It’s possible for malicious operators to seize hold of electrical control systems, for instance, the engine control unit of driverless vehicles, from various vectors, e.g. autonomic control system, remote vehicle acc...It’s possible for malicious operators to seize hold of electrical control systems, for instance, the engine control unit of driverless vehicles, from various vectors, e.g. autonomic control system, remote vehicle access, or human drivers. To mitigate potential risks, this paper provides the inauguration study by proposing a theoretical framework in the physical, human and cyber triad. Its goal is to, at each time point, detect adversary control behaviors and protect control systems against malicious operations via integrating a variety of methods. This paper only proposes a theoretical framework which tries to indicate possible threats. With the support of the framework, the security system can lightly reduce the risk. The development and implementation of the system are out of scope.展开更多
Dear Editor,This letter presents an improved repetitive controller(IRC) that uses a complex-coefficient filter to enhance the tracking performance of a system for periodic signals. Compared with the low-pass filter us...Dear Editor,This letter presents an improved repetitive controller(IRC) that uses a complex-coefficient filter to enhance the tracking performance of a system for periodic signals. Compared with the low-pass filter used in the conventional repetitive controller(CRC), the complex-coefficient filter causes less change in the phase and amplitude of a signal at the frequencies of the periodic signal, especially at the fundamental frequency, when the two filters have the same cutofffrequency.展开更多
In view of the deficiencies in aspects such as failure rate requirements and analysis assumptions of advisory circular,this paper investigates the sources of high safety requirements,and the top-down design method for...In view of the deficiencies in aspects such as failure rate requirements and analysis assumptions of advisory circular,this paper investigates the sources of high safety requirements,and the top-down design method for the flight control system life cycle.Correspondingly,measures are proposed,including enhancing the safety target value to 10^(−10)per flight hour and implementing development assurance.In view of the shortcomings of mainstream aircraft flight control systems,such as weak backup capability and complex fault reconfiguration logic,improvements have been made to the system’s operating modes,control channel allocation,and common mode failure mitigation schemes based on the existing flight control architecture.The flight control design trends and philosophies have been analyzed.A flight control system architecture scheme is proposed,which includes three operating modes and multi-level voters/monitors,three main control channels,and a backup system independent of the main control system,which has been confirmed through functional modeling simulations.The proposed method plays an important role in the architecture design of safety-critical flight control system.展开更多
Addressing issues such as the disconnect between theory and practice and low student engagement in control system education,this paper uses the course“The Working Process of Open-Loop Control Systems”as a case study...Addressing issues such as the disconnect between theory and practice and low student engagement in control system education,this paper uses the course“The Working Process of Open-Loop Control Systems”as a case study to explore the integration of AI technology with the“project-guided and task-driven”teaching model.By constructing a four-dimensional teaching framework of“situation-task-activity-evaluation,”AI tools are embedded in project practices such as the construction of a mechanical timed flower watering device and the optimization of a digital timed flower watering device,achieving precision,interactivity,and personalization in the teaching process.Teaching practice demonstrates that this model significantly enhances students’technical awareness,materialization capabilities,and engineering thinking,providing a reference for the teaching reform of technical courses in high school education.展开更多
Aiming at the problems of poor adaptability and insufficient fault prediction of traditional mechanical automation control systems in complex working conditions,a mechanical automation control system based on artifici...Aiming at the problems of poor adaptability and insufficient fault prediction of traditional mechanical automation control systems in complex working conditions,a mechanical automation control system based on artificial intelligence is designed.This design integrates expert control,fuzzy control,and neural network control technologies,and builds a hierarchical distributed architecture.Fault warning adopts threshold judgment and dynamic time warping pattern recognition technologies,and state monitoring realizes accurate analysis through multi-source data fusion and Kalman filtering algorithm.Practical applications show that this system can reduce the equipment failure rate by more than 30%.With the help of intelligent scheduling optimization,it can significantly improve production efficiency and reduce energy consumption,providing a reliable technical solution and practical path for the intelligent upgrade of the mechanical automation field.展开更多
Aquila Optimizer(AO)is a recently proposed population-based optimization technique inspired by Aquila’s behavior in catching prey.AO is applied in various applications and its numerous variants were proposed in the l...Aquila Optimizer(AO)is a recently proposed population-based optimization technique inspired by Aquila’s behavior in catching prey.AO is applied in various applications and its numerous variants were proposed in the literature.However,chaos theory has not been extensively investigated in AO.Moreover,it is still not applied in the parameter estimation of electro-hydraulic systems.In this work,ten well-defined chaotic maps were integrated into a narrowed exploitation of AO for the development of a robust chaotic optimization technique.An extensive investigation of twenty-three mathematical benchmarks and ten IEEE Congress on Evolutionary Computation(CEC)functions shows that chaotic Aquila optimization techniques perform better than the baseline technique.The investigation is further conducted on parameter estimation of an electro-hydraulic control system,which is performed on various noise levels and shows that the proposed chaotic AO with Piecewise map(CAO6)achieves the best fitness values of and at noise levels and respectively.Friedman test 2.873E-05,1.014E-04,8.728E-031.300E-03,1.300E-02,1.300E-01,for repeated measures,computational analysis,and Taguchi test reflect the superiority of CAO6 against the state of the arts,demonstrating its potential for addressing various engineering optimization problems.However,the sensitivity to parameter tuning may limit its direct application to complex optimization scenarios.展开更多
With the advancement of more electric aircraft(MEA)technology,the application of electro-hydrostatic actuators(EHAs)in aircraft actuation systems has become increasingly prevalent.This paper focuses on the modeling an...With the advancement of more electric aircraft(MEA)technology,the application of electro-hydrostatic actuators(EHAs)in aircraft actuation systems has become increasingly prevalent.This paper focuses on the modeling and mode switching analysis of EHA used in the primary flight control actuation systems of large aircraft,addressing the challenges associated with mode switching.First,we analyze the functional architecture and operational characteristics of multi-mode EHA,and sumarize the operating modes and implementation methods.Based on the EHA system architecture,we then develop a theoretical mathematical model and a simulation model.Using the simulation model,we analyze the performance of the EHA during normal operation.Finally,the performance of the EHA during mode switching under various functional switching scenarios is investigated.The results indicate that the EHA meets the performance requirements in terms of accuracy,bandwidth,and load capacity.Additionally,the hydraulic cylinder operates smoothly during the EHA mode switching,and the response time for switching between different modes is less than the specified threshold.These findings validate the system performance of multi-mode EHA,which helps to improve the reliability of EHA and the safety of aircraft flight control systems.展开更多
Micro/nanorobots have exhibited excellent application potential in the biomedical field,such as drug delivery,minimally invasive surgery,and bio-sensing.Furthermore,in order to achieve practical application,it is esse...Micro/nanorobots have exhibited excellent application potential in the biomedical field,such as drug delivery,minimally invasive surgery,and bio-sensing.Furthermore,in order to achieve practical application,it is essential for swimming micro/nanorobots to navigate towards specific targets or adjust their speed and morphology in complete environments.The navigation of swimming micro/nanorobots with temporal and spatial precision is critical for fulfilling the demand of applications.Here,we introduced a fully integrated wearable control system for micro/nanorobots navigation and manipulation,which is composed of a multifunctional sensor array,an artificial intelligence(AI)planner,and a magnetic field generator.The sensor array could perceive real-time changes in gestures,wrist rotation,and acoustic signals.AI planner based on machine learning offers adaptive path planning in response to dynamically changing signals to generate magnetic fields for the on-demand manipulation of micro/nanorobots.Such a novel,feasible control strategy was validated in the biological experiment in which cancer cells were targeted and killed by photothermal therapy using micro/nanorobots and integrated control platform.This wearable control system could play a crucial role in future intelligent medical applications and could be easily reconfigured toward other medical robots’control.展开更多
With the integration of informatization and intelligence into the Communication-Based Train Control(CBTC)systems,the system is facing an increasing number of information security threats.As an important method of char...With the integration of informatization and intelligence into the Communication-Based Train Control(CBTC)systems,the system is facing an increasing number of information security threats.As an important method of characterizing the system security status,the security situation assessment is used to analyze the system security situation.However,existing situation assessment methods fail to integrate the coupling relationship between the physical layer and the information layer of the CBTC systems,and cannot dynamically characterize the real-time security situation changes under cyber attacks.In this paper,a hierarchical security situation assessment approach is proposed to address the security challenges of CBTC systems,which can perceive cyber attacks,quantify the security situation,and characterize the security situation changes under cyber attacks.Specifically,for the physical layer ofCBTC systems,the impact of cyber attacks is evaluated with the train punctuality rate and train departure interval indicators.For the information layer of CBTC systems,the system vulnerabilities and system threats are selected as static level indicators,and the critical network characteristics are selected as dynamic level indicators to quantify the real-time security situation.Finally,the comprehensive security situation assessment value of the CBTC systems is obtained by integrating the physical and information layer indicators.Simulation results illustrate that the proposed approach can dynamically characterize the real-time security situation of CBTC systems,enhancing the ability to perceive and assess information security risks.展开更多
To handle input and output time delays that commonly exist in many networked control systems(NCSs), a new robust continuous sliding mode control(CSMC) scheme is proposed for the output tracking in uncertain single inp...To handle input and output time delays that commonly exist in many networked control systems(NCSs), a new robust continuous sliding mode control(CSMC) scheme is proposed for the output tracking in uncertain single input-single-output(SISO) networked control systems. This scheme consists of three consecutive steps. First, although the network-induced delay in those systems can be effectively handled by using Pade approximation(PA), the unmatched disturbance cames out as another difficulty in the control design. Second, to actively estimate this unmatched disturbance, a generalized proportional integral observer(GPIO) technique is utilized based on only one measured state. Third, by constructing a new sliding manifold with the aid of the estimated unmatched disturbance and states, a GPIO-based CSMC is synthesized, which is employed to cope with not only matched and unmatched disturbances, but also networkinduced delays. The stability of the entire closed-loop system under the proposed GPIO-based CSMC is detailedly analyzed.The promising tracking efficiency and feasibility of the proposed control methodology are verified through simulations and experiments on Quanser's servo module for motion control under various test conditions.展开更多
In the production processes of modern industry,accurate assessment of the system’s health state and traceability non-optimal factors are key to ensuring“safe,stable,long-term,full load and optimal”operation of the ...In the production processes of modern industry,accurate assessment of the system’s health state and traceability non-optimal factors are key to ensuring“safe,stable,long-term,full load and optimal”operation of the production process.The benzene-to-ethylene ratio control system is a complex system based on anMPC-PID doublelayer architecture.Taking into consideration the interaction between levels,coupling between loops and conditions of incomplete operation data,this paper proposes a health assessment method for the dual-layer control system by comprehensively utilizing deep learning technology.Firstly,according to the results of the pre-assessment of the system layers and loops bymultivariate statisticalmethods,seven characteristic parameters that have a significant impact on the health state of the system are identified.Next,aiming at the problem of incomplete assessment data set due to the uneven distribution of actual system operating health state,the original unbalanced dataset is augmented using aWasserstein generative adversarial network with gradient penalty term,and a complete dataset is obtained to characterise all the health states of the system.On this basis,a new deep learning-based health assessment framework for the benzeneto-ethylene ratio control system is constructed based on traditionalmultivariate statistical assessment.This framework can overcome the shortcomings of the linear weighted fusion related to the coupling and nonlinearity of the subsystem health state at different layers,and reduce the dependence of the prior knowledge.Furthermore,by introducing a dynamic attention mechanism(AM)into the convolutional neural network(CNN),the assessment model integrating both assessment and traceability is constructed,which can achieve the health assessment and trace the non-optimal factors of the complex control systems with the double-layer architecture.Finally,the effectiveness and superiority of the proposed method have been verified by the benzene-ethylene ratio control system of the alkylation process unit in a styrene plant.展开更多
Dear Editor,This letter presents a joint probabilistic scheduling and resource allocation method(PSRA) for 5G-based wireless networked control systems(WNCSs). As a control-aware optimization method, PSRA minimizes the...Dear Editor,This letter presents a joint probabilistic scheduling and resource allocation method(PSRA) for 5G-based wireless networked control systems(WNCSs). As a control-aware optimization method, PSRA minimizes the linear quadratic Gaussian(LQG) control cost of WNCSs by optimizing the activation probability of subsystems, the number of uplink repetitions, and the durations of uplink and downlink phases. Simulation results show that PSRA achieves smaller LQG control costs than existing works.展开更多
This paper investigates the problem of optimal secure control for networked control systems under hybrid attacks.A control strategy based on the Stackelberg game framework is proposed,which differs from conventional m...This paper investigates the problem of optimal secure control for networked control systems under hybrid attacks.A control strategy based on the Stackelberg game framework is proposed,which differs from conventional methods by considering both denial-of-service(DoS)and false data injection(FDI)attacks simultaneously.Additionally,the stability conditions for the system under these hybrid attacks are established.It is technically challenging to design the control strategy by predicting attacker actions based on Stcakelberg game to ensure the system stability under hybrid attacks.Another technical difficulty lies in establishing the conditions for mean-square asymptotic stability due to the complexity of the attack scenarios Finally,simulations on an unstable batch reactor system under hybrid attacks demonstrate the effectiveness of the proposed strategy.展开更多
This article studies the existence and uniqueness of the mild solution of a family of control systems with a delay that are governed by the nonlinear fractional evolution differential equations in Banach spaces.Moreov...This article studies the existence and uniqueness of the mild solution of a family of control systems with a delay that are governed by the nonlinear fractional evolution differential equations in Banach spaces.Moreover,we establish the controllability of the considered system.To do so,first,we investigate the approximate controllability of the corresponding linear system.Subsequently,we prove the nonlinear system is approximately controllable if the corresponding linear system is approximately controllable.To reach the conclusions,the theory of resolvent operators,the Banach contraction mapping principle,and fixed point theorems are used.While concluding,some examples are given to demonstrate the efficacy of the proposed results.展开更多
This paper introduces the Integrated Security Embedded Resilience Architecture (ISERA) as an advanced resilience mechanism for Industrial Control Systems (ICS) and Operational Technology (OT) environments. The ISERA f...This paper introduces the Integrated Security Embedded Resilience Architecture (ISERA) as an advanced resilience mechanism for Industrial Control Systems (ICS) and Operational Technology (OT) environments. The ISERA framework integrates security by design principles, micro-segmentation, and Island Mode Operation (IMO) to enhance cyber resilience and ensure continuous, secure operations. The methodology deploys a Forward-Thinking Architecture Strategy (FTAS) algorithm, which utilises an industrial Intrusion Detection System (IDS) implemented with Python’s Network Intrusion Detection System (NIDS) library. The FTAS algorithm successfully identified and responded to cyber-attacks, ensuring minimal system disruption. ISERA has been validated through comprehensive testing scenarios simulating Denial of Service (DoS) attacks and malware intrusions, at both the IT and OT layers where it successfully mitigates the impact of malicious activity. Results demonstrate ISERA’s efficacy in real-time threat detection, containment, and incident response, thus ensuring the integrity and reliability of critical infrastructure systems. ISERA’s decentralised approach contributes to global net zero goals by optimising resource use and minimising environmental impact. By adopting a decentralised control architecture and leveraging virtualisation, ISERA significantly enhances the cyber resilience and sustainability of critical infrastructure systems. This approach not only strengthens defences against evolving cyber threats but also optimises resource allocation, reducing the system’s carbon footprint. As a result, ISERA ensures the uninterrupted operation of essential services while contributing to broader net zero goals.展开更多
In this paper,a new study concerning the usage of artificial neural networks in the control application is given.It is shown,that the data gathered during proper operation of a given control plant can be used in the l...In this paper,a new study concerning the usage of artificial neural networks in the control application is given.It is shown,that the data gathered during proper operation of a given control plant can be used in the learning process to fully embrace the control pattern.Interestingly,the instances driven by neural networks have the ability to outperform the original analytically driven scenarios.Three different control schemes,namely perfect,linear-quadratic,and generalized predictive controllers were used in the theoretical study.In addition,the nonlinear recurrent neural network-based generalized predictive controller with the radial basis function-originated predictor was obtained to exemplify the main results of the paper regarding the real-world application.展开更多
This article presents an adaptive optimal control method for a semi-active suspension system.The model of the suspension system is built,in which the components of uncertain parameters and exogenous disturbance are de...This article presents an adaptive optimal control method for a semi-active suspension system.The model of the suspension system is built,in which the components of uncertain parameters and exogenous disturbance are described.The adaptive optimal control law consists of the sum of the optimal control component and the adaptive control component.First,the optimal control law is designed for the model of the suspension system after ignoring the components of uncertain parameters and exogenous disturbance caused by the road surface.The optimal control law expresses the desired dynamic characteristics of the suspension system.Next,the adaptive component is designed with the purpose of compensating for the effects caused by uncertain parameters and exogenous disturbance caused by the road surface;the adaptive component has adaptive parameter rules to estimate uncertain parameters and exogenous disturbance.When exogenous disturbances are eliminated,the system responds with an optimal controller designed.By separating theoretically the dynamic of a semi-active suspension system,this solution allows the design of two separate controllers easily and has reduced the computational burden and the use of too many tools,thus allowing for more convenient hardware implementation.The simulation results also show the effectiveness of damping oscillations of the proposed solution in this article.展开更多
Background Interconnection of different power systems has a major effect on system stability.This study aims to design an optimal load frequency control(LFC)system based on a proportional-integral(PI)controller for a ...Background Interconnection of different power systems has a major effect on system stability.This study aims to design an optimal load frequency control(LFC)system based on a proportional-integral(PI)controller for a two-area power system.Methods Two areas were connected through an AC tie line in parallel with a DC link to stabilize the frequency of oscillations in both areas.The PI parameters were tuned using the cuckoo search algorithm(CSA)to minimize the integral absolute error(IAE).A state matrix was provided,and the stability of the system was verified by calculating the eigenvalues.The frequency response was investigated for load variation,changes in the generator rate constraint,the turbine time constant,and the governor time constant.Results The CSA was compared with particle swarm optimization algorithm(PSO)under identical conditions.The system was modeled based on a state-space mathematical representation and simulated using MATLAB.The results demonstrated the effectiveness of the proposed controller based on both algorithms and,it is clear that CSA is superior to PSO.Conclusion The CSA algorithm smoothens the system response,reduces ripples,decreases overshooting and settling time,and improves the overall system performance under different disturbances.展开更多
Fiber quality measurement in spinning preparation is crucial for optimizing waste and meeting yarn quality specifications.The brand-new Uster AFIS 6–the next-generation laboratory instrument from Uster Technologies–...Fiber quality measurement in spinning preparation is crucial for optimizing waste and meeting yarn quality specifications.The brand-new Uster AFIS 6–the next-generation laboratory instrument from Uster Technologies–uniquely tests man-made fiber properties in addition to cotton.It provides critical data to optimize fiber process control for cotton,man-made fibers,and blended yarns.展开更多
Permanent magnet synchronous motor(PMSM)is widely used in alternating current servo systems as it provides high eficiency,high power density,and a wide speed regulation range.The servo system is placing higher demands...Permanent magnet synchronous motor(PMSM)is widely used in alternating current servo systems as it provides high eficiency,high power density,and a wide speed regulation range.The servo system is placing higher demands on its control performance.The model predictive control(MPC)algorithm is emerging as a potential high-performance motor control algorithm due to its capability of handling multiple-input and multipleoutput variables and imposed constraints.For the MPC used in the PMSM control process,there is a nonlinear disturbance caused by the change of electromagnetic parameters or load disturbance that may lead to a mismatch between the nominal model and the controlled object,which causes the prediction error and thus affects the dynamic stability of the control system.This paper proposes a data-driven MPC strategy in which the historical data in an appropriate range are utilized to eliminate the impact of parameter mismatch and further improve the control performance.The stability of the proposed algorithm is proved as the simulation demonstrates the feasibility.Compared with the classical MPC strategy,the superiority of the algorithm has also been verified.展开更多
文摘It’s possible for malicious operators to seize hold of electrical control systems, for instance, the engine control unit of driverless vehicles, from various vectors, e.g. autonomic control system, remote vehicle access, or human drivers. To mitigate potential risks, this paper provides the inauguration study by proposing a theoretical framework in the physical, human and cyber triad. Its goal is to, at each time point, detect adversary control behaviors and protect control systems against malicious operations via integrating a variety of methods. This paper only proposes a theoretical framework which tries to indicate possible threats. With the support of the framework, the security system can lightly reduce the risk. The development and implementation of the system are out of scope.
基金supported in part by the National Natural Science Foundation of China(61873348,6230 3266,62273200)JSPS(Japan Society for the Promotion of Science) KAKENHI(22H03998,23K25252)
文摘Dear Editor,This letter presents an improved repetitive controller(IRC) that uses a complex-coefficient filter to enhance the tracking performance of a system for periodic signals. Compared with the low-pass filter used in the conventional repetitive controller(CRC), the complex-coefficient filter causes less change in the phase and amplitude of a signal at the frequencies of the periodic signal, especially at the fundamental frequency, when the two filters have the same cutofffrequency.
文摘In view of the deficiencies in aspects such as failure rate requirements and analysis assumptions of advisory circular,this paper investigates the sources of high safety requirements,and the top-down design method for the flight control system life cycle.Correspondingly,measures are proposed,including enhancing the safety target value to 10^(−10)per flight hour and implementing development assurance.In view of the shortcomings of mainstream aircraft flight control systems,such as weak backup capability and complex fault reconfiguration logic,improvements have been made to the system’s operating modes,control channel allocation,and common mode failure mitigation schemes based on the existing flight control architecture.The flight control design trends and philosophies have been analyzed.A flight control system architecture scheme is proposed,which includes three operating modes and multi-level voters/monitors,three main control channels,and a backup system independent of the main control system,which has been confirmed through functional modeling simulations.The proposed method plays an important role in the architecture design of safety-critical flight control system.
文摘Addressing issues such as the disconnect between theory and practice and low student engagement in control system education,this paper uses the course“The Working Process of Open-Loop Control Systems”as a case study to explore the integration of AI technology with the“project-guided and task-driven”teaching model.By constructing a four-dimensional teaching framework of“situation-task-activity-evaluation,”AI tools are embedded in project practices such as the construction of a mechanical timed flower watering device and the optimization of a digital timed flower watering device,achieving precision,interactivity,and personalization in the teaching process.Teaching practice demonstrates that this model significantly enhances students’technical awareness,materialization capabilities,and engineering thinking,providing a reference for the teaching reform of technical courses in high school education.
文摘Aiming at the problems of poor adaptability and insufficient fault prediction of traditional mechanical automation control systems in complex working conditions,a mechanical automation control system based on artificial intelligence is designed.This design integrates expert control,fuzzy control,and neural network control technologies,and builds a hierarchical distributed architecture.Fault warning adopts threshold judgment and dynamic time warping pattern recognition technologies,and state monitoring realizes accurate analysis through multi-source data fusion and Kalman filtering algorithm.Practical applications show that this system can reduce the equipment failure rate by more than 30%.With the help of intelligent scheduling optimization,it can significantly improve production efficiency and reduce energy consumption,providing a reliable technical solution and practical path for the intelligent upgrade of the mechanical automation field.
基金funded by Taif University,Saudi Arabia,Project No.(TU-DSPP-2024-52).
文摘Aquila Optimizer(AO)is a recently proposed population-based optimization technique inspired by Aquila’s behavior in catching prey.AO is applied in various applications and its numerous variants were proposed in the literature.However,chaos theory has not been extensively investigated in AO.Moreover,it is still not applied in the parameter estimation of electro-hydraulic systems.In this work,ten well-defined chaotic maps were integrated into a narrowed exploitation of AO for the development of a robust chaotic optimization technique.An extensive investigation of twenty-three mathematical benchmarks and ten IEEE Congress on Evolutionary Computation(CEC)functions shows that chaotic Aquila optimization techniques perform better than the baseline technique.The investigation is further conducted on parameter estimation of an electro-hydraulic control system,which is performed on various noise levels and shows that the proposed chaotic AO with Piecewise map(CAO6)achieves the best fitness values of and at noise levels and respectively.Friedman test 2.873E-05,1.014E-04,8.728E-031.300E-03,1.300E-02,1.300E-01,for repeated measures,computational analysis,and Taguchi test reflect the superiority of CAO6 against the state of the arts,demonstrating its potential for addressing various engineering optimization problems.However,the sensitivity to parameter tuning may limit its direct application to complex optimization scenarios.
基金supported by the Chinese Civil Aircraft Project(No.MJ-2017-S49).
文摘With the advancement of more electric aircraft(MEA)technology,the application of electro-hydrostatic actuators(EHAs)in aircraft actuation systems has become increasingly prevalent.This paper focuses on the modeling and mode switching analysis of EHA used in the primary flight control actuation systems of large aircraft,addressing the challenges associated with mode switching.First,we analyze the functional architecture and operational characteristics of multi-mode EHA,and sumarize the operating modes and implementation methods.Based on the EHA system architecture,we then develop a theoretical mathematical model and a simulation model.Using the simulation model,we analyze the performance of the EHA during normal operation.Finally,the performance of the EHA during mode switching under various functional switching scenarios is investigated.The results indicate that the EHA meets the performance requirements in terms of accuracy,bandwidth,and load capacity.Additionally,the hydraulic cylinder operates smoothly during the EHA mode switching,and the response time for switching between different modes is less than the specified threshold.These findings validate the system performance of multi-mode EHA,which helps to improve the reliability of EHA and the safety of aircraft flight control systems.
基金supported by the National Key Research and Development Program(2022YFB4701700)National Excellent Youth Science Fund Project of the National Natural Science Foundation of China(52322502)+6 种基金the National Outstanding Youth Science Fund Project of National Natural Science Foundation of China(52025054)National Natural Science Foundation of China(52175009),Postdoctoral Fellowship Program of CPSF(GZC20232498)Postdoctoral Innovative Talents in Shandong Province(SDBX2023011)China Postdoctoral Science Foundation Grant(2023M733341)Key R&D Program of Shandong Province,China(2021ZLGX04)National Heilongjiang Providence Nature Science Foundation of China(YQ2022E022)Fundamental Research Funds for the Central Universities。
文摘Micro/nanorobots have exhibited excellent application potential in the biomedical field,such as drug delivery,minimally invasive surgery,and bio-sensing.Furthermore,in order to achieve practical application,it is essential for swimming micro/nanorobots to navigate towards specific targets or adjust their speed and morphology in complete environments.The navigation of swimming micro/nanorobots with temporal and spatial precision is critical for fulfilling the demand of applications.Here,we introduced a fully integrated wearable control system for micro/nanorobots navigation and manipulation,which is composed of a multifunctional sensor array,an artificial intelligence(AI)planner,and a magnetic field generator.The sensor array could perceive real-time changes in gestures,wrist rotation,and acoustic signals.AI planner based on machine learning offers adaptive path planning in response to dynamically changing signals to generate magnetic fields for the on-demand manipulation of micro/nanorobots.Such a novel,feasible control strategy was validated in the biological experiment in which cancer cells were targeted and killed by photothermal therapy using micro/nanorobots and integrated control platform.This wearable control system could play a crucial role in future intelligent medical applications and could be easily reconfigured toward other medical robots’control.
基金supported in part by the project of the State Key Laboratory of Advanced Rail Autonomous Operation(RAO2023ZZ004)in part by the Beijing Natural Science Foundation-Fengtai Rail Transit Frontier Research Joint Fund(L211002)+2 种基金in part by the Foundation of China State Railway Group Corporation Limited under Grant L2021G003in part by the Scientific and Technical Research Fund of China Academy of Railway Sciences Corporation Limited under Grant 2021YJ094in part by the Project I23L00200 and Project I24F00010.
文摘With the integration of informatization and intelligence into the Communication-Based Train Control(CBTC)systems,the system is facing an increasing number of information security threats.As an important method of characterizing the system security status,the security situation assessment is used to analyze the system security situation.However,existing situation assessment methods fail to integrate the coupling relationship between the physical layer and the information layer of the CBTC systems,and cannot dynamically characterize the real-time security situation changes under cyber attacks.In this paper,a hierarchical security situation assessment approach is proposed to address the security challenges of CBTC systems,which can perceive cyber attacks,quantify the security situation,and characterize the security situation changes under cyber attacks.Specifically,for the physical layer ofCBTC systems,the impact of cyber attacks is evaluated with the train punctuality rate and train departure interval indicators.For the information layer of CBTC systems,the system vulnerabilities and system threats are selected as static level indicators,and the critical network characteristics are selected as dynamic level indicators to quantify the real-time security situation.Finally,the comprehensive security situation assessment value of the CBTC systems is obtained by integrating the physical and information layer indicators.Simulation results illustrate that the proposed approach can dynamically characterize the real-time security situation of CBTC systems,enhancing the ability to perceive and assess information security risks.
基金supported in part by the Australian Research Council Discovery Project(DP190101557)
文摘To handle input and output time delays that commonly exist in many networked control systems(NCSs), a new robust continuous sliding mode control(CSMC) scheme is proposed for the output tracking in uncertain single input-single-output(SISO) networked control systems. This scheme consists of three consecutive steps. First, although the network-induced delay in those systems can be effectively handled by using Pade approximation(PA), the unmatched disturbance cames out as another difficulty in the control design. Second, to actively estimate this unmatched disturbance, a generalized proportional integral observer(GPIO) technique is utilized based on only one measured state. Third, by constructing a new sliding manifold with the aid of the estimated unmatched disturbance and states, a GPIO-based CSMC is synthesized, which is employed to cope with not only matched and unmatched disturbances, but also networkinduced delays. The stability of the entire closed-loop system under the proposed GPIO-based CSMC is detailedly analyzed.The promising tracking efficiency and feasibility of the proposed control methodology are verified through simulations and experiments on Quanser's servo module for motion control under various test conditions.
基金supported by the National Science Foundation of China(62263020)the Key Project of Natural Science Foundation of Gansu Province(25JRRA061)+1 种基金the Key R&D Program of Gansu Province(23YFGA0061)the Scientific Research Initiation Fund of Lanzhou University of Technology(061602).
文摘In the production processes of modern industry,accurate assessment of the system’s health state and traceability non-optimal factors are key to ensuring“safe,stable,long-term,full load and optimal”operation of the production process.The benzene-to-ethylene ratio control system is a complex system based on anMPC-PID doublelayer architecture.Taking into consideration the interaction between levels,coupling between loops and conditions of incomplete operation data,this paper proposes a health assessment method for the dual-layer control system by comprehensively utilizing deep learning technology.Firstly,according to the results of the pre-assessment of the system layers and loops bymultivariate statisticalmethods,seven characteristic parameters that have a significant impact on the health state of the system are identified.Next,aiming at the problem of incomplete assessment data set due to the uneven distribution of actual system operating health state,the original unbalanced dataset is augmented using aWasserstein generative adversarial network with gradient penalty term,and a complete dataset is obtained to characterise all the health states of the system.On this basis,a new deep learning-based health assessment framework for the benzeneto-ethylene ratio control system is constructed based on traditionalmultivariate statistical assessment.This framework can overcome the shortcomings of the linear weighted fusion related to the coupling and nonlinearity of the subsystem health state at different layers,and reduce the dependence of the prior knowledge.Furthermore,by introducing a dynamic attention mechanism(AM)into the convolutional neural network(CNN),the assessment model integrating both assessment and traceability is constructed,which can achieve the health assessment and trace the non-optimal factors of the complex control systems with the double-layer architecture.Finally,the effectiveness and superiority of the proposed method have been verified by the benzene-ethylene ratio control system of the alkylation process unit in a styrene plant.
基金supported by the Liaoning Revitalization Talents Program(XLYC2203148)
文摘Dear Editor,This letter presents a joint probabilistic scheduling and resource allocation method(PSRA) for 5G-based wireless networked control systems(WNCSs). As a control-aware optimization method, PSRA minimizes the linear quadratic Gaussian(LQG) control cost of WNCSs by optimizing the activation probability of subsystems, the number of uplink repetitions, and the durations of uplink and downlink phases. Simulation results show that PSRA achieves smaller LQG control costs than existing works.
基金supported in part by Shanghai Rising-Star Program,China under grant 22QA1409400in part by National Natural Science Foundation of China under grant 62473287 and 62088101in part by Shanghai Municipal Science and Technology Major Project under grant 2021SHZDZX0100.
文摘This paper investigates the problem of optimal secure control for networked control systems under hybrid attacks.A control strategy based on the Stackelberg game framework is proposed,which differs from conventional methods by considering both denial-of-service(DoS)and false data injection(FDI)attacks simultaneously.Additionally,the stability conditions for the system under these hybrid attacks are established.It is technically challenging to design the control strategy by predicting attacker actions based on Stcakelberg game to ensure the system stability under hybrid attacks.Another technical difficulty lies in establishing the conditions for mean-square asymptotic stability due to the complexity of the attack scenarios Finally,simulations on an unstable batch reactor system under hybrid attacks demonstrate the effectiveness of the proposed strategy.
文摘This article studies the existence and uniqueness of the mild solution of a family of control systems with a delay that are governed by the nonlinear fractional evolution differential equations in Banach spaces.Moreover,we establish the controllability of the considered system.To do so,first,we investigate the approximate controllability of the corresponding linear system.Subsequently,we prove the nonlinear system is approximately controllable if the corresponding linear system is approximately controllable.To reach the conclusions,the theory of resolvent operators,the Banach contraction mapping principle,and fixed point theorems are used.While concluding,some examples are given to demonstrate the efficacy of the proposed results.
基金funded by the Office of Gas and Electricity Markets(Ofgem)and supported by De Montfort University(DMU)and Nottingham Trent University(NTU),UK.
文摘This paper introduces the Integrated Security Embedded Resilience Architecture (ISERA) as an advanced resilience mechanism for Industrial Control Systems (ICS) and Operational Technology (OT) environments. The ISERA framework integrates security by design principles, micro-segmentation, and Island Mode Operation (IMO) to enhance cyber resilience and ensure continuous, secure operations. The methodology deploys a Forward-Thinking Architecture Strategy (FTAS) algorithm, which utilises an industrial Intrusion Detection System (IDS) implemented with Python’s Network Intrusion Detection System (NIDS) library. The FTAS algorithm successfully identified and responded to cyber-attacks, ensuring minimal system disruption. ISERA has been validated through comprehensive testing scenarios simulating Denial of Service (DoS) attacks and malware intrusions, at both the IT and OT layers where it successfully mitigates the impact of malicious activity. Results demonstrate ISERA’s efficacy in real-time threat detection, containment, and incident response, thus ensuring the integrity and reliability of critical infrastructure systems. ISERA’s decentralised approach contributes to global net zero goals by optimising resource use and minimising environmental impact. By adopting a decentralised control architecture and leveraging virtualisation, ISERA significantly enhances the cyber resilience and sustainability of critical infrastructure systems. This approach not only strengthens defences against evolving cyber threats but also optimises resource allocation, reducing the system’s carbon footprint. As a result, ISERA ensures the uninterrupted operation of essential services while contributing to broader net zero goals.
文摘In this paper,a new study concerning the usage of artificial neural networks in the control application is given.It is shown,that the data gathered during proper operation of a given control plant can be used in the learning process to fully embrace the control pattern.Interestingly,the instances driven by neural networks have the ability to outperform the original analytically driven scenarios.Three different control schemes,namely perfect,linear-quadratic,and generalized predictive controllers were used in the theoretical study.In addition,the nonlinear recurrent neural network-based generalized predictive controller with the radial basis function-originated predictor was obtained to exemplify the main results of the paper regarding the real-world application.
基金supported in part by the Thai Nguyen University of Technology,Vietnam.
文摘This article presents an adaptive optimal control method for a semi-active suspension system.The model of the suspension system is built,in which the components of uncertain parameters and exogenous disturbance are described.The adaptive optimal control law consists of the sum of the optimal control component and the adaptive control component.First,the optimal control law is designed for the model of the suspension system after ignoring the components of uncertain parameters and exogenous disturbance caused by the road surface.The optimal control law expresses the desired dynamic characteristics of the suspension system.Next,the adaptive component is designed with the purpose of compensating for the effects caused by uncertain parameters and exogenous disturbance caused by the road surface;the adaptive component has adaptive parameter rules to estimate uncertain parameters and exogenous disturbance.When exogenous disturbances are eliminated,the system responds with an optimal controller designed.By separating theoretically the dynamic of a semi-active suspension system,this solution allows the design of two separate controllers easily and has reduced the computational burden and the use of too many tools,thus allowing for more convenient hardware implementation.The simulation results also show the effectiveness of damping oscillations of the proposed solution in this article.
基金Supported by the Russian Science Foundation(Agreement 23-41-10001,https://rscf.ru/project/23-41-10001/).
文摘Background Interconnection of different power systems has a major effect on system stability.This study aims to design an optimal load frequency control(LFC)system based on a proportional-integral(PI)controller for a two-area power system.Methods Two areas were connected through an AC tie line in parallel with a DC link to stabilize the frequency of oscillations in both areas.The PI parameters were tuned using the cuckoo search algorithm(CSA)to minimize the integral absolute error(IAE).A state matrix was provided,and the stability of the system was verified by calculating the eigenvalues.The frequency response was investigated for load variation,changes in the generator rate constraint,the turbine time constant,and the governor time constant.Results The CSA was compared with particle swarm optimization algorithm(PSO)under identical conditions.The system was modeled based on a state-space mathematical representation and simulated using MATLAB.The results demonstrated the effectiveness of the proposed controller based on both algorithms and,it is clear that CSA is superior to PSO.Conclusion The CSA algorithm smoothens the system response,reduces ripples,decreases overshooting and settling time,and improves the overall system performance under different disturbances.
文摘Fiber quality measurement in spinning preparation is crucial for optimizing waste and meeting yarn quality specifications.The brand-new Uster AFIS 6–the next-generation laboratory instrument from Uster Technologies–uniquely tests man-made fiber properties in addition to cotton.It provides critical data to optimize fiber process control for cotton,man-made fibers,and blended yarns.
文摘Permanent magnet synchronous motor(PMSM)is widely used in alternating current servo systems as it provides high eficiency,high power density,and a wide speed regulation range.The servo system is placing higher demands on its control performance.The model predictive control(MPC)algorithm is emerging as a potential high-performance motor control algorithm due to its capability of handling multiple-input and multipleoutput variables and imposed constraints.For the MPC used in the PMSM control process,there is a nonlinear disturbance caused by the change of electromagnetic parameters or load disturbance that may lead to a mismatch between the nominal model and the controlled object,which causes the prediction error and thus affects the dynamic stability of the control system.This paper proposes a data-driven MPC strategy in which the historical data in an appropriate range are utilized to eliminate the impact of parameter mismatch and further improve the control performance.The stability of the proposed algorithm is proved as the simulation demonstrates the feasibility.Compared with the classical MPC strategy,the superiority of the algorithm has also been verified.
