The increasing interconnection of modern industrial control systems(ICSs)with the Internet has enhanced operational efficiency,but alsomade these systemsmore vulnerable to cyberattacks.This heightened exposure has dri...The increasing interconnection of modern industrial control systems(ICSs)with the Internet has enhanced operational efficiency,but alsomade these systemsmore vulnerable to cyberattacks.This heightened exposure has driven a growing need for robust ICS security measures.Among the key defences,intrusion detection technology is critical in identifying threats to ICS networks.This paper provides an overview of the distinctive characteristics of ICS network security,highlighting standard attack methods.It then examines various intrusion detection methods,including those based on misuse detection,anomaly detection,machine learning,and specialised requirements.This paper concludes by exploring future directions for developing intrusion detection systems to advance research and ensure the continued security and reliability of ICS operations.展开更多
As attack techniques evolve and data volumes increase,the integration of artificial intelligence-based security solutions into industrial control systems has become increasingly essential.Artificial intelligence holds...As attack techniques evolve and data volumes increase,the integration of artificial intelligence-based security solutions into industrial control systems has become increasingly essential.Artificial intelligence holds significant potential to improve the operational efficiency and cybersecurity of these systems.However,its dependence on cyber-based infrastructures expands the attack surface and introduces the risk that adversarial manipulations of artificial intelligence models may cause physical harm.To address these concerns,this study presents a comprehensive review of artificial intelligence-driven threat detection methods and adversarial attacks targeting artificial intelligence within industrial control environments,examining both their benefits and associated risks.A systematic literature review was conducted across major scientific databases,including IEEE,Elsevier,Springer Nature,ACM,MDPI,and Wiley,covering peer-reviewed journal and conference papers published between 2017 and 2026.Studies were selected based on predefined inclusion and exclusion criteria following a structured screening process.Based on an analysis of 101 selected studies,this survey categorizes artificial intelligence-based threat detection approaches across the physical,control,and application layers of industrial control systems and examines poisoning,evasion,and extraction attacks targeting industrial artificial intelligence.The findings identify key research trends,highlight unresolved security challenges,and discuss implications for the secure deployment of artificial intelligence-enabled cybersecurity solutions in industrial control systems.展开更多
Realistic faults and failures often occur probabilistically in the lane-keeping system of autonomous electric vehicles,reducing system reliability and posing significant challenges to driving safety.To enhance the sys...Realistic faults and failures often occur probabilistically in the lane-keeping system of autonomous electric vehicles,reducing system reliability and posing significant challenges to driving safety.To enhance the system resilience,this paper proposes a novel robust fuzzy fault-tolerant control strategy that incorporates the adaptive event-trigger(AET)mechanism to realize stable,reliable,and precise lane-keeping control in the presence of multiple system uncertainties and probabilistic faults.First,to capture the uncertain and time-varying nature of tire cornering stiffness,an effective Takagi-Sugeno(T-S)fuzzy tire model is developed.Then,by employing the distribution-based probabilistic approach,two sets of unrelated random variables,random sensor and actuator faults in the control system,are modeled.Next,to improve communication efficiency and address ineluctable network-induced delays,an AET control framework with a well-designed triggering condition is established.Subsequently,a robust fuzzy output feedback fault-tolerant lane-keeping controller that satisfies the H∞per-formance is designed by using the Lyapunov-Krasovski functional method.Furthermore,the mean-square ex-ponential stability of the closed-loop system is rigorously guaranteed.Finally,real-time simulations based on Carsim/Simulink co-simulation platform under dynamic driving conditions demonstrate the feasibility and ef-fectiveness of the proposed control strategy.展开更多
To address the finite-time tracking control problem for fractional-order nonlinear systems(FONSs) with actuator faults and external disturbance,a novel strategy of the finite-time adaptive fuzzy fault-tolerant control...To address the finite-time tracking control problem for fractional-order nonlinear systems(FONSs) with actuator faults and external disturbance,a novel strategy of the finite-time adaptive fuzzy fault-tolerant controller is presented in this paper by utilizing the finite-time stability theory and fractional-order dynamic surface control scheme combined with backstepping method.A new lemma is developed for analyzing the finite-time stability of FONSs in terms of fractional differential inequality,which modifies some existing results.Fuzzy logic systems are adopted to identify unknown nonlinear characteristics in FONS.In order to compensate for the influence of unknown external disturbance and estimation error for fuzzy logic systems,an auxiliary function is employed to estimate the upper bound of parameters online.Furthermore,a global coordinate transformation is first introduced initially to decouple the fractional-order dynamic system of a specific class of underactuated single-link flexible manipulator systems,thereby transforming it into lower triangular systems.Simulation analyses and experimental results verify the feasibility and effectiveness of finite-time tracking control algorithm.展开更多
This paper investigates the consensus tracking control problem for high order nonlinear multi-agent systems subject to non-affine faults,partial measurable states,uncertain control coefficients,and unknown external di...This paper investigates the consensus tracking control problem for high order nonlinear multi-agent systems subject to non-affine faults,partial measurable states,uncertain control coefficients,and unknown external disturbances.Under the directed topology conditions,an observer-based finite-time control strategy based on adaptive backstepping and is proposed,in which a neural network-based state observer is employed to approximate the unmeasurable system state variables.To address the complexity explosion problem associated with the backstepping method,a finite-time command filter is incorporated,with error compensation signals designed to mitigate the filter-induced errors.Additionally,the Butterworth low-pass filter is introduced to avoid the algebraic ring problem in the design of the controller.The finite-time stability of the closed-loop system is rigorously analyzed with the finite-time Lyapunov stability criterion,validating that all closed-loop signals of the system remain bounded within a finite time.Finally,the effectiveness of the proposed control strategy is verified through a simulation example.展开更多
In this paper,we study the issue of controlling a rotating flexible body-beam system(RFBBS)which consists of a tip mass attached to the free-end and a rigid disk attached to the clamped-end of an Euler-Bernoulli beam....In this paper,we study the issue of controlling a rotating flexible body-beam system(RFBBS)which consists of a tip mass attached to the free-end and a rigid disk attached to the clamped-end of an Euler-Bernoulli beam.The boundary control input is affected by both unknown disturbance and nonlinear input backlash.First,the input backlash is considered as desired control input combined with a nonlinear input error,converting it to an external disturbance,and then,the control signal is designed through the energy-based control method.Next,the closed-loop system’s stability is analysed through Lyapunov direct method.Finally,the efficacy of the proposed control scheme is tested through numerical simulations utilizing the finite difference method.展开更多
This paper proposes a fault-tolerant control scheme for Euler-Lagrange systems that ensures the tracking error decays to a pre-specified accuracy level within a prescribed time period,despite unknown actuation charact...This paper proposes a fault-tolerant control scheme for Euler-Lagrange systems that ensures the tracking error decays to a pre-specified accuracy level within a prescribed time period,despite unknown actuation characteristics and potential fading powering faults.By performing deliberately designed coordinate transformations on the tracking error,the complex and demanding problem of“reaching specified precision within a given time”is transformed into a bounded control problem,facilitating the development of the control scheme.To enhance practicality,the design incorporates smooth function fitting and dynamic surface control techniques.Additionally,the proposed control algorithm is robust to faults,effectively handling a combination of fading powering faults and additive actuator faults without requiring additional human intervention.Numerical simulations on a two-link robotic manipulator verify the effectiveness of the proposed control algorithm.展开更多
Researchers are increasingly focused on enabling groups of multiple unmanned vehicles to operate cohesively in complex,real-world environments,where coordinated formation control and obstacle avoidance are essential f...Researchers are increasingly focused on enabling groups of multiple unmanned vehicles to operate cohesively in complex,real-world environments,where coordinated formation control and obstacle avoidance are essential for executing sophisticated collective tasks.This paper presents a Distributed Formation Control and Obstacle Avoidance(DFCOA)framework for multi-unmanned ground vehicles(UGV).DFCOA integrates a virtual leader structure for global guidance,an improved A^(*)path planning algorithm with an advanced cost function for efficient path planning,and a repulsive-force-based improved vector field histogram star(VFH^(*))technique for collision avoidance.The virtual leader generates a reference trajectory while enabling distributed execution;the improved A^(*)algorithm reduces planning time and number of nodes to determine the shortest path from the starting position to the goal;and the improved VFH^(*)uses 2D LiDAR data with inter-agent repulsive force to simultaneously avoid collision with obstacles and maintain safe inter-vehicle distances.The formation stability of the proposed DFCOA reaches 95.8%and 94.6%in two scenarios,with root mean square(RMS)centroid errors of 0.9516 and 1.0008 m,respectively.Velocity tracking is precise(velocity centroid error RMS of 0.2699 and 0.1700 m/s),and linear velocities closely match the desired 0.3 m/s.Safety metrics showed average collision risks of 0.7773 and 0.5143,with minimum inter-vehicle distances of 0.4702 and 0.8763 m,confirming collision-free navigation of four UGVs.DFCOA outperforms conventional methods in formation stability,path efficiency,and scalability,proving its suitability for decentralized multi-UGV applications.展开更多
Iterative Learning Control(ILC)provides an effective framework for optimizing repetitive tasks,making it particularly suitable for high-precision applications in both precision manufacturing and intelligent transporta...Iterative Learning Control(ILC)provides an effective framework for optimizing repetitive tasks,making it particularly suitable for high-precision applications in both precision manufacturing and intelligent transportation systems(ITS).This paper presents a systematic review of ILC's developmental progress,current methodologies,and practical implementations across these two critical domains.The review first analyzes the key technical challenges encountered when integrating ILC into precision manufacturing workflows.Through case studies,it evaluates demonstrated improvements in positioning accuracy,surface finish quality,and production throughput.Furthermore,the study examines ILC’s applications in ITS,with particular focus on vehicular motion control applications including autonomous vehicle trajectory tracking,platoon coordination,and traffic signal timing optimization,where its data-driven characteristics enhance adaptability to dynamic environments.Finally,the paper proposes targeted future research directions that are essential for fully realizing ILC’s potential in advancing these interconnected yet distinct fields.展开更多
The Nelder-Mead simplex method is a well-known algorithm enabling the minimization of functions that are not available in closed-form and that need not be differentiable or convex.Furthermore,it is particularly parsim...The Nelder-Mead simplex method is a well-known algorithm enabling the minimization of functions that are not available in closed-form and that need not be differentiable or convex.Furthermore,it is particularly parsimonious on the number of function evaluations,thus making it preferable to convex optimization paradigms in the case,common when dealing with control design problems,that the objective function of the optimization problem is non-differentiable,non-convex,and its closed-form is not available or difficult to be computed analytically.The main goal of this paper is to show how the joint use of the Nelder-Mead simplex method and the Morrison algorithm can be successfully used to solve relevant and challenging control problems that cannot be easily solved using analytic methods.In particular,it is shown how the problems of strong stabilization,static output feedback stabilization,and design of robust controllers having fixed structure can be framed as optimization problems,which,in turn,can be efficiently solved by coupling the two above mentioned algorithms.The performance of this procedure is compared with state-of-the-art techniques on dozens of static output feedback benchmark case studies,and its effectiveness is demonstrated by several examples.展开更多
Energy-regenerative suspension combined with piezoelectric and electromagnetic transduction has evolved into a core technological pathway in advancing automotive design paradigms.With the aim of improving energy harve...Energy-regenerative suspension combined with piezoelectric and electromagnetic transduction has evolved into a core technological pathway in advancing automotive design paradigms.With the aim of improving energy harvesting performance,time-delayed feedback control is widely used in an energy-regenerative suspension system under different external disturbances in this paper.Meanwhile,limited research has addressed the stochastic dynamics of time-delayed nonlinear energy-regenerative suspension systems.Different from previous studies,this work studies the stochastic response and P-bifurcation of the nonlinear energy-regenerative suspension system with time-delayed feedback control.Firstly,an approximately equivalent dimension reduction system is established by the variable transformation method,and then the stationary probability density function of amplitude is obtained by the stochastic averaging method.Secondly,the precision of the method used in this work is verified by comparing the numerical solutions with the analytical results.Finally,based on the stationary probability density function,the influence of system parameters on stochastic P-bifurcation and the mean output power is discussed.展开更多
The bipartite containment control problem for heterogeneous nonlinear multi-agent systems(HNMASs)within multi-group networks under signed digraphs is investigated,where the first-order and second-order nonlinear dynam...The bipartite containment control problem for heterogeneous nonlinear multi-agent systems(HNMASs)within multi-group networks under signed digraphs is investigated,where the first-order and second-order nonlinear dynamic agents belong to distinct groups.Interactions are cooperative-antagonistic within each group and sign-in-degree balanced across the inter-groups.Firstly,a state feedback control protocol is designed to ensure that the trajectories of followers in diverse groups can converge to distinct convex hulls formed by their corresponding leaders,respectively.As an extension,the bipartite control problem with time-variant formation for the multi-agent system(MAS)is also considered,and a corresponding control protocol with formation compensation vectors is given.Finally,in view of Lyapunov stability theory and matrix inequality,the sufficient conditions for realizing the bipartite containment control are obtained,and several simulations are provided to verify the validity of the above methods.展开更多
This paper introduces a small perturbation frequency domain thermal analysis model based on the nonlinear dynamics model.The model can be applied to study the high-precision temperature control of thermal systems unde...This paper introduces a small perturbation frequency domain thermal analysis model based on the nonlinear dynamics model.The model can be applied to study the high-precision temperature control of thermal systems under low-frequency complex perturbations.The frequency domain characteristics of the space gravitational wave detection satellite are analyzed,and a multi-channel perturbation structure is established.The effects of three kinds of heat flow perturbations,including external heat flow,power generation power,and waste heat of electronic equipment,on the temperature through five transfer paths are investigated.It has been discovered that the waste heat from electronic equipment inside the satellite has the most noticeable effect on the temperature power spectral density of temperature-sensitive optical loads,serving as the primary factor influencing thermal stability.For complex noise signals,the small perturbation analysis method can decompose the different frequency components or ranges,reducing the problem to linearized analysis and simplifying complex calculations.The results indicate that the temperature power spectral density decreases as signal frequency increases,with low-frequency signals exerting a greater influence on temperature stability.The small perturbation analysis method is a novel and effective method for temperature control of space thermal systems,with high accuracy and stability.展开更多
In scenarios where ground-based cloud computing infrastructure is unavailable,unmanned aerial vehicles(UAVs)act as mobile edge computing(MEC)servers to provide on-demand computation services for ground terminals.To ad...In scenarios where ground-based cloud computing infrastructure is unavailable,unmanned aerial vehicles(UAVs)act as mobile edge computing(MEC)servers to provide on-demand computation services for ground terminals.To address the challenge of jointly optimizing task scheduling and UAV trajectory under limited resources and high mobility of UAVs,this paper presents PER-MATD3,a multi-agent deep reinforcement learning algorithm with prioritized experience replay(PER)into the Centralized Training with Decentralized Execution(CTDE)framework.Specifically,PER-MATD3 enables each agent to learn a decentralized policy using only local observations during execution,while leveraging a shared replay buffer with prioritized sampling and centralized critic during training to accelerate convergence and improve sample efficiency.Simulation results show that PER-MATD3 reduces average task latency by up to 23%,improves energy efficiency by 21%,and enhances service coverage compared to state-of-the-art baselines,demonstrating its effectiveness and practicality in scenarios without terrestrial networks.展开更多
Trajectory tracking for nonlinear robotic systems remains a fundamental yet challenging problem in control engineering,particularly when both precision and efficiency must be ensured.Conventional control methods are o...Trajectory tracking for nonlinear robotic systems remains a fundamental yet challenging problem in control engineering,particularly when both precision and efficiency must be ensured.Conventional control methods are often effective for stabilization but may not directly optimize long-term performance.To address this limitation,this study develops an integrated framework that combines optimal control principles with reinforcement learning for a single-link robotic manipulator.The proposed scheme adopts an actor–critic structure,where the critic network approximates the value function associated with the Hamilton–Jacobi–Bellman equation,and the actor network generates near-optimal control signals in real time.This dual adaptation enables the controller to refine its policy online without explicit system knowledge.Stability of the closed-loop system is analyzed through Lyapunov theory,ensuring boundedness of the tracking error.Numerical simulations on the single-link manipulator demonstrate that themethod achieves accurate trajectory followingwhile maintaining lowcontrol effort.The results further showthat the actor–critic learning mechanism accelerates convergence of the control policy compared with conventional optimization-based strategies.This work highlights the potential of reinforcement learning integrated with optimal control for robotic manipulators and provides a foundation for future extensions to more complex multi-degree-of-freedom systems.The proposed controller is further validated in a physics-based virtual Gazebo environment,demonstrating stable adaptation and real-time feasibility.展开更多
This paper investigates the detection and mitigation of coordinated cyberattacks on Load Frequency Control(LFC)systems integrated with Battery Energy Storage Systems(BESS).As renewable energy sources gain greater pene...This paper investigates the detection and mitigation of coordinated cyberattacks on Load Frequency Control(LFC)systems integrated with Battery Energy Storage Systems(BESS).As renewable energy sources gain greater penetration,power grids are becoming increasingly vulnerable to cyber threats,potentially leading to frequency instability and widespread disruptions.We model two significant attack vectors:load-altering attacks(LAAs)and false data injection attacks(FDIAs)that corrupt frequency measurements.These are analyzed for their impact on grid frequency stability in both linear and nonlinear LFC models,incorporating generation rate constraints and nonlinear loads.A coordinated attack strategy is presented,combining LAAs and FDIAs to achieve stealthiness by concealing frequency deviations from system operators,thereby maximizing disruption while evading traditional detection.To counteract these threats,we propose an Unknown Input Observer(UIO)-based detection framework for linear and nonlinear LFCs.The UIO is designed using linear matrix inequalities(LMIs)to estimate system states while isolating unknown attack inputs,enabling attack detection through monitoring measurement residuals against a predefined threshold.For mitigation,we leverage BESS capabilities with two adaptive strategies:dynamic mitigation for dynamic LAAs,which tunes BESS parameters to enhance the system’s stability margin and accelerate convergence to equilibrium;and staticmitigation for static LAAs and FDIAs.Simulations show that the UIO achieves high detection accuracy,with residuals exceeding thresholds promptly under coordinated attacks,even in nonlinear models.Mitigation strategies reduce frequency deviations by up to 80%compared to unmitigated cases,restoring stability within seconds.展开更多
Embodied intelligent systems integrate perception,control,and decision-making within physical agents,and have become a cornerstone of modern aerospace,autonomous driving,and cooperative robotic applications.When opera...Embodied intelligent systems integrate perception,control,and decision-making within physical agents,and have become a cornerstone of modern aerospace,autonomous driving,and cooperative robotic applications.When operating in uncertain and dynamic environments,such systems must address challenges arising from incomplete sensing,unpredictable maneuvers,communication constraints,disturbances,and evolving network structures.展开更多
In wind power transmission via modular multilevel converter based high voltage direct current(MMCHVDC)systems,under traditional control strategies,MMC-HVDCcannot provide inertia support to the receiving-end grid(REG)d...In wind power transmission via modular multilevel converter based high voltage direct current(MMCHVDC)systems,under traditional control strategies,MMC-HVDCcannot provide inertia support to the receiving-end grid(REG)during disturbances.Moreover,due to the frequency decoupling between the two ends of the MMCHVDC,the sending-end wind farm(SEWF)cannot obtain the frequency variation information of the REG to provide inertia response.Therefore,this paper proposes a novel coordinated source-network-storage inertia control strategy based on wind power transmission via MMC-HVDC system.First,the grid-side MMC station(GS-MMC)maps the frequency variations of the REG to direct current(DC)voltage variations through the frequency mapping control,and uses submodule capacitor energy to provide inertial power.Then,the wind farm-side MMC station(WF-MMC)restores the DC voltage variations to frequency variations through the frequency restoration control and power loss compensation,providing real-time frequency information for the wind farm.Finally,based on real-time frequency information,thewind farmutilizes the rotor kinetic energy and energy storage to provide fast and lasting power support through the wind-storage coordinated inertia control strategy.Meanwhile,when the wind turbines withdraw from the inertia response phase,the energy storage can increase the power output to compensate for the power deficit,preventing secondary frequency drops.Furthermore,this paper uses small-signal analysis to determine the appropriate values for the key parameters of the proposed control strategy.A simulation model of the wind power transmission via MMCHVDC system is built in MATLAB/Simulink environment to validate and evaluate the proposed method.The results show that the proposed coordinated control strategy can effectively improve the system inertia level and avoid the secondary frequency drop under the load sudden increase condition.展开更多
Energy storage-equipped photovoltaic(PV-storage)systems can meet frequency regulation requirements under various operating conditions,and their coordinated support for grid frequency has become a future trend.To addre...Energy storage-equipped photovoltaic(PV-storage)systems can meet frequency regulation requirements under various operating conditions,and their coordinated support for grid frequency has become a future trend.To address frequency stability issues caused by low inertia and weak damping,this paper proposes a multi-timescale frequency regulation coordinated control strategy for PV-storage integrated systems.First,a self-synchronizing control strategy for grid-connected inverters is designed based on DC voltage dynamics,enabling active inertia support while transmitting frequency variation information.Next,an energy storage inertia support control strategy is developed to enhance the frequency nadir,and an active frequency support control strategy for PV system considering a frequency regulation deadband is proposed,where the deadband value is determined based on the power regulation margin of synchronous generators,allowing the PV-storage system to adaptively switch between inertia support and primary frequency regulation under different disturbance conditions.This approach ensures system frequency stability while fully leveraging the regulation capabilities of heterogeneous resources.Finally,the real-time digital simulation results of the PV-storage integrated system demonstrate that,compared to existing control methods,the proposed strategy effectively reduces the rate of change of frequency and improves the frequency nadir under various disturbance scenarios,verifying its effectiveness.展开更多
Intervention strategies to control non-point source nitrogen(N)and phosphorus(P)pollution in agriculture are expensive and there is a trade-off between engineering cost and treatment effectiveness.Implementing strateg...Intervention strategies to control non-point source nitrogen(N)and phosphorus(P)pollution in agriculture are expensive and there is a trade-off between engineering cost and treatment effectiveness.Implementing strategies often result in unsatisfactory outcomes and massive engineering costs when managing diffusive pollution in agricultural catchments.To address this issue,this paper proposes a robust,handy,catchment N&P decision support system(CNPDSS),an Android-based smartphone system integrated with a web-based geographic information system(GIS).The CNPDSS aims to provide artificial intelligence-driven decisions that minimize N&P loadings and engineering costs for mitigating pollution in agricultural catchments.It consists of four components:a general user interface(GUI),GIS,N&P pollution modeling(NPPM),and a DSS.The CNPDSS simplifies the GUI and integrates GIS modules to create a user-friendly interface,enabling non-professional users to operate the system easily through intuitive actions.The NPPM uses straightforward empirical models to predict N&P loadings,enhancing efficiency by avoiding excessive parameters.Taking into account the N&P movement pathway in the catchment,the DSS incorporates three control measures:source reduction in farmland(before migration stage),process retention by ecological ditch(midway transport stage),and down-end purification by constructed wetland(waterbody discharge stage),to formulate a comprehensive ternary controlling strategy.To optimize the cost-effectiveness of any proposed N&P control strategies for sub-catchments,a differential evolution algorithm(DEA)is employed in CNPDSS to carry out a dual-objective decision-making optimization computation.In this study,the CNPDSS is applied to a case study in an agricultural catchment in Central China to develop the most cost-effective ternary N&P control strategies that ensure the catchment water quality within Criterion Ⅲ of the Chinese Surface Water Quality Standard GB3838-2002 is met(total N concentration≤1.0 mg L^(-1)and total P concentration≤0.2 mg L^(-1)).Our results demonstrate that the CNPDSS is feasible and also possesses an adaptive design and flexible architecture to enable its generalization and extension to support strong hands-on applications in other catchments.展开更多
文摘The increasing interconnection of modern industrial control systems(ICSs)with the Internet has enhanced operational efficiency,but alsomade these systemsmore vulnerable to cyberattacks.This heightened exposure has driven a growing need for robust ICS security measures.Among the key defences,intrusion detection technology is critical in identifying threats to ICS networks.This paper provides an overview of the distinctive characteristics of ICS network security,highlighting standard attack methods.It then examines various intrusion detection methods,including those based on misuse detection,anomaly detection,machine learning,and specialised requirements.This paper concludes by exploring future directions for developing intrusion detection systems to advance research and ensure the continued security and reliability of ICS operations.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(RS-2023-00242528,50%)supported by the Korea Internet&Security Agency(KISA)through the Information Security Specialized University Support Project(50%).
文摘As attack techniques evolve and data volumes increase,the integration of artificial intelligence-based security solutions into industrial control systems has become increasingly essential.Artificial intelligence holds significant potential to improve the operational efficiency and cybersecurity of these systems.However,its dependence on cyber-based infrastructures expands the attack surface and introduces the risk that adversarial manipulations of artificial intelligence models may cause physical harm.To address these concerns,this study presents a comprehensive review of artificial intelligence-driven threat detection methods and adversarial attacks targeting artificial intelligence within industrial control environments,examining both their benefits and associated risks.A systematic literature review was conducted across major scientific databases,including IEEE,Elsevier,Springer Nature,ACM,MDPI,and Wiley,covering peer-reviewed journal and conference papers published between 2017 and 2026.Studies were selected based on predefined inclusion and exclusion criteria following a structured screening process.Based on an analysis of 101 selected studies,this survey categorizes artificial intelligence-based threat detection approaches across the physical,control,and application layers of industrial control systems and examines poisoning,evasion,and extraction attacks targeting industrial artificial intelligence.The findings identify key research trends,highlight unresolved security challenges,and discuss implications for the secure deployment of artificial intelligence-enabled cybersecurity solutions in industrial control systems.
基金Supported by National Natural Science Foundation of China(Grant Nos.52025121,52394263)National Key R&D Plan of China(Grant No.2023YFD2000301)+2 种基金Foundation of State Key Laboratory of Automobile Safety and Energy Saving of China(Grant No.KFZ2201)Jiangsu Provincial Scientific Research Center of Applied Mathematics(Grant No.BK20233002)Special Fund of Jiangsu Province for the Transformation of Scientific and Technological Achievements(Grant No.BA2021023).
文摘Realistic faults and failures often occur probabilistically in the lane-keeping system of autonomous electric vehicles,reducing system reliability and posing significant challenges to driving safety.To enhance the system resilience,this paper proposes a novel robust fuzzy fault-tolerant control strategy that incorporates the adaptive event-trigger(AET)mechanism to realize stable,reliable,and precise lane-keeping control in the presence of multiple system uncertainties and probabilistic faults.First,to capture the uncertain and time-varying nature of tire cornering stiffness,an effective Takagi-Sugeno(T-S)fuzzy tire model is developed.Then,by employing the distribution-based probabilistic approach,two sets of unrelated random variables,random sensor and actuator faults in the control system,are modeled.Next,to improve communication efficiency and address ineluctable network-induced delays,an AET control framework with a well-designed triggering condition is established.Subsequently,a robust fuzzy output feedback fault-tolerant lane-keeping controller that satisfies the H∞per-formance is designed by using the Lyapunov-Krasovski functional method.Furthermore,the mean-square ex-ponential stability of the closed-loop system is rigorously guaranteed.Finally,real-time simulations based on Carsim/Simulink co-simulation platform under dynamic driving conditions demonstrate the feasibility and ef-fectiveness of the proposed control strategy.
基金supported by the National Natural Science Foundation of China(62403340,62303339)Sichuan Science and Technology Program(2026NSFSC1518)+2 种基金China Postdoctoral Science Foundation(CPSF)(2025T180940,2024M762208)Postdoctoral Fellowship Program of CPSF(GZC20231783)Guangxi Key Laboratory of Brain-Inspired Computing and Intelligent Chips(BCIC-24-K2)。
文摘To address the finite-time tracking control problem for fractional-order nonlinear systems(FONSs) with actuator faults and external disturbance,a novel strategy of the finite-time adaptive fuzzy fault-tolerant controller is presented in this paper by utilizing the finite-time stability theory and fractional-order dynamic surface control scheme combined with backstepping method.A new lemma is developed for analyzing the finite-time stability of FONSs in terms of fractional differential inequality,which modifies some existing results.Fuzzy logic systems are adopted to identify unknown nonlinear characteristics in FONS.In order to compensate for the influence of unknown external disturbance and estimation error for fuzzy logic systems,an auxiliary function is employed to estimate the upper bound of parameters online.Furthermore,a global coordinate transformation is first introduced initially to decouple the fractional-order dynamic system of a specific class of underactuated single-link flexible manipulator systems,thereby transforming it into lower triangular systems.Simulation analyses and experimental results verify the feasibility and effectiveness of finite-time tracking control algorithm.
基金supported in part by the Beijing Natural Science Foundation under Grant 4252050in part by the National Science Fund for Distinguished Young Scholars under Grant 62425304in part by the Basic Science Center Programs of NSFC under Grant 62088101.
文摘This paper investigates the consensus tracking control problem for high order nonlinear multi-agent systems subject to non-affine faults,partial measurable states,uncertain control coefficients,and unknown external disturbances.Under the directed topology conditions,an observer-based finite-time control strategy based on adaptive backstepping and is proposed,in which a neural network-based state observer is employed to approximate the unmeasurable system state variables.To address the complexity explosion problem associated with the backstepping method,a finite-time command filter is incorporated,with error compensation signals designed to mitigate the filter-induced errors.Additionally,the Butterworth low-pass filter is introduced to avoid the algebraic ring problem in the design of the controller.The finite-time stability of the closed-loop system is rigorously analyzed with the finite-time Lyapunov stability criterion,validating that all closed-loop signals of the system remain bounded within a finite time.Finally,the effectiveness of the proposed control strategy is verified through a simulation example.
基金supported in part by the National Natural Science Fundation of China under Grant Nos.62403263 and 62373207in part by the Natural Science Fundation of Qingdao,China under Grant No.24-4-4-zrjj-88-jch+1 种基金in part by the Team Plan for Youth Innovation of Universities in Shandong Province under Grant No.2024KJH148in part by the Foundation of Key Laboratory of Autonomous Systems and Networked Control(South China University of Technology),Ministry of Education under Grant No.2024A01.
文摘In this paper,we study the issue of controlling a rotating flexible body-beam system(RFBBS)which consists of a tip mass attached to the free-end and a rigid disk attached to the clamped-end of an Euler-Bernoulli beam.The boundary control input is affected by both unknown disturbance and nonlinear input backlash.First,the input backlash is considered as desired control input combined with a nonlinear input error,converting it to an external disturbance,and then,the control signal is designed through the energy-based control method.Next,the closed-loop system’s stability is analysed through Lyapunov direct method.Finally,the efficacy of the proposed control scheme is tested through numerical simulations utilizing the finite difference method.
基金supported in part by the National Natural Science Foundation of China(W2411061,624B2029)the Graduate Research and Innovation Foundation of Chongqing,China(CYS20069)+1 种基金the Fundamental Research Funds for the Central Universities(2024CDJYXTD-007)the Natural Science Foundation of Chongqing(CSTB2023NSCQ-LZX0026).
文摘This paper proposes a fault-tolerant control scheme for Euler-Lagrange systems that ensures the tracking error decays to a pre-specified accuracy level within a prescribed time period,despite unknown actuation characteristics and potential fading powering faults.By performing deliberately designed coordinate transformations on the tracking error,the complex and demanding problem of“reaching specified precision within a given time”is transformed into a bounded control problem,facilitating the development of the control scheme.To enhance practicality,the design incorporates smooth function fitting and dynamic surface control techniques.Additionally,the proposed control algorithm is robust to faults,effectively handling a combination of fading powering faults and additive actuator faults without requiring additional human intervention.Numerical simulations on a two-link robotic manipulator verify the effectiveness of the proposed control algorithm.
文摘Researchers are increasingly focused on enabling groups of multiple unmanned vehicles to operate cohesively in complex,real-world environments,where coordinated formation control and obstacle avoidance are essential for executing sophisticated collective tasks.This paper presents a Distributed Formation Control and Obstacle Avoidance(DFCOA)framework for multi-unmanned ground vehicles(UGV).DFCOA integrates a virtual leader structure for global guidance,an improved A^(*)path planning algorithm with an advanced cost function for efficient path planning,and a repulsive-force-based improved vector field histogram star(VFH^(*))technique for collision avoidance.The virtual leader generates a reference trajectory while enabling distributed execution;the improved A^(*)algorithm reduces planning time and number of nodes to determine the shortest path from the starting position to the goal;and the improved VFH^(*)uses 2D LiDAR data with inter-agent repulsive force to simultaneously avoid collision with obstacles and maintain safe inter-vehicle distances.The formation stability of the proposed DFCOA reaches 95.8%and 94.6%in two scenarios,with root mean square(RMS)centroid errors of 0.9516 and 1.0008 m,respectively.Velocity tracking is precise(velocity centroid error RMS of 0.2699 and 0.1700 m/s),and linear velocities closely match the desired 0.3 m/s.Safety metrics showed average collision risks of 0.7773 and 0.5143,with minimum inter-vehicle distances of 0.4702 and 0.8763 m,confirming collision-free navigation of four UGVs.DFCOA outperforms conventional methods in formation stability,path efficiency,and scalability,proving its suitability for decentralized multi-UGV applications.
基金funded by the Wuxi Young Scientific and Technological Talent Support Initiative,project number:TJXD-2024-203the Natural Science Foundation of the Jiangsu Higher Education Institutions of China,grant number:24KJB470027.
文摘Iterative Learning Control(ILC)provides an effective framework for optimizing repetitive tasks,making it particularly suitable for high-precision applications in both precision manufacturing and intelligent transportation systems(ITS).This paper presents a systematic review of ILC's developmental progress,current methodologies,and practical implementations across these two critical domains.The review first analyzes the key technical challenges encountered when integrating ILC into precision manufacturing workflows.Through case studies,it evaluates demonstrated improvements in positioning accuracy,surface finish quality,and production throughput.Furthermore,the study examines ILC’s applications in ITS,with particular focus on vehicular motion control applications including autonomous vehicle trajectory tracking,platoon coordination,and traffic signal timing optimization,where its data-driven characteristics enhance adaptability to dynamic environments.Finally,the paper proposes targeted future research directions that are essential for fully realizing ILC’s potential in advancing these interconnected yet distinct fields.
基金partially supported by the Italian Ministry for Research in the framework of the 2020 Program for Research Projects of National Interest(2020RTWES4)。
文摘The Nelder-Mead simplex method is a well-known algorithm enabling the minimization of functions that are not available in closed-form and that need not be differentiable or convex.Furthermore,it is particularly parsimonious on the number of function evaluations,thus making it preferable to convex optimization paradigms in the case,common when dealing with control design problems,that the objective function of the optimization problem is non-differentiable,non-convex,and its closed-form is not available or difficult to be computed analytically.The main goal of this paper is to show how the joint use of the Nelder-Mead simplex method and the Morrison algorithm can be successfully used to solve relevant and challenging control problems that cannot be easily solved using analytic methods.In particular,it is shown how the problems of strong stabilization,static output feedback stabilization,and design of robust controllers having fixed structure can be framed as optimization problems,which,in turn,can be efficiently solved by coupling the two above mentioned algorithms.The performance of this procedure is compared with state-of-the-art techniques on dozens of static output feedback benchmark case studies,and its effectiveness is demonstrated by several examples.
基金Project supported by the National Natural Science Foundation of China(Grant No.12002089)the Science and Technology Projects in Guangzhou(Grant No.2023A04J1323)UKRI Horizon Europe Guarantee(Marie SklodowskaCurie Fellowship)(Grant No.EP/Y016130/1)。
文摘Energy-regenerative suspension combined with piezoelectric and electromagnetic transduction has evolved into a core technological pathway in advancing automotive design paradigms.With the aim of improving energy harvesting performance,time-delayed feedback control is widely used in an energy-regenerative suspension system under different external disturbances in this paper.Meanwhile,limited research has addressed the stochastic dynamics of time-delayed nonlinear energy-regenerative suspension systems.Different from previous studies,this work studies the stochastic response and P-bifurcation of the nonlinear energy-regenerative suspension system with time-delayed feedback control.Firstly,an approximately equivalent dimension reduction system is established by the variable transformation method,and then the stationary probability density function of amplitude is obtained by the stochastic averaging method.Secondly,the precision of the method used in this work is verified by comparing the numerical solutions with the analytical results.Finally,based on the stationary probability density function,the influence of system parameters on stochastic P-bifurcation and the mean output power is discussed.
基金National Natural Science Foundation of China(No.12071370)。
文摘The bipartite containment control problem for heterogeneous nonlinear multi-agent systems(HNMASs)within multi-group networks under signed digraphs is investigated,where the first-order and second-order nonlinear dynamic agents belong to distinct groups.Interactions are cooperative-antagonistic within each group and sign-in-degree balanced across the inter-groups.Firstly,a state feedback control protocol is designed to ensure that the trajectories of followers in diverse groups can converge to distinct convex hulls formed by their corresponding leaders,respectively.As an extension,the bipartite control problem with time-variant formation for the multi-agent system(MAS)is also considered,and a corresponding control protocol with formation compensation vectors is given.Finally,in view of Lyapunov stability theory and matrix inequality,the sufficient conditions for realizing the bipartite containment control are obtained,and several simulations are provided to verify the validity of the above methods.
基金supported by the National Key Research and Development Program of China(No.2022YFC2204400)。
文摘This paper introduces a small perturbation frequency domain thermal analysis model based on the nonlinear dynamics model.The model can be applied to study the high-precision temperature control of thermal systems under low-frequency complex perturbations.The frequency domain characteristics of the space gravitational wave detection satellite are analyzed,and a multi-channel perturbation structure is established.The effects of three kinds of heat flow perturbations,including external heat flow,power generation power,and waste heat of electronic equipment,on the temperature through five transfer paths are investigated.It has been discovered that the waste heat from electronic equipment inside the satellite has the most noticeable effect on the temperature power spectral density of temperature-sensitive optical loads,serving as the primary factor influencing thermal stability.For complex noise signals,the small perturbation analysis method can decompose the different frequency components or ranges,reducing the problem to linearized analysis and simplifying complex calculations.The results indicate that the temperature power spectral density decreases as signal frequency increases,with low-frequency signals exerting a greater influence on temperature stability.The small perturbation analysis method is a novel and effective method for temperature control of space thermal systems,with high accuracy and stability.
基金supported by the National Natural Science Foundation of China under Grant No.61701100.
文摘In scenarios where ground-based cloud computing infrastructure is unavailable,unmanned aerial vehicles(UAVs)act as mobile edge computing(MEC)servers to provide on-demand computation services for ground terminals.To address the challenge of jointly optimizing task scheduling and UAV trajectory under limited resources and high mobility of UAVs,this paper presents PER-MATD3,a multi-agent deep reinforcement learning algorithm with prioritized experience replay(PER)into the Centralized Training with Decentralized Execution(CTDE)framework.Specifically,PER-MATD3 enables each agent to learn a decentralized policy using only local observations during execution,while leveraging a shared replay buffer with prioritized sampling and centralized critic during training to accelerate convergence and improve sample efficiency.Simulation results show that PER-MATD3 reduces average task latency by up to 23%,improves energy efficiency by 21%,and enhances service coverage compared to state-of-the-art baselines,demonstrating its effectiveness and practicality in scenarios without terrestrial networks.
基金supported in part by the National Science and Technology Council under Grant NSTC 114-2221-E-027-104.
文摘Trajectory tracking for nonlinear robotic systems remains a fundamental yet challenging problem in control engineering,particularly when both precision and efficiency must be ensured.Conventional control methods are often effective for stabilization but may not directly optimize long-term performance.To address this limitation,this study develops an integrated framework that combines optimal control principles with reinforcement learning for a single-link robotic manipulator.The proposed scheme adopts an actor–critic structure,where the critic network approximates the value function associated with the Hamilton–Jacobi–Bellman equation,and the actor network generates near-optimal control signals in real time.This dual adaptation enables the controller to refine its policy online without explicit system knowledge.Stability of the closed-loop system is analyzed through Lyapunov theory,ensuring boundedness of the tracking error.Numerical simulations on the single-link manipulator demonstrate that themethod achieves accurate trajectory followingwhile maintaining lowcontrol effort.The results further showthat the actor–critic learning mechanism accelerates convergence of the control policy compared with conventional optimization-based strategies.This work highlights the potential of reinforcement learning integrated with optimal control for robotic manipulators and provides a foundation for future extensions to more complex multi-degree-of-freedom systems.The proposed controller is further validated in a physics-based virtual Gazebo environment,demonstrating stable adaptation and real-time feasibility.
基金supported by the Natural Science Foundation of China No.62303126the project Major Scientific and Technological Special Project of Guizhou Province([2024]014).
文摘This paper investigates the detection and mitigation of coordinated cyberattacks on Load Frequency Control(LFC)systems integrated with Battery Energy Storage Systems(BESS).As renewable energy sources gain greater penetration,power grids are becoming increasingly vulnerable to cyber threats,potentially leading to frequency instability and widespread disruptions.We model two significant attack vectors:load-altering attacks(LAAs)and false data injection attacks(FDIAs)that corrupt frequency measurements.These are analyzed for their impact on grid frequency stability in both linear and nonlinear LFC models,incorporating generation rate constraints and nonlinear loads.A coordinated attack strategy is presented,combining LAAs and FDIAs to achieve stealthiness by concealing frequency deviations from system operators,thereby maximizing disruption while evading traditional detection.To counteract these threats,we propose an Unknown Input Observer(UIO)-based detection framework for linear and nonlinear LFCs.The UIO is designed using linear matrix inequalities(LMIs)to estimate system states while isolating unknown attack inputs,enabling attack detection through monitoring measurement residuals against a predefined threshold.For mitigation,we leverage BESS capabilities with two adaptive strategies:dynamic mitigation for dynamic LAAs,which tunes BESS parameters to enhance the system’s stability margin and accelerate convergence to equilibrium;and staticmitigation for static LAAs and FDIAs.Simulations show that the UIO achieves high detection accuracy,with residuals exceeding thresholds promptly under coordinated attacks,even in nonlinear models.Mitigation strategies reduce frequency deviations by up to 80%compared to unmitigated cases,restoring stability within seconds.
文摘Embodied intelligent systems integrate perception,control,and decision-making within physical agents,and have become a cornerstone of modern aerospace,autonomous driving,and cooperative robotic applications.When operating in uncertain and dynamic environments,such systems must address challenges arising from incomplete sensing,unpredictable maneuvers,communication constraints,disturbances,and evolving network structures.
基金funded by State Grid Corporation of China Central Branch Technology Project(52140024000C).
文摘In wind power transmission via modular multilevel converter based high voltage direct current(MMCHVDC)systems,under traditional control strategies,MMC-HVDCcannot provide inertia support to the receiving-end grid(REG)during disturbances.Moreover,due to the frequency decoupling between the two ends of the MMCHVDC,the sending-end wind farm(SEWF)cannot obtain the frequency variation information of the REG to provide inertia response.Therefore,this paper proposes a novel coordinated source-network-storage inertia control strategy based on wind power transmission via MMC-HVDC system.First,the grid-side MMC station(GS-MMC)maps the frequency variations of the REG to direct current(DC)voltage variations through the frequency mapping control,and uses submodule capacitor energy to provide inertial power.Then,the wind farm-side MMC station(WF-MMC)restores the DC voltage variations to frequency variations through the frequency restoration control and power loss compensation,providing real-time frequency information for the wind farm.Finally,based on real-time frequency information,thewind farmutilizes the rotor kinetic energy and energy storage to provide fast and lasting power support through the wind-storage coordinated inertia control strategy.Meanwhile,when the wind turbines withdraw from the inertia response phase,the energy storage can increase the power output to compensate for the power deficit,preventing secondary frequency drops.Furthermore,this paper uses small-signal analysis to determine the appropriate values for the key parameters of the proposed control strategy.A simulation model of the wind power transmission via MMCHVDC system is built in MATLAB/Simulink environment to validate and evaluate the proposed method.The results show that the proposed coordinated control strategy can effectively improve the system inertia level and avoid the secondary frequency drop under the load sudden increase condition.
基金supported by the State Grid Corporation of China under Grant for Science and Technology Projects(No.SGNXJYOOZWJS2500029).
文摘Energy storage-equipped photovoltaic(PV-storage)systems can meet frequency regulation requirements under various operating conditions,and their coordinated support for grid frequency has become a future trend.To address frequency stability issues caused by low inertia and weak damping,this paper proposes a multi-timescale frequency regulation coordinated control strategy for PV-storage integrated systems.First,a self-synchronizing control strategy for grid-connected inverters is designed based on DC voltage dynamics,enabling active inertia support while transmitting frequency variation information.Next,an energy storage inertia support control strategy is developed to enhance the frequency nadir,and an active frequency support control strategy for PV system considering a frequency regulation deadband is proposed,where the deadband value is determined based on the power regulation margin of synchronous generators,allowing the PV-storage system to adaptively switch between inertia support and primary frequency regulation under different disturbance conditions.This approach ensures system frequency stability while fully leveraging the regulation capabilities of heterogeneous resources.Finally,the real-time digital simulation results of the PV-storage integrated system demonstrate that,compared to existing control methods,the proposed strategy effectively reduces the rate of change of frequency and improves the frequency nadir under various disturbance scenarios,verifying its effectiveness.
基金financially supported by the National Key Research and Development Program of China(2024YFD1700104 and 2022YFE0209200-03)the National Natural Science Foundation of China(42161144002 and 41977156)+3 种基金the Guangxi Natural Science Foundation,China(2022GXNSFBA035625)the Guangxi Technology Base and Talent Subject,China(Guike AD22035927)the Shandong Key Research and Development Project,China(2022TZXD0045)the State Key Laboratory of Earth System Numerical Modeling and Application,Institute of Atmospheric Physics,Chinese Academy of Sciences。
文摘Intervention strategies to control non-point source nitrogen(N)and phosphorus(P)pollution in agriculture are expensive and there is a trade-off between engineering cost and treatment effectiveness.Implementing strategies often result in unsatisfactory outcomes and massive engineering costs when managing diffusive pollution in agricultural catchments.To address this issue,this paper proposes a robust,handy,catchment N&P decision support system(CNPDSS),an Android-based smartphone system integrated with a web-based geographic information system(GIS).The CNPDSS aims to provide artificial intelligence-driven decisions that minimize N&P loadings and engineering costs for mitigating pollution in agricultural catchments.It consists of four components:a general user interface(GUI),GIS,N&P pollution modeling(NPPM),and a DSS.The CNPDSS simplifies the GUI and integrates GIS modules to create a user-friendly interface,enabling non-professional users to operate the system easily through intuitive actions.The NPPM uses straightforward empirical models to predict N&P loadings,enhancing efficiency by avoiding excessive parameters.Taking into account the N&P movement pathway in the catchment,the DSS incorporates three control measures:source reduction in farmland(before migration stage),process retention by ecological ditch(midway transport stage),and down-end purification by constructed wetland(waterbody discharge stage),to formulate a comprehensive ternary controlling strategy.To optimize the cost-effectiveness of any proposed N&P control strategies for sub-catchments,a differential evolution algorithm(DEA)is employed in CNPDSS to carry out a dual-objective decision-making optimization computation.In this study,the CNPDSS is applied to a case study in an agricultural catchment in Central China to develop the most cost-effective ternary N&P control strategies that ensure the catchment water quality within Criterion Ⅲ of the Chinese Surface Water Quality Standard GB3838-2002 is met(total N concentration≤1.0 mg L^(-1)and total P concentration≤0.2 mg L^(-1)).Our results demonstrate that the CNPDSS is feasible and also possesses an adaptive design and flexible architecture to enable its generalization and extension to support strong hands-on applications in other catchments.
