Dear Editor,This letter proposes a reinforcement learning-based predictive learning algorithm for unknown continuous-time nonlinear systems with observation loss.Firstly,we construct a temporal nonzero-sum game over p...Dear Editor,This letter proposes a reinforcement learning-based predictive learning algorithm for unknown continuous-time nonlinear systems with observation loss.Firstly,we construct a temporal nonzero-sum game over predictive control input sequences,deriving multiple optimal predictive control input sequences from its solution.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
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
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
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
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
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
THE power industrial control system(power ICS)is thecore infrastructure that ensures the safe,stable,and efficient operation of power systems.Its architecture typi-cally adopts a hierarchical and partitioned end-edge-...THE power industrial control system(power ICS)is thecore infrastructure that ensures the safe,stable,and efficient operation of power systems.Its architecture typi-cally adopts a hierarchical and partitioned end-edge-cloud collaborative design.However,the large-scale integration ofdistributed renewable energy resources,coupled with the extensivedeployment of sensing and communication devices,has resulted inthe new-type power system characterized by dynamic complexityand high uncertainty[1]-[4].展开更多
This paper investigates the platoon control of heterogeneous vehicular cyber-physical systems(VCPSs) subject to external disturbances by using neural network and uniformly quantized communication data.To reduce the ad...This paper investigates the platoon control of heterogeneous vehicular cyber-physical systems(VCPSs) subject to external disturbances by using neural network and uniformly quantized communication data.To reduce the adverse effects of quantization errors on system performance,a coupling sliding mode surface is established for each following vehicle.The radial basis function(RBF) neural networks are employed to approximate the unknown external disturbances.Then,a novel platoon control law is proposed for cooperative tracking in which each following vehicle only uses the uniformly quantized data of the neighboring vehicles.And the designed controllers in this paper are fully distributed due to the fact that the selection of each vehicle's controller parameters is independent of the entire communication topology.The string stability of VCPSs in the entire control process is ensured rather than only ensuring the string stability after the sliding mode surface converges to zero.Compared with the existing controller design methods and quantization mechanisms,the neural adaptive sliding-mode platoon controller proposed in this paper is superior in performances including tracking errors,driving comfort and fuel economy.Numerical simulations illustrate the effectiveness and superiority of the designed control strategy.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
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
Renewable Energy Systems(RES)provide a sustainable solution to climate warming and environmental pollution by enhancing stability and reliability through status acquisition and analysis on cloud platforms and intellig...Renewable Energy Systems(RES)provide a sustainable solution to climate warming and environmental pollution by enhancing stability and reliability through status acquisition and analysis on cloud platforms and intelligent processing on edge servers(ES).However,securely distributing encrypted data stored in the cloud to terminals that meet decryption requirements has become a prominent research topic.Additionally,managing attributes,including addition,deletion,and modification,is a crucial issue in the access control scheme for RES.To address these security concerns,a trust-based ciphertext-policy attribute-based encryption(CP-ABE)device access control scheme is proposed for RES(TB-CP-ABE).This scheme effectivelymanages the distribution and control of encrypted data on the cloud through robust attribute key management.By introducing trust management mechanisms and outsourced decryption technology,the ES system can effectively assess and manage the trust worthiness of terminal devices,ensuring that only trusted devices can participate in data exchange and access sensitive information.Besides,the ES system dynamically evaluates trust scores to set decryption trust thresholds,thereby regulating device data access permissions and enhancing the system’s security.To validate the security of the proposed TB-CP-ABE against chosen plaintext attacks,a comprehensive formal security analysis is conducted using the widely accepted random oraclemodel under the decisional q-Bilinear Diffie-Hellman Exponent(q-BDHE)assumption.Finally,comparative analysis with other schemes demonstrates that the TB-CP-ABE scheme cuts energy/communication costs by 43%,and scaleswell with rising terminals,maintaining average latency below 50ms,ensuring real-time service feasibility.The proposed scheme not only provides newinsights for the secure management of RES but also lays a foundation for future secure energy solutions.展开更多
In most existing CP-ABE schemes, there is only one authority in the system and all the public keys and private keys are issued by this authority, which incurs ciphertext size and computation costs in the encryption an...In most existing CP-ABE schemes, there is only one authority in the system and all the public keys and private keys are issued by this authority, which incurs ciphertext size and computation costs in the encryption and decryption operations that depend at least linearly on the number of attributes involved in the access policy. We propose an efficient multi-authority CP-ABE scheme in which the authorities need not interact to generate public information during the system initialization phase. Our scheme has constant ciphertext length and a constant number of pairing computations. Our scheme can be proven CPA-secure in random oracle model under the decision q-BDHE assumption. When user's attributes revocation occurs, the scheme transfers most re-encryption work to the cloud service provider, reducing the data owner's computational cost on the premise of security. Finally the analysis and simulation result show that the schemes proposed in this thesis ensure the privacy and secure access of sensitive data stored in the cloud server, and be able to cope with the dynamic changes of users' access privileges in large-scale systems. Besides, the multi-authority ABE eliminates the key escrow problem, achieves the length of ciphertext optimization and enhances the effi ciency of the encryption and decryption operations.展开更多
WITH the rapid development of technologies such as Artificial Intelligence(AI),edge computing,and cloud intelligence,the medical field is undergoing a fundamental transformation[1].These technologies significantly enh...WITH the rapid development of technologies such as Artificial Intelligence(AI),edge computing,and cloud intelligence,the medical field is undergoing a fundamental transformation[1].These technologies significantly enhance the medical system's capability to process complex data and also improve the real-time response rate to patient needs.In this wave of technological innovation,parallel intelligence,along with Artificial systems,Computational experiments,and Parallel execution(ACP)approach[2]will play a crucial role.Through parallel interactions between virtual and real systems,this approach optimizes the functionality of medical devices and instruments,enhancing the accuracy of diagnoses and treatments while enabling the autonomous evolution and adaptive adjustment of medical systems.展开更多
This paper is concerned with event-triggered control of discrete-time systems with or without input saturation.First,an accumulative-error-based event-triggered scheme is devised for control updates.When the accumulat...This paper is concerned with event-triggered control of discrete-time systems with or without input saturation.First,an accumulative-error-based event-triggered scheme is devised for control updates.When the accumulated error between the current state and the latest control update exceeds a certain threshold,an event is triggered.Such a scheme can ensure the event-generator works at a relatively low rate rather than falls into hibernation especially after the system steps into its steady state.Second,the looped functional method for continuous-time systems is extended to discrete-time systems.By introducing an innovative looped functional that links the event-triggered scheme,some sufficient conditions for the co-design of control gain and event-triggered parameters are obtained in terms of linear matrix inequalities with a couple of tuning parameters.Then,the proposed method is applied to discrete-time systems with input saturation.As a result,both suitable control gains and event-triggered parameters are also co-designed to ensure the system trajectories converge to the region of attraction.Finally,an unstable reactor system and an inverted pendulum system are given to show the effectiveness of the proposed method.展开更多
Dear Editor,In this letter,a constrained networked predictive control strategy is proposed for the optimal control problem of complex nonlinear highorder fully actuated(HOFA)systems with noises.The method can effectiv...Dear Editor,In this letter,a constrained networked predictive control strategy is proposed for the optimal control problem of complex nonlinear highorder fully actuated(HOFA)systems with noises.The method can effectively deal with nonlinearities,constraints,and noises in the system,optimize the performance metric,and present an upper bound on the stable output of the system.展开更多
Cyber-physical systems(CPSs)are regarded as the backbone of the fourth industrial revolution,in which communication,physical processes,and computer technology are integrated.In modern industrial systems,CPSs are widel...Cyber-physical systems(CPSs)are regarded as the backbone of the fourth industrial revolution,in which communication,physical processes,and computer technology are integrated.In modern industrial systems,CPSs are widely utilized across various domains,such as smart grids,smart healthcare systems,smart vehicles,and smart manufacturing,among others.Due to their unique spatial distribution,CPSs are highly vulnerable to cyber-attacks,which may result in severe performance degradation and even system instability.Consequently,the security concerns of CPSs have attracted significant attention in recent years.In this paper,a comprehensive survey on the security issues of CPSs under cyber-attacks is provided.Firstly,mathematical descriptions of various types of cyberattacks are introduced in detail.Secondly,two types of secure estimation and control processing schemes,including robust methods and active methods,are reviewed.Thirdly,research findings related to secure control and estimation problems for different types of CPSs are summarized.Finally,the survey is concluded by outlining the challenges and suggesting potential research directions for the future.展开更多
A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source di...A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source disturbances are addressed according to their specific characteristics as follows:(A)an MTN data-driven model,which is used for uncertainty description,is designed accompanied with the mechanism model to represent the unmanned systems;(B)an adaptive MTN filter is used to remove the influence of the internal disturbance;(C)an MTN disturbance observer is constructed to estimate and compensate for the influence of the external disturbance;(D)the Extended Kalman Filter(EKF)algorithm is utilized as the learning mechanism for MTNs.Second,to address the time-delay effect,a recursiveτstep-ahead MTN predictive model is designed utilizing recursive technology,aiming to mitigate the impact of time-delay,and the EKF algorithm is employed as its learning mechanism.Then,the MTN predictive control law is designed based on the quadratic performance index.By implementing the proposed composite controller to unmanned systems,simultaneous feedforward compensation and feedback suppression to the multi-source disturbances are conducted.Finally,the convergence of the MTN and the stability of the closed-loop system are established utilizing the Lyapunov theorem.Two exemplary applications of unmanned systems involving unmanned vehicle and rigid spacecraft are presented to validate the effectiveness of the proposed approach.展开更多
With the rapid development of cloud computing and control theory, a new paradigm of networked control systems called cloud control systems is proposed to meet the requirements of large-scale and complex applications. ...With the rapid development of cloud computing and control theory, a new paradigm of networked control systems called cloud control systems is proposed to meet the requirements of large-scale and complex applications. Currently, cloud control systems are mainly built by using a centralized architecture. The centralized system is overly dependent on the central control plane and has huge challenges in large-scale heterogeneous node systems. In this paper, we propose a decentralized approach to establish cloud control systems by proposing a distributed point-to-point task routing method. A considerable number of tasks in the system will not rely on the central plane and will be directly routed to the target devices through the pointto-point routing method, which improves the horizontal scalability of the cloud control system. The point-to-point routing method directly gives a unique address to every task, making inter-task communication more efficient in a complex heterogeneous and busy cloud control systems. Finally, we experimentally demonstrate that the distributed point-to-point task routing approach is compatible against the state-of-the-art central systems in large-scale task situations.展开更多
基金supported by the National Natural Science Foundation of China(62433014,62373287,62573324,62333005,62273255)in part by the International Exchange Program for Graduate Students of Tongji University(4360143306)+3 种基金in part by the Fundamental Research Funds for Central Universities(22120230311)supported by DeutscheForschungsgemeinschaft(DFG,German Research Foundation)under Germany’s Excellence Strategy(EXC 2075390740016,468094890)support by the Stuttgart Center for Simulation Science(SimTech)the International Max Planck Research School for Intelligent Systems(IMPRS-IS)for supporting Y.Xie。
文摘Dear Editor,This letter proposes a reinforcement learning-based predictive learning algorithm for unknown continuous-time nonlinear systems with observation loss.Firstly,we construct a temporal nonzero-sum game over predictive control input sequences,deriving multiple optimal predictive control input sequences from its solution.
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 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.
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 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partially supported by the National Natural Science Foundation of China(62293500,62293505,62233010,62503240)Natural Science Foundation of Jiangsu Province(BK20250679)。
文摘THE power industrial control system(power ICS)is thecore infrastructure that ensures the safe,stable,and efficient operation of power systems.Its architecture typi-cally adopts a hierarchical and partitioned end-edge-cloud collaborative design.However,the large-scale integration ofdistributed renewable energy resources,coupled with the extensivedeployment of sensing and communication devices,has resulted inthe new-type power system characterized by dynamic complexityand high uncertainty[1]-[4].
基金supported by the National Natural Science Foundation of China(62173079,62473203)Liaoning Provincial Science and Technology Plan Joint Program(2024-MSLH-019)+1 种基金the Education Department of Liaoning Province(LJKMZ20221840)Interdisciplinary project of Dalian University(DLUXK-2024-YB-004)。
文摘This paper investigates the platoon control of heterogeneous vehicular cyber-physical systems(VCPSs) subject to external disturbances by using neural network and uniformly quantized communication data.To reduce the adverse effects of quantization errors on system performance,a coupling sliding mode surface is established for each following vehicle.The radial basis function(RBF) neural networks are employed to approximate the unknown external disturbances.Then,a novel platoon control law is proposed for cooperative tracking in which each following vehicle only uses the uniformly quantized data of the neighboring vehicles.And the designed controllers in this paper are fully distributed due to the fact that the selection of each vehicle's controller parameters is independent of the entire communication topology.The string stability of VCPSs in the entire control process is ensured rather than only ensuring the string stability after the sliding mode surface converges to zero.Compared with the existing controller design methods and quantization mechanisms,the neural adaptive sliding-mode platoon controller proposed in this paper is superior in performances including tracking errors,driving comfort and fuel economy.Numerical simulations illustrate the effectiveness and superiority of the designed control strategy.
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 the Science and Technology Project of the State Grid Corporation of China,Grant number 5700-202223189A-1-1-ZN.
文摘Renewable Energy Systems(RES)provide a sustainable solution to climate warming and environmental pollution by enhancing stability and reliability through status acquisition and analysis on cloud platforms and intelligent processing on edge servers(ES).However,securely distributing encrypted data stored in the cloud to terminals that meet decryption requirements has become a prominent research topic.Additionally,managing attributes,including addition,deletion,and modification,is a crucial issue in the access control scheme for RES.To address these security concerns,a trust-based ciphertext-policy attribute-based encryption(CP-ABE)device access control scheme is proposed for RES(TB-CP-ABE).This scheme effectivelymanages the distribution and control of encrypted data on the cloud through robust attribute key management.By introducing trust management mechanisms and outsourced decryption technology,the ES system can effectively assess and manage the trust worthiness of terminal devices,ensuring that only trusted devices can participate in data exchange and access sensitive information.Besides,the ES system dynamically evaluates trust scores to set decryption trust thresholds,thereby regulating device data access permissions and enhancing the system’s security.To validate the security of the proposed TB-CP-ABE against chosen plaintext attacks,a comprehensive formal security analysis is conducted using the widely accepted random oraclemodel under the decisional q-Bilinear Diffie-Hellman Exponent(q-BDHE)assumption.Finally,comparative analysis with other schemes demonstrates that the TB-CP-ABE scheme cuts energy/communication costs by 43%,and scaleswell with rising terminals,maintaining average latency below 50ms,ensuring real-time service feasibility.The proposed scheme not only provides newinsights for the secure management of RES but also lays a foundation for future secure energy solutions.
基金supported by National Basic Research Program of China(973Program)(2012CB720000)National Natural Science Foundation of China(61225015,61273128)+2 种基金Foundation for Innovative Research Groups of the National Natural Science Foundation of China(61321002)the Ph.D.Programs Foundation of Ministry of Education of China(20111101110012)CAST Foundation(CAST201210)
基金supported by National Natural Science Foundation of China under Grant No.60873231Natural Science Foundation of Jiangsu Province under Grant No.BK2009426+1 种基金Major State Basic Research Development Program of China under Grant No.2011CB302903Key University Science Research Project of Jiangsu Province under Grant No.11KJA520002
文摘In most existing CP-ABE schemes, there is only one authority in the system and all the public keys and private keys are issued by this authority, which incurs ciphertext size and computation costs in the encryption and decryption operations that depend at least linearly on the number of attributes involved in the access policy. We propose an efficient multi-authority CP-ABE scheme in which the authorities need not interact to generate public information during the system initialization phase. Our scheme has constant ciphertext length and a constant number of pairing computations. Our scheme can be proven CPA-secure in random oracle model under the decision q-BDHE assumption. When user's attributes revocation occurs, the scheme transfers most re-encryption work to the cloud service provider, reducing the data owner's computational cost on the premise of security. Finally the analysis and simulation result show that the schemes proposed in this thesis ensure the privacy and secure access of sensitive data stored in the cloud server, and be able to cope with the dynamic changes of users' access privileges in large-scale systems. Besides, the multi-authority ABE eliminates the key escrow problem, achieves the length of ciphertext optimization and enhances the effi ciency of the encryption and decryption operations.
基金supported by the Science and Technology Development Fund,Macao Special Administrative Region(SAR)(0093/2023/RIA2,0145/2023/RIA3).
文摘WITH the rapid development of technologies such as Artificial Intelligence(AI),edge computing,and cloud intelligence,the medical field is undergoing a fundamental transformation[1].These technologies significantly enhance the medical system's capability to process complex data and also improve the real-time response rate to patient needs.In this wave of technological innovation,parallel intelligence,along with Artificial systems,Computational experiments,and Parallel execution(ACP)approach[2]will play a crucial role.Through parallel interactions between virtual and real systems,this approach optimizes the functionality of medical devices and instruments,enhancing the accuracy of diagnoses and treatments while enabling the autonomous evolution and adaptive adjustment of medical systems.
基金supported in part by the National Natural Science Foundation of China(62473221)the Natural Science Foundation of Shandong Province,China(ZR2024MF006)Qingdao Natural Science Foundation(24-4-4-zrjj-165-jch)。
文摘This paper is concerned with event-triggered control of discrete-time systems with or without input saturation.First,an accumulative-error-based event-triggered scheme is devised for control updates.When the accumulated error between the current state and the latest control update exceeds a certain threshold,an event is triggered.Such a scheme can ensure the event-generator works at a relatively low rate rather than falls into hibernation especially after the system steps into its steady state.Second,the looped functional method for continuous-time systems is extended to discrete-time systems.By introducing an innovative looped functional that links the event-triggered scheme,some sufficient conditions for the co-design of control gain and event-triggered parameters are obtained in terms of linear matrix inequalities with a couple of tuning parameters.Then,the proposed method is applied to discrete-time systems with input saturation.As a result,both suitable control gains and event-triggered parameters are also co-designed to ensure the system trajectories converge to the region of attraction.Finally,an unstable reactor system and an inverted pendulum system are given to show the effectiveness of the proposed method.
基金supported in part by the National Natural Science Foundation of China(62173255,62188101)Shenzhen Key Laboratory of Control Theory and Intelligent Systems(ZDSYS20220330161800001)
文摘Dear Editor,In this letter,a constrained networked predictive control strategy is proposed for the optimal control problem of complex nonlinear highorder fully actuated(HOFA)systems with noises.The method can effectively deal with nonlinearities,constraints,and noises in the system,optimize the performance metric,and present an upper bound on the stable output of the system.
文摘Cyber-physical systems(CPSs)are regarded as the backbone of the fourth industrial revolution,in which communication,physical processes,and computer technology are integrated.In modern industrial systems,CPSs are widely utilized across various domains,such as smart grids,smart healthcare systems,smart vehicles,and smart manufacturing,among others.Due to their unique spatial distribution,CPSs are highly vulnerable to cyber-attacks,which may result in severe performance degradation and even system instability.Consequently,the security concerns of CPSs have attracted significant attention in recent years.In this paper,a comprehensive survey on the security issues of CPSs under cyber-attacks is provided.Firstly,mathematical descriptions of various types of cyberattacks are introduced in detail.Secondly,two types of secure estimation and control processing schemes,including robust methods and active methods,are reviewed.Thirdly,research findings related to secure control and estimation problems for different types of CPSs are summarized.Finally,the survey is concluded by outlining the challenges and suggesting potential research directions for the future.
基金co-supported by the National Key R&D Program of China(No.2023YFB4704400)the Zhejiang Provincial Natural Science Foundation of China(No.LQ24F030012)the National Natural Science Foundation of China General Project(No.62373033)。
文摘A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source disturbances are addressed according to their specific characteristics as follows:(A)an MTN data-driven model,which is used for uncertainty description,is designed accompanied with the mechanism model to represent the unmanned systems;(B)an adaptive MTN filter is used to remove the influence of the internal disturbance;(C)an MTN disturbance observer is constructed to estimate and compensate for the influence of the external disturbance;(D)the Extended Kalman Filter(EKF)algorithm is utilized as the learning mechanism for MTNs.Second,to address the time-delay effect,a recursiveτstep-ahead MTN predictive model is designed utilizing recursive technology,aiming to mitigate the impact of time-delay,and the EKF algorithm is employed as its learning mechanism.Then,the MTN predictive control law is designed based on the quadratic performance index.By implementing the proposed composite controller to unmanned systems,simultaneous feedforward compensation and feedback suppression to the multi-source disturbances are conducted.Finally,the convergence of the MTN and the stability of the closed-loop system are established utilizing the Lyapunov theorem.Two exemplary applications of unmanned systems involving unmanned vehicle and rigid spacecraft are presented to validate the effectiveness of the proposed approach.
基金supported by the National Key Research and Development Program of China (2018AAA0103203)the National Natural Science Foundation of China (62073036,61836001,62102022,62122014)the Beijing Natural Science Foundation of China (42020741)。
文摘With the rapid development of cloud computing and control theory, a new paradigm of networked control systems called cloud control systems is proposed to meet the requirements of large-scale and complex applications. Currently, cloud control systems are mainly built by using a centralized architecture. The centralized system is overly dependent on the central control plane and has huge challenges in large-scale heterogeneous node systems. In this paper, we propose a decentralized approach to establish cloud control systems by proposing a distributed point-to-point task routing method. A considerable number of tasks in the system will not rely on the central plane and will be directly routed to the target devices through the pointto-point routing method, which improves the horizontal scalability of the cloud control system. The point-to-point routing method directly gives a unique address to every task, making inter-task communication more efficient in a complex heterogeneous and busy cloud control systems. Finally, we experimentally demonstrate that the distributed point-to-point task routing approach is compatible against the state-of-the-art central systems in large-scale task situations.
