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This paper is concerned with the design of positive L1$$ {L}_1 $$ filter for positive Markovian jump systems (PMJSs) with adaptive event‐triggered mechanism and quantization. Considering the mode jump of the system, a new adaptive event‐triggered condition is established, which not only can regulate the communication threshold according to the error of dynamic systems, but also can avoid the asynchronous phenomenon between the system mode and the filter mode. In order to further reduce the burden of communication, a new positive L1$$ {L}_1 $$ filter with the quantized measure system output is developed, and the design condition which ensures the filter error system to be stochastically stable with L1$$ {L}_1 $$ performance is presented which can be solved in terms of linear programming (LP) technique. It is illustrated that there is no Zeno phenomenon. The proposed results are applied to the power allocation in a mobile telecommunication system, and it is shown that compared with the existing results, the event‐triggered filter of this paper has some advantages. [ABSTRACT FROM AUTHOR]

Power quality is a critical issue in power systems that can affect the performance and reliability of sensitive equipment. The dynamic voltage restorer (DVR) is a widely used technology for improving power quality, but it mainly focuses on mitigating voltage sags and often neglects the issue of harmonic distortion. This paper proposes a DVR voltage compensation strategy based on the discrete Fourier transform (DFT). This strategy utilizes the DFT to analyze the voltage waveform, separating the fundamental and desired harmonic components. With this strategy, the DVR can effectively compensate for voltage sag and achieve selective harmonic compensation, thereby improving the power quality of the power system. Additionally, a method utilizing hardware circuitry and an LED as a timestamp is proposed to dynamically estimate digital siginal processor (DSP) resource occupancy rate in real‐time, ensuring effective compensation while minimizing resource occupancy. A 3 kW DVR experimental prototype was constructed and tested to evaluate the practicality of the proposed strategy. The simulation and experimental results show that the method proposed in this paper successfully realizes the compensation of voltage sag and harmonics in the power system, and improves the stability and reliability of the power system. [ABSTRACT FROM AUTHOR]

Stability issues can significantly increase the risk of hybrid microgrids (HMGs), particularly during island mode operation. The dynamic performance of the system can induce constraints and stability margins that may elevate the loss of load probability. This paper presents a new stability‐oriented risk assessment model that bridges the conventional reliability models, stability, and system risk. The proposed model ensures the risk of the system by considering the redesign or reconfiguration of HMGs to address stability issues. First, the interlinking converters (ICs) DC‐link voltage stability is analysed to determine the acceptable power flow margins in rectifying and inversion mode. Next, the new general risk assessment model is introduced. The results show that the stability margin significantly increases the risk of the HMG, particularly when considering the aging of converters. The study also examines the impact of various load characteristics and ICs with different numbers but the same total size. In some cases, the risk is acceptable for the desired loads, or it can be reduced to an admissible level by reconfiguring the ICs. Finally, the paper demonstrates the effectiveness of the proposed model in the optimal design of HMGs, aiming to guarantee the system's risk. [ABSTRACT FROM AUTHOR]

In this paper, the problem of fault estimation (FE) and fault‐tolerant control (FTC) for a class of systems with dynamic event‐triggered mechanism (ETM) and time delay in a network environment is studied. To shorten the time between events and minimize the amount of data transmitted, this paper introduces dynamic variables within the ETM. In order to ensure the interval between the system state and its estimation, a fault/state estimation observer considering time delay is designed, which improves the reliability and security of the system. On this basis, a fault‐tolerant controller considering time delay is constructed to compensate the impact of faults on the system. The observer, controller, and ETM are co‐designed to maintain system performance. By making full use of the information between subsystems, a continuous time interconnection systems is constructed and the cooperative control mechanism is redesigned on the basis of the above system. Through theoretical analysis, it has been demonstrated that Zeno phenomenon does not exist in the ETM in this paper. Finally, the main results are verified by numerical simulation, and the validity of the conclusions is demonstrated. [ABSTRACT FROM AUTHOR]

Wireless power transfer (WPT) technology can completely isolate the equipment from the power supply, and has been widely used. However, the transmission efficiency of WPT system is optimal only as the transmitting coil was completely aligned with the receiving coil. But in practical applications, the mobile devices are all dynamic system, and perfectly alignment of the coils is a very demanding condition. In order to solve this problem, the paper proposed a coil position adaptive adjustment control scheme of WPT system with lateral misalignment or angular misalignment. Firstly, the relationship between transmission efficiency and lateral misalignment and angular misalignment was analyzed. And the research results show that if there is a lateral misalignment between the coils, the transmission efficiency can be optimized by adjusting the angular misalignment. Secondly, based on the hill climbing algorithm, a coil position adaptive adjustment control method was proposed. Finally, a coil position adaptive adjustment WPT system experimental platform was built, and the experimental researches are carried on. The results of the experiment show that as the transfer distance is 0.10 m and the lateral misalignment is 0.12 m, the transfer efficiency can be increased from 38.38% to 55.61% through the proposed control scheme. And the results verify the feasibility and effectiveness of control scheme proposed in the paper. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This paper proposes a nonlinear control law with a dynamic controller based on high gain observer (HGO) for a co‐axial double rotor magnetic gear (CADRMG) based on Halbach array used in wind turbines. The generalized canonical form (GCF) is used to normalize the nonlinear augmented system to achieve the dynamic control signal with a chain of integrator of system output. In addition, a tracking problem is defined to track high speed rotor (HSR) with respect to GCF. On the other hand, the HGO is used to estimate the error tracking at the expense of nonlinear terms. Furthermore, the nonlinear system observability of the augmented system is evaluated. A dynamic control law is used to solve the tracking problem based on HGO. This controller can eliminate the effect of load torque as a constant and unknown disturbance in the closed‐loop system. Moreover, the closed‐loop stability of the system under dynamic signal control is guaranteed. The system states estimation is calculated by recursive equations from tracking error estimations. Finally, numerical simulations are given to illustrate the theoretical results of proposed system. [ABSTRACT FROM AUTHOR]

Double-array structures have been widely used to implement dictionaries with string keys. Although the space efficiency of dynamic double-array dictionaries tends to decrease with key updates, we can still maintain high efficiency using existing methods. However, these methods have practical problems of time and functionality. This paper presents several efficient rearrangement methods to solve these problems. Through experiments using real-world datasets, we demonstrate that the proposed rearrangement methods are much more practical than existing methods. [ABSTRACT FROM AUTHOR]

Regarding the low accuracy and instability of common online methods for estimating dynamic models in the time domain, in the presence of uncertainty in system dynamics, sensor noise and environmental disturbances, this area is still open for further research. In this paper, a new estimation method is proposed based on a new online robust meta‐heuristic adaptive LSQR (ORALSQR) for simultaneous estimation of a multi input/output linear dynamic model and system state variables. This new adaptive LSQR algorithm is used to solve the output matrix equations of the least squares error problem. The presented algorithm, based on its iterative nature, searches the answer subspace by using a new meta‐heuristic logic. In addition, the algorithm solving steps and the search domain size in each iteration are intelligently determined by the method. In an identification maneuver, this method estimates the state variables using an estimated model in the Kalman filter, then estimates the model online for the next iteration using the state variables. In addition the stability proof of this method is presented. Numerical results show more accuracy and robustness of this method compared to the other methods mentioned in this paper which contain LS and RLS based estimation methods. [ABSTRACT FROM AUTHOR]

This paper surveys recent multiagent reinforcement learning and neural Myerson auction deep learning efforts to improve mobility control and resource management in autonomous ground and aerial vehicles. The multiagent reinforcement learning communication network (CommNet) was introduced to enable multiple agents to perform actions in a distributed manner to achieve shared goals by training all agents' states and actions in a single neural network. Additionally, the Myerson auction method guarantees trustworthiness among multiple agents to optimize rewards in highly dynamic systems. Our findings suggest that the integration of MARL CommNet and Myerson techniques is very much needed for improved efficiency and trustworthiness. [ABSTRACT FROM AUTHOR]

This paper explores general coupled dynamic equations on time scales with multiple delays and investigates the existence of periodic solutions using the coincidence degree theory approach. The model studied includes the mathematical models of Nicholson, Mackey–Glass, and Lasota–Wazewska as special cases. Furthermore, we demonstrate that the solutions are not only asymptotically stable but also exponentially synchronized. The presented results significantly extend and complement existing findings in the field. The paper concludes with illustrative examples that highlight the practical implications of our analytical discoveries. [ABSTRACT FROM AUTHOR]

Extensive inverter‐based power sources (IPS) impose significant challenges on the restoration of high renewable penetrated power systems (HRPPS). To enhance HRPPS resilience, the proper utilization of IPSs must be implemented. Combining frequency dynamics of IPSs and synchronous generators, this paper proposes a coordinated restoration method for multi‐type power sources after a major blackout. First, interactions between synchronous generators and IPSs are systematically analyzed. Based on this, output characteristics and constraints of IPSs in the power sources restoration process are quantified. Second, the dynamic frequency regulation capability (DFRC) of restored systems is quantified based on a unified transfer function structure model. Then the maximum power disturbance that restored systems can bear is derived based on DFRC indices including the maximum frequency deviation and the rate of change of frequency. Third, considering interactions between power sources and the DFRC of restored systems, a coordinated restoration optimization model of multi‐type power sources is proposed. Finally, case studies based on a modified IEEE 39‐bus system are simulated to verify the applicability and superiority of the proposed method. Meanwhile, results show that the proposed method for quantifying DFRC is more suitable for HRPPSs than traditional inertia‐based methods. [ABSTRACT FROM AUTHOR]

This paper addresses the tracking control for an extended chained nonholonomic system under input saturation and matched disturbances. Specifically, the tracking error dynamic system is split into two subsystems in a cascade form. An integral sliding mode saturated controller is designed in combination with a finite‐time observer (FTDO) to ensure the finite‐time stability of the first subsystem, thereby simplifying the design of the second subsystem. A backstepping‐based adaptive saturated tracking control method is proposed for the second subsystem by incorporating the filter technology. The stability of the resulting closed‐loop system is demonstrated, and the tracking errors can be arbitrarily minimized through the appropriate design parameter selection. The effectiveness of the proposed scheme is validated through simulation and experimental results. [ABSTRACT FROM AUTHOR]

This paper studies the event triggered delayed impulsive control strategy for quasi‐synchronization for complex dynamical systems with parameter mismatch and packet loss. By constructing a comparative lemma with time delays and Lyapunov function, a few sufficient synchronization conditions are proposed by Cauchy matrix with time delays to analyze the system. Meanwhile, the possibility of packet loss is also considered and the quasi‐synchronization of the system is investigated by using both event triggered delayed impulsive control and self‐triggered impulsive control mechanisms. Theoretical analysis results are validated by numerical simulations. [ABSTRACT FROM AUTHOR]

This paper investigates cooperative linear quadratic differential games for uncertain dynamic systems with conservative players, who attempt to optimize the worst‐case outcomes with limited a priori knowledge about the uncertainties. Without properly designed control strategies, the uncertainties could cause significant impacts on the performance indexes of players and spoil the cooperation among players. In this paper, compensation‐based robust control techniques are applied to handle the uncertainties, including parameter perturbations and external disturbances, by transforming the impacts of the uncertainties into a more tractable form. Subsequently, using Vickrey–Clarke–Groves principles, an auction‐based control allocation mechanism is designed to incentivize the players to cooperatively implement the compensation. With the methods proposed, the worst‐case performance indexes of players can be improved, and the cooperation among players is maintained in the uncertain system. Numerical examples demonstrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of robust and high precision trajectory tracking control of uncertain robot manipulators with prescribed performance. Time delay estimation (TDE) technique is employed to estimate system model, then a new self‐adjusting strategy (SAS) is designed to adjust TDE gain online. Prescribed performance control (PPC) method is used to guarantee transient response speed and steady‐state accuracy. Moreover, the transform function of PPC employed in this paper has unlimited domain, which greatly improves the robustness and stability of system. An improved fixed‐time dynamical system is firstly deduced and analyzed, then a new fast nonsingular terminal sliding mode surface is designed to accelerate convergence rate of tracking errors. Finally, the whole system is strictly proven to be practical finite‐time stable, which means that tracking errors can converge to a small neighborhood of zero within a uniformly bounded convergence time. Main advantages of the proposed approach include model‐free, robust, singularity‐free, faster transient response and higher steady‐state tracking precision. Experimental results carried out on the Rethink Sawyer Robot also verified the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Summary: This paper presents the first model reference adaptive control system for nonlinear, time‐varying, hybrid dynamical plants affected by matched and parametric uncertainties, whose resetting events are unknown functions of time and the plant's state. In addition to a control law and an adaptive law, which resemble those of the classical model reference adaptive control framework for continuous‐time dynamical systems, the proposed framework allows imposing instantaneous variations in the reference model's trajectory to rapidly steer the trajectory tracking error to zero, while retaining the closed‐loop system's ability to follow a user‐defined signal. These results are enabled by the first extension of the classical LaSalle–Yoshizawa theorem to time‐varying hybrid dynamical systems, which is presented in this paper as well. A numerical simulation shows the key features of the proposed adaptive control system and highlights its ability to reduce both the control effort and the trajectory tracking error over a classical model reference adaptive control system applied to the same problem. [ABSTRACT FROM AUTHOR]

This paper presents a new nonlinear non‐intrusive reduced‐order model (NL‐NIROM) that outperforms traditional proper orthogonal decomposition (POD)‐based reduced order model (ROM). This improvement is achieved through the use of auto‐encoder (AE) and self‐attention based deep learning methods. The novelty of this work is that it uses stacked auto‐encoder (SAE) network to project the original high‐dimensional dynamical systems onto a low dimensional nonlinear subspace and predict fluid dynamics using an self‐attention based deep learning method. This paper introduces a new model reduction neural network architecture for fluid flow problem, as well as, a linear non‐intrusive reduced order model (L‐NIROM) based on POD and self‐attention mechanism. In the NL‐NIROM, the SAE network compresses high‐dimensional physical information into several much smaller sized representations in a reduced latent space. These representations are expressed by a number of codes in the middle layer of SAE neural network. Then, those codes at different time levels are trained to construct a set of hyper‐surfaces using self‐attention based deep learning methods. The inputs of the self‐attention based network are previous time levels' codes and the outputs of the network are current time levels' codes. The codes at current time level are then projected back to the original full space by the decoder layers in the SAE network. The capability of the new model, NL‐NIROM, is demonstrated through two test cases: flow past a cylinder, and a lock exchange. The results show that the NL‐NIROM is more accurate than the popular model reduction method namely POD based L‐NIROM. [ABSTRACT FROM AUTHOR]

This paper demonstrates that the switching of one grid‐connected virtual synchronous generator (VSG) between two control modes, namely constant voltage control (CVC) and current limiting control (CLC) happens at the derived two switching lines. Based on the current limiting inequality, the traditional current‐switched model is transferred to the angle‐switched model proposed here, and the system can be studied as one switched dynamic system. Based on this model, the transient stability of VSG with controller limits is investigated. This is achieved by constructing Lyapunov functions for each subsystem and deriving the relationship between the values of Lyapunov functions constructed under different conditions at the switching moment. The stability of the system is ensured when the Lyapunov function of each subsystem presents a decreasing trend in two consecutive switching intervals. On this basis, the stability boundary of the switching system is derived. Further analysis shows that optimal adjustment of the saturation current angle can make the system reach the maximum stability boundary. Finally, the numerical simulations and experimental tests verify the correctness of the proposed analysis. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a control technique for output tracking of reference signals in continuous‐time dynamical systems. The technique is comprised of the following three elements: (i) a fluid‐flow version of the Newton–Raphson method for solving algebraic equations, (ii) a system‐output prediction which has to track the future reference signal, and (iii) a speedup of the control action for enhancing the tracker's accuracy and, in some cases, stabilizing the closed‐loop system. The technique can be suitable for linear and nonlinear systems, implementable by simple algorithms, and can track reference points as well as time‐dependent reference signals. Though inherently local, the tracking controller is proven to have a global convergence for a class of linear systems. The derived theoretical results of the paper include convergence of the tracking controller and error analysis, and are supported by illustrative simulation and laboratory experiments. [ABSTRACT FROM AUTHOR]

Summary: Plant Synthetic Biology aims to enhance the capacities of plants by designing and integrating synthetic gene circuits (SGCs). Quantitative reporting solutions that can produce quick, rich datasets affordably are necessary for SGC optimization. In this paper, we present a new, low‐cost, and high‐throughput reporter system for the quantitative measurement of gene expression in plants based on autonomous bioluminescence. This method eliminates the need for an exogenous supply of luciferase substrate by exploiting the entire Neonothopanus nambi fungal bioluminescence cyclic pathway to build a self‐sustained reporter. The HispS gene, the pathway's limiting step, was set up as the reporter's transcriptional entry point as part of the new system's design, which significantly improved the output's dynamic range and brought it on par with that of the gold standard FLuc/RLuc reporter. Additionally, transient ratiometric measurements in N. benthamiana were made possible by the addition of an enhanced GFP as a normalizer. The performance of new NeoLuc/eGFP system was extensively validated with SGCs previously described, including phytohormone and optogenetic sensors. Furthermore, we employed NeoLuc/eGFP in the optimization of challenging SGCs, including new configurations for an agrochemical (copper) switch, a new blue optogenetic sensor, and a dual copper/red‐light switch for tight regulation of metabolic pathways. [ABSTRACT FROM AUTHOR]

Stability of a complex DC bus system (DBS) is a significant design consideration because of its constant power load (CPL) characteristics of the tightly‐regulated load converter. In this paper, the dynamic properties of a stand‐alone buck‐type DBS are investigated based upon the closed‐form large‐signal‐averaged model. The stability‐analysis‐in‐large around the operation point shows that CPLs create a destabilizing effect on the system that can lead to severe voltage oscillation caused by discontinuity‐induced Hopf bifurcations, which are qualitatively different from the bifurcations we know for smooth dynamical systems. Instead, this paper also explores the application of "amplitude death (AD) technique" to stabilize the coupled oscillatory DBSs and show that how this technique with its necessary modifications, for example, the heterogeneity‐induced AD technique characterized by two or more coupled DBSs with mismatched frequency of oscillations can provide an efficient open‐loop control solution for stabilizing the coupled DBSs. Finally, simulations and experimental results are provided to validate all these concepts. [ABSTRACT FROM AUTHOR]

This paper presents a panoramic view of research on economic and financial networks in the journal Networks, since its inception half a century ago. This paper focuses on both the breadth and depth of the journal articles, and within the context of earlier contributions, as well as more recent related ones in other scientific publications. From network optimization to game theory and a plethora of equilibrium concepts, along with novel dynamical systems frameworks, the journal has led the way in advancing economic and financial network models, algorithms, and applications. Moreover, Networks has helped to attract researchers in a variety of disciplines to the science of networks and the formulation and solution of associated problems drawn from the real world. [ABSTRACT FROM AUTHOR]

Many natural and economic systems are managed to deliver the highest benefits to society but are subject to regime shifts. We specifically consider the variability of the size of the system itself as a key driver of a regime shift. We address the question of how the optimal management of these systems should vary with its size. Put simply, certain management options might work when the system is of a given size, while others might be preferred when the system has grown or shrunk. In this paper, we develop a model that allows us to analyse the effect of the size of the system on its optimal management. We apply this model to a case of water pollution in a reservoir/lake that varies in size over time: sometimes the lake is deep and sometimes it is shallow. Numerical simulations were conducted to compare optimal management of the reservoir with and without explicitly modelling its size variation. The findings show that the overall social costs of optimally managing pollution are significantly smaller when the variability in size is taken into account. This is due to differences in the timing and magnitude of the optimal control. The key implication is that the variability of the size of a system should be explicitly considered in this type of management problems. [ABSTRACT FROM AUTHOR]

Time‐delay significantly impacts the control system's performance and even leads to the unexpected instability of the control system. This paper aims at the adverse effect of time‐delay on the control effect and stability of the active suspension system. This paper proposes to suppress vehicle body vibration by applying time‐delay displacement feedback. In this paper, the time‐delay control characteristics of the system are studied by combining theory with experiment. This paper first establishes the suspension system's dynamic Equation with time‐delay. Then, the frequency domain sweep method is used to analyze the stability of the control system. Finally, the time‐delay simulation model is established, and the single‐frequency excitation is used as the external excitation to compare with passive and backstepping control. The simulation results verify the superiority of the time‐delay control under external excitation. In this paper, through the 1/4 model bench test of the active suspension system, the simulation results and the test results under the same working conditions are compared, and it is found that the two have good consistency. The results show that the research is credible and can provide a reference for the actual design and application of active vehicle suspension controllers. [ABSTRACT FROM AUTHOR]

Both electric vehicles and Tokamak power supply use multiple parallel converters to realize high current operation in four quadrants. A finite‐time current sharing control algorithm based on disturbance compensation is proposed to improve the converter system's dynamic and steady‐state performance so that the system can achieve the required load current and branch‐current sharing in a finite time. In the proposed algorithm, this paper adopts a finite‐time observer for unknown disturbances in the converter system and load circuit due to disturbances, such as unknown inductance and resistance in the converter system. The results show that the load current control overshoot is minimal, and the dynamic performance of each branch current is higher than the traditional control algorithm. A finite‐time current sharing control algorithm based on disturbance compensation is proposed so that the converter system can achieve the required load current and branch current sharing in a finite time. [ABSTRACT FROM AUTHOR]

This paper reconceptualised the interrelated learning constructs in higher education based on the Dynamic Systems Theory (DST). The university students' learning experience before, during and post the Emergency Online Learning (EOL) was investigated to explore the dynamic changes among the learning constructs in higher education. A case study of a Chinese university was conducted, and one hundred and ninety‐three university students participated in the questionnaire. The data collected from this empirical research identify different hierarchical constructs of the conceptualised learning environment and reconceptualise the period of system reformation influenced by the EOL. The key findings include the identifications of the attractors and repellors framed by the DST and the impact on the changes in the learning constructs. The results of this paper contribute to further understanding of the university constructs' changes to better plan and support students' active learning in higher education. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive under frequency load shedding (UFLS) model that can be implemented in a multi‐area power system with real network characteristics. Conventional single‐area UFLS models operate on the basis of an equivalent center‐of‐inertia (COI) model, which ignores the local dynamics of system frequency response (SFR) and the impacts of load shedding location. Unlike the single‐SFR model, that is commonly utilized in previous works, the suggested multi‐area or multi‐SFR UFLS plan of this research has the distinct benefit of taking into account the dynamics of power transfer across different electric areas. The proposed multi‐area UFLS design incorporates a flywheel energy storage system (FESS) to support the inertial system frequency response and alleviate more than 30% load shedding while improving the frequency nadir by 25%. In order to investigate the performance of the proposed method under high penetrations of inertia‐free renewables, the inertia of the power network is reduced by around 30%; therefore the proposed UFLS scheme is assessed under a low inertia scenario. The proposed multi‐stage UFLS scheme is formulated as a mixed‐integer linear programming problem, and the optimal settings, including frequency set‐points and load shedding, are then optimized. The efficiency of the proposed model is verified using the IEEE‐118 Bus dynamic test system. [ABSTRACT FROM AUTHOR]

The security of natural gas supply and demand has always been an important topic, and as a result, analyzing and predicting the supply and demand of natural gas has received considerable attention from researchers. Numerous studies have extensively researched the supply and demand of natural gas. The main purpose of this study is to explore the supply‐demand balance mechanism for natural gas security. A system dynamics model is constructed to assess the supply and demand of natural gas in Sichuan Province, China. A new system dynamics model based on domestic gas, production gas, ecological environment and natural gas supply is proposed. The study considers four scenarios: natural development, economic priority development, environmental priority development and coordinated development. The results show that the rapid economic development has led to an excessive environmental carrying capacity, a larger gap between natural gas supply and demand, a decline in the quality of life of residents, and even affected the energy security of Sichuan Province and China. The findings also show that people's overprotection of the environment can lead to slow economic development, which prevents it from achieving its desired goals. The coordinated development scenario in this paper not only ensures the production of natural gas and the quality of life of residents, but also is an optimal scheme with low energy consumption and pollution. According to the research conclusions, considering the overall situation of China's energy security and the situation in Sichuan Province, the author proposes reforms from the supply and demand sides of natural gas. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, nonlinear control design scheme for a class of nonlinear systems is proposed based on operator coprime factorization theory. In detail, two stable controllers are provided to design a Bezout identity by combining left factorization (not coprime) with right factorization. Based on the proposed design method, a realization approach to left coprime factorization for the nonlinear system is obtained, which provides an effective framework for constructing left coprime factorization. Meanwhile, internal‐output stability of the nonlinear system is guaranteed. After that, based on the obtained left coprime factorization, the cases of the nonlinear systems with perturbations are discussed for guaranteeing robust stability for the perturbed systems. For the perturbations, two different cases, known bounded perturbations and unknown bounded perturbations, are investigated from different viewpoints to analyze robust stability issue for the perturbed systems. Finally, a simulation example is given to confirm the effectiveness of the proposed design method. [ABSTRACT FROM AUTHOR]

K. Hoffmann, L. Heib, G. Moretti, G. Rizzello, and K. Flaßkamp, Optimal operation of dielectric elastomer wave energy converters under harmonic and stochastic excitation, GAMM Mitt. Finally paper [4] deals with the optimal operation of dielectric elastomer wave energy converters under harmonic and stochastic excitation. The current special issue of the GAMM Mitteilungen, which is the second of a two-part series, contains several contributions on the topic of Applied and Nonlinear Dynamics. [Extracted from the article]

The continuous enhancement of optimization algorithms and their parameters has spurred the expansion of AI into novel application domains such as image recognition and smart home technology. This paper employs the system response curve (SRC) to the adaptive learning rate optimizer, addressing challenges associated with the establishment of the optimizer control model and parameter adjustments affecting the dynamic performance of the system. These insights offer theoretical support for the optimizer's application in deep learning models. To begin, the adaptive learning rate optimizer is a time‐varying system. Based on the intrinsic relationship between the network optimization and the control system, the time domain expression and approximate transfer function of the adaptive learning rate optimizer are derived, and the system dynamic model is established. Furthermore, based on the system control model of the optimizer, it is proposed to explain the performance impacts of different optimizers and their hyperparameters on the deep learning model through the SRC. Finally, experiments are performed on the MNIST, CIFAR‐10, UTKinect‐Action3D, and Florence3D‐Action datasets to validate the control theory of explaining optimizers through system response curves. The experimental results show that the recognition performance of the Adaptive Moment Estimate (Adam) is better than that of the Adaptive Gradient (AdaGrad) and Root Mean Square Propagation (RMSprop). Additionally, the learning rate affects the model training speed, and the practical application aligns with the theoretical analysis. [ABSTRACT FROM AUTHOR]

Summary: This article addresses the position control issue of multi‐quadrotor unmanned aerial vehicle (QUAV) formation. Concerning the translational dynamic of a multi‐QUAV system, on the one hand, it is an under‐actuation dynamic; on the other hand, it does not satisfy the matching condition. These features will cause inevitable thorny in the formation position control design. Furthermore, because of the state coupling problem, the formation control of multi‐QUAV system is more challenging and knotty than the control of single QUAV system. To achieve this control, both backstepping technique and neural network (NN) approximation strategy are combined by introducing an intermediary control, where NN is employed to compensate the system uncertainty. However, since the traditional adaptive NN control methods need to train a large number of adaptive parameters for the high approximation accuracy, it will cause the heavy computing burden if traditional adaptive method is used for the QUAV formation control. The proposed adaptive NN strategy in this paper only requires training a scalar adaptive parameter, which is generated from the norm of NN weight vector or matrix, thereby significantly reducing computational burden. Finally, according to Lyapunov stability proof and computer simulation, it is demonstrated that the control tasks can be successfully accomplished. [ABSTRACT FROM AUTHOR]

Bridge cranes are complex nonlinear dynamic systems with underactuated characteristics, making it challenging for controllers to man age the spatial swing of the load effectively. Additionally, uncertainties both within and outside the system adversely impact control performance. To address these issues, a Type‐II fuzzy sliding mode controller has proven effective in enhancing the anti‐swing control performance of the payload. However, due to the intricate parameter adjustment optimization problem and potential challenges in dealing with nonlinearity and uncertainty, especially in complex dynamic systems, this paper proposes a grey wolf algorithm based on a dynamic spiral hunting mechanism. This enhancement endows the algorithm with improved convergence speed and higher robustness, enabling more effective parameter tuning for the second‐order fractional‐order sliding mode controller (FSMC). The proposed algorithm demonstrates superior convergence speed and solution accuracy performance through testing and comparison. Finally, simulation verification under two conditions of the bridge crane system validates the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper studies the distributed optimization problem (DOP) of discrete time‐varying linear multi‐agent systems (MASs), in which the global objective function is formed by a sum of local convex objective functions. Firstly, a DOP with discrete time‐varying MASs is considered, in which the time‐varying linear matrix satisfies a certain equality constraint. To solve this problem, a novel discrete‐time distributed optimization algorithm (DOA) with event‐triggered communication mechanism (ETCM) is proposed. Secondly, by constructing the error dynamical system and using a series of inequality techniques, some sufficient conditions for achieving consensus and obtaining the optimal solution are established. It is found that the considered MAS has generality and the proposed DOA has the advantage of reducing communication burden. Finally, a numerical simulation is presented to verify the validity of theoretical results. [ABSTRACT FROM AUTHOR]

A dynamical system is considered such that, in this system, particles move on a toroidal lattice of the dimension N1×N2$$ {N}_1\times {N}_2 $$ according to a version of the rule of particle movement in Biham–Middleton–Levine traffic model. We introduce a stochastic case with direction choice for particles. Particles of the first type move along rows, and the particles of the second type move along columns. The goal is to find conditions of self‐organization system for any lattice dimension. We have proved that the BML model as a dynamical system is a special case of Buslaev nets. This equivalence allows us to use of Buslaev net analysis techniques to investigate the BML model. In Buslaev nets conception, the self‐organization property of the system corresponds to the existence of velocity single point spectrum equal to 1. In the paper, we consider the model version when one notable aspect is that a particle may change its type. Exactly, we assume a constant probability q$$ q $$ that a particle changes type at each step. In the case where q=0$$ q=0 $$, the system corresponds to the classical version of the BML model. We define a state of the system where all particles continue to move indefinitely, in both the present and the future, as a state of free movement. A sufficient condition for the system to result in a state of free movement from any initial state (condition for self‐organization) has been found. This condition is that the number of particles be not greater than half the greatest common divisor of the numbers N1,N2,…,Nn$$ {N}_1,{N}_2,\dots, {N}_n $$. It has been proved that, if n=2$$ n=2 $$, and whether 0

We consider a diffusive Rosenzweig–MacArthur predator–prey model in the situation when the prey diffuses at a rate much smaller than that of the predator. In a certain parameter regime, the existence of fronts in the system is known: the underlying dynamical system in a singular limit is reduced to a scalar Fisher–KPP (Kolmogorov–Petrovski–Piskunov) equation and the fronts supported by the full system are small perturbations of the Fisher–KPP fronts. The existence proof is based on the application of the Geometric Singular Perturbation Theory with respect to two small parameters. This paper is focused on the stability of the fronts. We show that, for some parameter regime, the fronts are spectrally and asymptotically stable using energy estimates, exponential dichotomies, the Evans function calculation, and a technique that involves constructing unstable augmented bundles. The energy estimates provide bounds on the unstable spectrum which depend on the small parameters of the system; the bounds are inversely proportional to these parameters. We further improve these estimates by showing that the eigenvalue problem is a small perturbation of some limiting (as the modulus of the eigenvalue parameter goes to infinity) system and that the limiting system has exponential dichotomies. Persistence of the exponential dichotomies then leads to bounds uniform in the small parameters. The main novelty of this approach is related to the fact that the limit of the eigenvalue problem is not autonomous. We then use the concept of the unstable augmented bundles and by treating these as multiscale topological structures with respect to the same two small parameters consequently as in the existence proof, we show that the stability of the fronts is also governed by the scalar Fisher–KPP equation. Furthermore, we perform numerical computations of the Evans function to explicitly identify regions in the parameter space where the fronts are spectrally stable. [ABSTRACT FROM AUTHOR]

The diversity of transmission modes and the heterogeneity of populations in the transmission of infectious diseases are issues that have to be faced in the current disease protection. In this paper, an infectious disease model incorporating age structure and horizontal and environmental spread, along with asymptomatic infection, is proposed to describe diversification of disease transmission routes and population heterogeneity. The expression of the basic reproduction number R0$$ {\mathcal{R}}_0 $$ is derived by a linear approximation method, and it is concluded that if R0<1$$ {\mathcal{R}}_0<1 $$, the disease‐free steady state is globally asymptotically stable; if R0>1$$ {\mathcal{R}}_0>1 $$, the model has a unique endemic steady state which is locally asymptotically stable under certain conditions. Further, the uniform persistence of disease is proved using the persistence theory of the infinite‐dimensional dynamical system when R0>1$$ {\mathcal{R}}_0>1 $$. In additional, the issue of optimal control problem according to this model is investigated, and the representation of optimal control with respect to state variables and adjoint variables is obtained which also imply the existence and uniqueness of optimal control. Finally, numerical simulations are carried out to interpret the theoretical results and to discuss the impact of age and control measures in disease transmission. [ABSTRACT FROM AUTHOR]

Summary: The d‐q detection method based on instantaneous reactive power theory (IRPT) is one of the commonly used time‐domain analysis methods for harmonic detection. Due to the poor dynamic performance of the algorithm caused by the low pass filter (LPF) in the d‐q detection method, an improved series repetitive control (ISRC) structure is proposed in this paper. Using attenuation factor and correction factor to improve the series repetitive controller, the ISRC contains a negative resonance peak with a peak value of 150 dB at the 6 k harmonic frequency, and the amplitude gain is 0 dB at frequencies outside the 6 k harmonic frequency. Therefore, ISRC can achieve good attenuation effect on 6 k harmonics while maintaining the same gain at other frequencies. ISRC is used instead of LPF in the traditional d‐q detection and this strategy improves the dynamic response of the system while guaranteeing its steady state performance. Finally, simulations and experiments verify the feasibility of the strategy. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new passivity‐based controller design technique for discrete‐time fully actuated systems. The controller establishes finite‐time and fixed‐time convergence of dynamical system trajectories to an equilibrium state. A new form of a dissipation function, selected a priori, is introduced to design a feedback control rule to achieve such convergence properties. An energy shaping and damping injection methodology is extended to achieve non‐asymptotic stabilization. A numerical example to validate the proposed methods is provided. [ABSTRACT FROM AUTHOR]

In this paper, we shall deal with the long‐time dynamics of a nonlinear system of wave equation with a fractional damping and a small delay. Inspired by the works of Chueshov and Lasiecka on the property of quasi‐stability of dynamic systems, we show first the quasi‐stability property holds for the problem considered here and then prove the existence of global and exponential attractors. We shall also prove the upper semicontinuities of global attractors with respect to the fractional exponents. Finally, we shall show the continuities of global attractors with respect to a small parameter multiplying the delay term in a residual dense set and their upper semicontinuities in a complete metric space. [ABSTRACT FROM AUTHOR]

Summary: This paper proposes a hybrid observer for state estimation over a network. The network provides delayed measurements of the output of the plant at time instants that are not necessarily periodic and are accompanied by timestamps provided by a clock that synchronizes with the clock of the observer in finite time. The proposed observer, along with the plant and communication network, are modeled by a hybrid dynamical system that has two timers, a logic variable, and two memory states to capture the mechanisms involved in the events associated with sampling and arrival of information, as well as the logic in the estimation algorithm. The hybrid model also includes a generic clock synchronization scheme to cope with a mismatch between the clocks at the plant and the observer. Convergence properties of the estimation error of the system are shown analytically and supported by numerical examples. [ABSTRACT FROM AUTHOR]

Complex dynamic systems offer a rich platform for understanding the individual or the person‐specific mechanisms. Yet, in learning analytics research and education at large, a complex dynamic system has rarely been framed, developed, or used to understand the individual student where the learning process takes place. Individual (or person‐specific) methods can accurately and precisely model the individual person, create person‐specific models, and devise unique parameters for each individual. Our study used the latest advances in complex systems dynamics to study the differences between group‐based and individual self‐regulated learning (SRL) dynamics. The findings show that SRL is a complex, dynamic system where different sub‐processes influence each other resulting in the emergence of non‐trivial patterns that vary across individuals and time scales, and as such far from the uniform picture commonly theorized. We found that the average SRL process does not reflect the individual SRL processes of different people. Therefore, interventions derived from the group‐based SRL insights are unlikely to be effective in personalization. We posit that, if personalized interventions are needed, modelling the person with person‐specific methods should be the guiding principle. Our study offered a reliable solution to model the person‐specific self‐regulation processes which can serve as a ground for understanding and improving individual learning and open the door for precision education. Practitioner notesWhat is already known about this topic Self‐regulation is a catalyst for effective learning and achievement.Our understanding of SRL personalization comes from insights based on aggregate group‐based data.What this paper adds Every student has their own unique SRL process that varies from the average in non‐trivial ways.We offer a credible method for mapping the individualized SRL process.SRL dynamics vary across time scales. That is, the trait dynamics are different from the state dynamics, and they should be supported differently.Implications for practice and/or policy Personalization can best be achieved if based on unique person‐specific idiographic methods.Supporting learning and SRL in particular can be more efficient when we understand the differences across time scales and persons and apply insights accordingly.The general SRL average should not be expected to work for everyone. [ABSTRACT FROM AUTHOR]

Summary: An adaptive optimal controller is proposed in this paper to maximize the captured power of a variable speed wind power system. The proposed controller is a combination of optimal and adaptive control components. The Adaptive Dynamic Programming technique is used to design the optimal control component to overcome the nonlinear problem of system dynamics and ensure stability. While the neural network is used to approximate unknown disturbances and system uncertainties. After that, the adaptive control component fully compensates for the effects of these unknown elements. Neither optimal nor adaptive control components necessitate prior knowledge of system dynamics. Furthermore, the approximation network updates only the weight matrix norm rather than the weight matrix of the neural network in each interval time, which significantly reduces computation. The stability analysis of the closed‐loop system is obtained using Lyapunov stability theory. The correctness and robustness of the control scheme are validated in two different scenarios using MATLAB/Simulink. The presented robust adaptive optimal controller is also compared to other existing controllers to demonstrate its benefits. [ABSTRACT FROM AUTHOR]

This paper investigates a neural network adaptive controller design method for turbo‐generator of power systems with external disturbance, unknown system dynamic, prescribed performance, and unknown actuator dead zone. First, the unknown system dynamic is estimated and overcome through a neural network. Using the implicit function theorem, the unknown asymmetric dead‐zone behavior of the actuator is overcome by another static neural network. Second, the external disturbance and the reconstruction error of neural networks are handled by a robust term updated online. Moreover, there is no requirement to pre‐know or off‐line estimate the reconstruction error of neural networks and the upper bound of the system external disturbance. Third, based on Lyapunov theory, the smooth control law is proposed. In the meantime, the uniform ultimate boundedness of the networks weights is strictly proved. Furthermore, the system state error satisfies the prescribed transient performance and can converge to a small neighborhood around zero. Finally, a numerical simulation shows the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Safety is the most fundamental problem of safety‐critical systems. Safety control addresses the problem whether a given unsafe region of the state space can be avoided by a specific control‐input. Moreover, linearly parameterized dynamical system is a general assumption in most safety‐critical adaptive control literature; however, unknown parameters in real systems are usually nonlinear. The control problem of nonlinearly parameterized systems is really difficult without linear‐in‐the‐parameters (LIP) assumptions, which tends to be complicated and computationally intensive. In this paper, a novel model reference safety‐critical adaptive control (MRAC) approach is proposed for a class of nonlinearly parameterized systems. The proposed approach involves a novel controller architecture with a modified update law, which specifically filters out the unsafe behavior (the system is in a state which the system cannot operate normally, i.e., the given unsafe region), while preserving favorable tracking capability and robustness. The novelty of this paper is that the nonlinearly parameterized systems can be enforced safety, without LIP assumptions and complex calculations. Most importantly, this approach is effective for nonlinearly parameterized systems as well as linearly parameterized systems. Finally, three illustrative numerical examples are presented to demonstrate the effectiveness of the proposed design approach. [ABSTRACT FROM AUTHOR]

Although the voltage feedforward control based on the point of common coupling (PCC) has been widely studied in the grid‐connected inverters to suppress the harmonics in the grid current, harmonic instability still occurs when the PCC voltage contains a series of background harmonics under weak grid condition. To this end, this paper applies the grid voltage sensed by phasor measurement units instead of the PCC voltage for feedforward. Meanwhile, considering that coupling between the phase‐locked loop (PLL) and the grid impedance would cause instability under weak grid condition, a novel compensation term is introduced to eliminate the negative influence of PLL. In addition, a method to improve the transient response of the system is also proposed so that the cut‐off frequency does not change with the impedance of the grid. By analysing the output impedance of the system, it can be proved that the strategy proposed can effectively improve the stability of the system. The experimental results indicate that the harmonics in the grid current are obviously reduced and the dynamic performance of the system is improved, which verifies the analysis. [ABSTRACT FROM AUTHOR]

In this paper, the use of time‐varying piecewise quadratic functions is investigated to characterize the finite‐time stability of state‐dependent impulsive dynamical linear systems. Finite‐time stability defines the behavior of a dynamic system over a bounded time interval. More precisely, a system is said to be finite‐time stable if, given a set of initial conditions, its state vector does not exit a predefined domain for a certain finite interval of time. This paper presents new sufficient conditions for finite‐time stability based on time‐varying piecewise quadratic functions. These conditions can be reformulated as a set of Linear Matrix Inequalities that can be efficiently solved through convex optimization solvers. Different numerical analysis are included in order to prove that the presented conditions are able to improve the results presented so far in the literature. [ABSTRACT FROM AUTHOR]

Concerning the difficulty in determining the restart strategy for AC/DC interconnected systems, this paper proposes a method of calculating the optimal restart strategy for AC/DC interconnected systems based on dynamic energy. First, a dynamic energy function that characterises the stability of system is constructed. Then, the analytical expressions of the dynamic energy function are calculated separately for the deionisation stage, the restart stage, and the alpha retard stage of the entire restart process. On this basis, the variation rate of the system dynamic energy is defined as the energy accumulation rate, which quantifies the accumulation or consumption of dynamic energy during the restart process, and facilitates detailed analysis of the effects of the restart time and restart duration on the stability of system. Then, a method to calculate the optimal restart time is proposed with the goal of minimising the dynamic energy accumulated in the system. Finally, hardware‐in‐the‐loop tests are carried out on the RT‐LAB platform for simulation verification. The experimental results show that the dynamic energy function constructed in this paper can reflect the stability of system. Using the proposed optimal restart strategy can reduce the accumulation of dynamic energy and improve the stability of system. [ABSTRACT FROM AUTHOR]

The increase in penetration of large‐scale renewable generation significantly affects power system characteristics. Thus, it is necessary to assess the steady state and dynamic performance of a power system for different installing scenarios of renewable power plants such as photovoltaic (PV) ones. The paper specifically investigates the impacts of very large‐scale photovoltaic (VLS‐PV) generation on the power system dynamic performance including rotor‐angle and frequency stabilities. For this purpose, a detailed equivalent dynamic model for a multi‐unit VLS‐PV system and corresponding controllers are developed. The paper also proposes a comprehensive set of replacement scenarios comprising different PV penetration, location, and voltage control strategies as well as simulating various large disturbances. To investigate the impacts of VLS‐PV generation on dynamic system performance, a study framework for frequency and rotor‐angle stability involving the modal and transient analysis is presented as well. By using the VLS‐PV detailed model along with other dynamic components of a power system such as synchronous generators, automatic voltage regulators (AVRs), and governors, the stabilities studying instructions are employed for the IEEE 39‐bus and 118‐bus test systems. [ABSTRACT FROM AUTHOR]

Stochastic dynamics describes processes in complex systems having the probabilistic nature. They can involve very different dynamical systems and occur on very different temporal and spatial scale. This paper discusses the concept of stochastic dynamics and its implementation in the popular program MBN Explorer. Stochastic dynamics in MBN Explorer relies on the Monte Carlo approach and permits simulations of physical, chemical, and biological processes. The paper presents the basic theoretical concepts underlying stochastic dynamics implementation and provides several examples highlighting its applicability to different systems, such as diffusing proteins seeking an anchor point on a cell membrane, deposition of nanoparticles on a surface leading to structures with fractal morphologies, and oscillations of compounds in an autocatalytic reaction. The chosen examples illustrate the diversity of applications that can be modeled by means of stochastic dynamics with MBN Explorer. [ABSTRACT FROM AUTHOR]