/approachUsing a socio-technical approach, knowledgeable and experienced representatives of RIS innovation actors were brought together to develop a constructivist multiple criteria framework that integrates knowledge visualisation and collaborative decision-making techniques (i.e. cognitive mapping and system dynamics).FindingsThe study introduces a multiple criteria model supporting KM encompassing conditions and practices of RIS innovation actors facilitating and encouraging KC and KS. The potential for knowledge visualisation in collaborative decision-making is explored in great depth and illustrated in a case study setting.Practical implicationsThe panel members who participated in this study consider our methodological proposal to be extremely versatile and see great potential for further applications in RIS contexts.Originality/valueThe combined use of cognitive mapping and system dynamics according to the strategic options development and analysis (SODA) approach offers a holistic and well-informed perspective on the issue in question. The literature reports no prior work of this methodological combination in the same research context.