Stability Of Systems Research Articles

Exponential Stabilization of Stochastic Feedforward Nonlinear Systems: A Dynamic Gain Approach

Exponential Stabilization of Stochastic Feedforward Nonlinear Systems: A Dynamic Gain Approach

Event-Triggered Impulsive Control for Input-to-State Stabilization of Nonlinear Time-Delay Systems.

This article investigates the event-triggered impulsive control (ETIC) problem for a class of nonlinear time-delay systems subject to exogenous disturbances. An original event-triggered mechanism (ETM) which utilizes the information of system state and external input is constructed based on Lyapunov function approach. To achieve the input-to-state stability (ISS) of the considered system, some sufficient conditions are presented, in which the underlying relationship among ETM, exogenous input, and impulse action is established. Furthermore, the possible Zeno behavior induced by the proposed ETM is excluded simultaneously. As an application, the design criterion of ETM and impulse gain is put forward for a class of impulsive control systems with delay according to the feasibility of some linear matrix inequalities (LMIs). Finally, two examples with numerical simulations are provided to confirm the effectiveness of the developed theoretical results, where the synchronization issue of delayed Chua's circuit is considered.

Generalized Dixon Resultant for Strong Delay-Independent Stability of Linear Systems With Multiple Delays

Generalized Dixon Resultant for Strong Delay-Independent Stability of Linear Systems With Multiple Delays

Unbalanced force analysis on valve core clamping in hydraulic spool valves

Hydraulic systems are widely used in nuclear power plant equipments. The reliability and stability of hydraulic systems can be seriously impacted by the valve core clamping resulting from unbalanced forces. This paper investigates the unbalanced force characteristics of hydraulic spool valve cores to identify the mechanisms behind such failures. Numerical models are established for hydraulic spool valve cores using sliding mesh technology with varying control face structures and space angles between the inlet and outlet, which are validated by experiments. The turbulent flow is modeled using the RNG k-ε model. Simulation results reveal that larger openings weaken the unbalanced force distribution while the larger inlet flow rates exacerbate the unbalanced force distribution, and control face structures significantly impact force distribution by altering the throttling effect of the throttle orifice. Furthermore, the impact of the space angles between the inlet and outlet is little compared with other parameters. These findings provide a reference for designing and optimizing hydraulic spool valves, contributing to higher reliability and stability of hydraulic systems.

Command filter-based adaptive neural two-bit-triggered containment control for saturated nonlinear multi-agent systems

Abstract This paper considers the adaptive two-bit-triggered containment control problem for nonlinear multi-agent systems in the presence of input saturation. Since input saturation occurs frequently in practical systems, which can affect the stability of the multi-agent systems under consideration, an auxiliary design system is introduced to address this issue. Meanwhile, considering limited transmission resources in practical systems, this paper mainly focuses on the triggering condition and the control signal transmission bits, presenting a two-bit-triggered control approach to optimize the utilization of transmission resources. Furthermore, a command filter is introduced into the design process to solve the problem of complexity explosion. The proposed method ensures that all signals of the closed-loop system are bounded and the output signals of all followers converge to a convex hull spanned by the outputs of the leaders. Finally, two simulation examples are provided to verify the validity of the presented control scheme.

Fast finite-time control for a class of stochastic low-order nonlinear system with uncertainties

This work explores the challenge of adaptive fast finite-time control for stochastic low-order nonlinear systems with uncertainties. It introduces an improved practical finite-time criterion that aims to achieve accelerated convergence. The criterion aims to extend to a larger range of stochastic nonlinear systems, accommodating state uncertainties and integrating Lyapunov functions with varying powers to ensure rapid convergence in the presence of uncertainties. The entire control design approach is based on the backstepping method. Specifically, fuzzy logic systems are employed in the recursive design of controllers and adaptive laws to address complex uncertain terms. Additionally, the desired magnitude target powers of the Lyapunov function in the criterion are effectively obtained by a clever overlapping selection of parameters. Finally, this study demonstrates the practical finite-time stability of stochastic low-order uncertain nonlinear systems and validates the effectiveness of the proposed approach through simulation example, highlighting its ability to achieve fast convergence.

Fabrication of soy protein nanoparticles based on metal-phenolic networks for stabilization of nano-emulsions delivery system

The use of soy protein isolate (SPI) nanoparticles as a stabilizer in nano-emulsion systems has garnered significant interest. While metal-phenolic networks (MPNs) have been explored for their multifunctional surface modification capabilities, their integration with food protein-based delivery systems remains less explored. In this study, we attempt to develop a novel strategy to encapsulate cinnamaldehyde using MPNs (EGCG-Fe3+) with self-assembling soy protein nanoparticles (SE-Fe NPs) as a stabilizer for nano-emulsions. UV, Raman, and X-ray photoelectron spectroscopy analyses demonstrated that SE-Fe NPs were generated through metal-phenolic coordination and covalent interactions. SE-Fe NPs had a narrower particle size distribution and enhanced radical scavenging (up to 3.35-fold), as well as thermal stability. Furthermore, the smaller droplet size, higher modulus, higher cinnamaldehyde encapsulation efficiency (from 63.5% to 84.2%), and improved bio-accessibility of SE-Fe NPs stabilized nano-emulsions delivery system demonstrated in this study shows promising future applications in the food industry.

An improved BiGAN model for anomaly detection in finance

Financial systems play a pivotal role in shaping contemporary society, and the detection of financial anomalies holds immense significance in mitigating the adverse repercussions of market uncertainties on the global economy. In this context, this study presents an innovative LSTM-GANs model, specifically crafted to enhance the detection of anomalies in financial stock markets. The model introduces an "Anomaly Score" as a pivotal metric, which is computed through a combination of factors such as Reconstruction Loss, Latent Space Distance, and Discriminator Score. This composite score provides a quantitative assessment of the anomaly level within the financial data. By applying a predefined threshold to this Anomaly Score, the model efficiently identifies and flags anomalies. In a world where financial markets are increasingly complex and prone to unexpected events, the ability to detect and respond to anomalies swiftly is paramount. This novel LSTM-GANs model offers a promising approach to bolster the accuracy and effectiveness of financial anomaly detection, thereby contributing to the stability and resilience of global financial systems.

Fixed-time stabilization of output-constrained stochastic high-order nonlinear systems

Fixed-time stabilization of output-constrained stochastic high-order nonlinear systems

Analysis method of explosive electromagnetic radiation energy

AbstractThe energy of electromagnetic radiation from explosions is coupled to electronic equipment circuits, disrupting the initiation sequence or causing failure in an increasing number of cases, which seriously affects the stability of weapon systems. There is a significant difference between the characteristics of the explosive electromagnetic radiation signals and modulated electromagnetic signals. The electric field intensity and signal power cannot directly represent the magnitude of the explosive electromagnetic radiation energy, and traditional electromagnetic signal analysis methods are unsuitable for explosion electromagnetic signal analysis. To solve this problem, the mechanism of explosive electromagnetic radiation was first analyzed. Through verification experiments of the explosion electromagnetic intensity and temperature, it was concluded that there is a strong correlation between the explosion plasma temperature and electromagnetic intensity. The temperature of the explosive plasma is derived based on the measured surface temperature of the explosive fireball, a functional relationship is established between the energy of the explosive plasma and the temperature of the plasma, and plasma energy is introduced as a parameter for electric field intensity correction. The interference signal analysis method based on eye diagram is used to calibrate the electromagnetic radiation damage ability, achieving quantitative analysis of the interference degree of electromagnetic radiation energy on the signal, and providing a new approach for the analysis of explosive electromagnetic radiation energy.

The Influence of Initiators, Particle Size and Composition on the Electrokinetic Potential of N-(Isopropyl)acrylamide Derivatives.

The aim of this study was to characterize and compare the zeta potential of particles sensitive to external thermal stimuli. Poly N-(isopropyl) acrylamide (PNIPA) was selected as the thermosensitive polymer with a volume phase transition temperature (VPTT) between 32 and 33 °C. The hydrodynamic diameter (DH) of the nanoparticles was measured by dynamic light scattering. Zeta potential (ZP) measurements were performed with the same instrument used for DH measurements. ZP measurements allow the prediction of the stability of colloidal systems in aqueous solutions. These measurements were combined with a pH study before and after the purification process of the particles. The ZP was measured to determine the electrostatic interactions between the particles, which can lead to particle aggregation and decrease their colloidal stability. The effect of the composition of the synthesized particles on the ZP was assessed. One of the most important factors influencing ZP is pH, especially in aqueous solutions. The initiator did not significantly affect the DH of the particles, but it did significantly affect the ZP. The synthesized particles were subjected to a visible radiation absorption study in the selected temperature range to determine the VPTT.

Electrochemical oxidation of low concentrated tetracycline (TC) in aqueous solution: Operation optimization, degradation mechanisms, and detoxification efficiency

The three-dimensional electrode (3DE) process has attracted much attention due to its high efficiency in treating hard-to-degrade organic pollutants. In this work, an efficient removal of low-concentration tetracycline (TC) from aqueous solution was achieved using a 3DE reactor equipped with IrO2-RuO2-Ti anode and granular activated carbon (GAC). In addition, the reliable response surface model (RSM) indicates that maintaining a current density of 12.5 mA/cm2 led to effective TC degradation (≥ 96%) within two hours. After verification, •OH and •O2− were identified as the primary active oxygen species in the 3DE system and the addition of particle electrodes significantly enhanced the system's ability to generate •O2−. Additionally, we proposed the complete degradation pathway for TC in the 3DE system, utilizing LC-MS and DFT calculations. Notably, the 3DE system demonstrates superior detoxification efficiency. Many highly toxic intermediates detoxify into minimal amounts of less toxic substances after degradation and exhibit a growth-promoting ability for mung beans comparable to that of pure water. Meanwhile, the outstanding universality, stability, and cost-effectiveness of 3DE systems also contribute to its extensive application prospects. The study presents an efficient, cost-effective, and viable process scheme for removing low-concentration TC from aqueous solutions.

Control and Intelligent Optimization of a Photovoltaic (PV) Inverter System: A Review

PV power generation is developing fast in both centralized and distributed forms under the background of constructing a new power system with high penetration of renewable sources. However, the control performance and stability of the PV system is seriously affected by the interaction between PV internal control loops and the external power grid. The impact of the PV system on the reliability, stability, and power quality of power systems has restricted them to further participate in power supplies with a large capacity. Traditional control methods have become ineffective at dealing with these problems as the PV system becomes increasingly complex and nonlinear. Intelligent control as a more advanced technology has been integrated into the PV system to improve system control performance and stability. However, intelligent control for the PV system is still in the early stages due to the extensive calculation and intricate implementation of intelligent algorithms. Further investigations should be carried out to effectively combine intelligent control with the PV system to constitute an intelligent PV power system with multiple functions, high stability, and high-performance. This paper provides a systematic classification and detailed introduction of various intelligent optimization methods in a PV inverter system based on the traditional structure and typical control. The future trends and research topics are given to provide a reference for the intelligent optimization control in the PV system.

Load Margin Assessment of Power Systems Using Physics-Informed Neural Network with Optimized Parameters

Challenges in the operation of power systems arise from several factors such as the interconnection of large power systems, integration of new energy sources and the increase in electrical energy demand. These challenges have required the development of fast and reliable tools for evaluating the operation of power systems. The load margin (LM) is an important index in evaluating the stability of power systems, but traditional methods for determining the LM consist of solving a set of differential-algebraic equations whose information may not always be available. Data-Driven techniques such as Artificial Neural Networks were developed to calculate and monitor LM, but may present unsatisfactory performance due to difficulty in generalization. Therefore, this article proposes a design method for Physics-Informed Neural Networks whose parameters will be tuned by bio-inspired algorithms in an optimization model. Physical knowledge regarding the operation of power systems is incorporated into the PINN training process. Case studies were carried out and discussed in the IEEE 68-bus system considering the N-1 criterion for disconnection of transmission lines. The PINN load margin results obtained by the proposed method showed lower error values for the Root Mean Square Error (RMSE), Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE) indices than the traditional training Levenberg-Marquard method.

A Study of the Stability of Integro-Differential Volterra-Type Systems of Equations with Impulsive Effects and Point Delay Dynamics

This research relies on several kinds of Volterra-type integral differential systems and their associated stability concerns under the impulsive effects of the Volterra integral terms at certain time instants. The dynamics are defined as delay-free dynamics contriobution together with the contributions of a finite set of constant point delay dynamics, plus a Volterra integral term of either a finite length or an infinite one with intrinsic memory. The global asymptotic stability is characterized via Krasovskii–Lyapuvov functionals by incorporating the impulsive effects of the Volterra-type terms together with the effects of the point delay dynamics.

Numerical simulation of a fractional stochastic delay differential equations using spectral scheme: a comprehensive stability analysis

The fractional stochastic delay differential equation (FSDDE) is a powerful mathematical tool for modeling complex systems that exhibit both fractional order dynamics and stochasticity with time delays. The purpose of this study is to explore the stability analysis of a system of FSDDEs. Our study emphasizes the interaction between fractional calculus, stochasticity, and time delays in understanding the stability of such systems. Analyzing the moments of the system’s solutions, we investigate stochasticity’s influence on FSDDS. The article provides practical insight into solving FSDDS efficiently using various numerical techniques. Additionally, this research focuses both on asymptotic as well as Lyapunov stability of FSDDS. The local stability conditions are clearly presented and also the effects of a fractional orders with delay on the stability properties are examine. Through a comprehensive test of a stability criteria, practical examples and numerical simulations we demonstrate the complexity and challenges concern with the analyzing FSDDEs.

Assessment of Grid and System Supportability Based on Spatio-Temporal Conditions—Novel Key Performance Indicators for Energy System Evaluation

The energy transition introduces new technical standards, laws and regulations regarding the stability and reliability of energy grids and systems. Due to the non-existence of a measuring standard, key performance indicators (KPIs) were developed to enable the measurement and comparison of individual energy grid (namely electricity, heat and gas grid) and system supportabilities while also promoting well-founded decision-making and optimization efforts. Inconsistencies in definitions concerning fundamental energy terms and the correlations between them inhibit the effective usage of the KPIs. Therefore, the overarching issue of the security of energy supply and its related subjects were also approached. The primary subject of this paper is the development of two new KPIs to measure and compare the energy grid and system supportability. These KPIs are based on spatio-temporal conditions in their respective grids. The usage and benefits of the developed KPIs are exemplarily highlighted by analyzing the impact of a scenario with the integration of a large-scale heat pump into the electricity and heat grid. The energy grid supportability is determined for each grid, whereas the energy system supportability takes the interactions of the electricity and heat grid into account. The developed KPIs are intended to enable stakeholders to identify areas with optimization potential in energy grids and systems. Moreover, the KPIs can be used to create a standardized evaluation method for regulatory requirements.

Stability and stabilization of systems with a cyclical time-varying delay via delay-product-type looped-functionals

In this paper, the stability analysis and synthesis of systems with a cyclical time-varying delay are investigated. At first, the entire delay interval is divided into intervals of monotonically increasing and monotonically decreasing alternating cycles. Then, a Lyapunov-Krasovskii functional (LKF) with two delay-product-type looped-functionals is constructed based on different delay monotone intervals. For the generated quadratic function with respect to delay in the derivative of LKF, a matrix-injection-based method is utilized to obtain the negative-definite conditions of LKF’s derivative. A delay-dependent stability criterion with less conservatism is derived and the corresponding state-feedback control strategy is given consequently. Finally, a numerical example is utilized to show the superiority and the effectiveness of the proposed method, and a single-area power system under load frequency control is applied to verify the feasibility of the proposed control strategy.

Enhanced energy balancing and optimal load curtailment strategy for DC microgrid integration in hybrid AC/DC distribution networks

AbstractUnleashing the potential of distributed renewable energy sources (RESs), intelligent and autonomous microgrids are becoming pivotal in attaining net‐zero carbon emission goals. Hybrid AC/DC microgrids rise as cutting‐edge microgrid topologies, capitalising on the best of both AC and DC systems. However, the integration of intermittent renewables and uncertainties in loading poses stability challenges. Advanced bidirectional converter controls provide efficient power exchange, but in extreme contingencies, a resilient supervisory control framework (load management/load curtailment approach) is inevitable to withstand/avoid unplanned renewable disruptions/blackouts. Moreover, the operational paradigm shift towards achieving net‐zero emissions, isolated operation of RESs, and conventional load shedding methods are anticipated to encounter substantial challenges, necessitating the development of alternative strategies. In order to improve the stability of hybrid microgrid systems in islanding scenarios, this research presents an energy balancing and load curtailment strategy. The proposed method aims at optimising resource utilisation, prioritising essential loads, and executing an optimal load curtailment strategy (if required), thereby augmenting the stability of systems. Unlike a meta‐heuristic or exhaustive search, which depends on 2n − 1 possible combinations and become unworkable as load numbers increase, the suggested methodology is based on a mathematically modelled load restriction method. By including load criticality, this strategy effectively prevents blackouts even with an increasing number of loads, providing a significantly more useful and practical solution. Additionally, the proposed charging algorithm ensures that the energy storage system imports energy from the grid during off‐peak hours and maximises power generation from the DC subgrid. The efficacy of the proposed strategy is validated using a modified IEEE‐33 bus system as a test case for a hybrid AC/DC microgrid. Simulation results demonstrate the effectiveness of the MILP‐based load curtailment approach in maintaining system stability and preventing blackouts during unforeseen events.

Power transformer fault diagnosis using dynamic multiscale graph modeling and M2SGCN network based on statistical fusion

Power equipment fault diagnostics hold significant importance for the stability of power grid systems. In pursuit of this objective, this paper proposes a fault diagnosis method that utilizes dynamic multiscale graph (DMG) modeling and the multiscale multi-stream GCN(M2SGCN) network, incorporating statistical fusion. Specifically, a novel DMG modeling method is proposed to derive visibility graph data and horizontal visibility graph data from vibration signals across multiple scales. Next, a comprehensive neural network architecture named M2SGCN is established to learn global and local features simultaneously, providing a more precise representation. Subsequently, a Dempster Shafer evidence theory statistical fusion technique combined with an adaptive threshold model (DSTFusion) is utilized to integrate primary decision results for enhanced fault diagnosis accuracy. In addition, two datasets obtained from single-phase and three-phase power transformers are analyzed to demonstrate the evolution process. When compared to state-of-the-art indicators such as accuracy, precision, recall, and F1 scores, the method proposed excels in multiple aspects, successfully detecting fault states before their occurrence and achieving outstanding performance.