Realizing optimal control performance for continuum robots(CRs) poses huge challenges on traditional modelbased optimal control approaches due to their high degrees of freedom,complex nonlinear dynamics and soft conti...Realizing optimal control performance for continuum robots(CRs) poses huge challenges on traditional modelbased optimal control approaches due to their high degrees of freedom,complex nonlinear dynamics and soft continuum morphologies which are difficult to explicitly model.This paper proposes a model-free adaptive optimal control algorithm(ADAPT)for CRs.In our strategy,we consider CRs as a class of nonlinear continuous-time dynamical systems in the state space,wherein the position of the end-effector is considered as the state and the input torque is mapped as the control input.Then,the optimized Hamilton-Jacobi-Bellman(HJB) equation is derived by optimal control principles,and subsequently solved by the proposed ADAPT algorithm without requiring knowledge of the original system dynamics.Under some mild assumptions,the global stability and convergence of the closed-loop control approach are guaranteed.Several simulation experiments are conducted on a magnetic CR(MCR) to demonstrate the practicality and effectiveness of the ADAPT algorithm.展开更多
In this paper, the containment control problem in nonlinear multi-agent systems(NMASs) under denial-of-service(DoS) attacks is addressed. Firstly, a prediction model is obtained using the broad learning technique to t...In this paper, the containment control problem in nonlinear multi-agent systems(NMASs) under denial-of-service(DoS) attacks is addressed. Firstly, a prediction model is obtained using the broad learning technique to train historical data generated by the system offline without DoS attacks. Secondly, the dynamic linearization method is used to obtain the equivalent linearization model of NMASs. Then, a novel model-free adaptive predictive control(MFAPC) framework based on historical and online data generated by the system is proposed, which combines the trained prediction model with the model-free adaptive control method. The development of the MFAPC method motivates a much simpler robust predictive control solution that is convenient to use in the case of DoS attacks. Meanwhile, the MFAPC algorithm provides a unified predictive framework for solving consensus tracking and containment control problems. The boundedness of the containment error can be proven by using the contraction mapping principle and the mathematical induction method. Finally, the proposed MFAPC is assessed through comparative experiments.展开更多
The increasing penetration of second-life battery energy storage systems(SLBESS)in power grids presents substantial challenges to system operation and control due to the heterogeneous characteristics and uncertain deg...The increasing penetration of second-life battery energy storage systems(SLBESS)in power grids presents substantial challenges to system operation and control due to the heterogeneous characteristics and uncertain degradation patterns of repurposed batteries.This paper presents a novel model-free adaptive voltage controlembedded dung beetle-inspired heuristic optimization algorithmfor optimal SLBESS capacity configuration and power dispatch.To simultaneously address the computational complexity and ensure system stability,this paper develops a comprehensive bilevel optimization framework.At the upper level,a dung beetle optimization algorithmdetermines the optimal SLBESS capacity configuration byminimizing total lifecycle costswhile incorporating the charging/discharging power trajectories derived from the model-free adaptive voltage control strategy.At the lower level,a health-priority power dispatch optimization model intelligently allocates power demands among heterogeneous battery groups based on their real-time operational states,state-of-health variations,and degradation constraints.The proposed model-free approach circumvents the need for complex battery charging/discharging power controlmodels and extensive historical data requirements whilemaintaining system stability through adaptive controlmechanisms.A novel cycle life degradation model is developed to quantify the relationship between remaining useful life,depth of discharge,and operational patterns.The integrated framework enables simultaneous strategic planning and operational control,ensuring both economic efficiency and extended battery lifespan.The effectiveness of the proposed method is validated through comprehensive case studies on hybrid energy storage systems,demonstrating superior computational efficiency,robust performance across different network configurations,and significant improvements in battery utilization compared to conventional approaches.展开更多
A kind of adaptive sliding model control algorithm is developed to solve and improve the mathematical model dependency and un-modeled dynamics of a controlled system. The control strategy derived from a kind of data-d...A kind of adaptive sliding model control algorithm is developed to solve and improve the mathematical model dependency and un-modeled dynamics of a controlled system. The control strategy derived from a kind of data-driven control method in essence, thereby the input and output data are utilized by the controller with no information about the control system model. Theoretical analysis proves that this proposed control algorithm can improve the utilization of the estimated pseudo partial derivative information and accelerate the velocity of the convergence. The stability of the control system is further verified by rigorous mathematical analysis. This new discrete-time nonlinear systems model-free control algorithm obtained better control performance through the simulations for the linear motor position and the information tracking speed, which also achieved robust and accurate traceability.展开更多
In order to solve the problems of dynamic modeling and complicated parameters identification of trajectory tracking control of the quadrotor,a data driven model-free adaptive control method based on the improved slidi...In order to solve the problems of dynamic modeling and complicated parameters identification of trajectory tracking control of the quadrotor,a data driven model-free adaptive control method based on the improved sliding mode control(ISMC)algorithm is designed,which does not depend on the precise dynamic model of the quadrotor.The design of the general sliding mode control(SMC)algorithm depends on the mathematical model of the quadrotor and has chattering problems.In this paper,according to the dynamic characteristics of the quadrotor,an adaptive update law is introduced and a saturation function is used to improve the SMC.The proposed control strategy has an inner and an outer loop control structures.The outer loop position control provides the required reference attitude angle for the inner loop.The inner loop attitude control ensures rapid convergence of the attitude angle.The effectiveness and feasibility of the algorithm are verified by mathematical simulation.The mathematical simulation results show that the designed model-free adaptive control method of the quadrotor is effective,and it can effectively realize the trajectory tracking control of the quadrotor.The design of the controller does not depend on the kinematic and dynamic models of the unmanned aerial vehicle(UAV),and has high control accuracy,stability,and robustness.展开更多
Designing appropriate loss functions is critical to the success of supervised learning models.However,most conventional losses are fixed and manually designed,making them suboptimal for diverse and dynamic learning sc...Designing appropriate loss functions is critical to the success of supervised learning models.However,most conventional losses are fixed and manually designed,making them suboptimal for diverse and dynamic learning scenarios.In this work,we propose an Adaptive Meta-Loss Network(Adaptive-MLN)that learns to generate taskagnostic loss functions tailored to evolving classification problems.Unlike traditional methods that rely on static objectives,Adaptive-MLN treats the loss function itself as a trainable component,parameterized by a shallow neural network.To enable flexible,gradient-free optimization,we introduce a hybrid evolutionary approach that combines GeneticAlgorithms(GA)for global exploration and Evolution Strategies(ES)for local refinement.This co-evolutionary process dynamically adjusts the loss landscape,improvingmodel generalization without relying on analytic gradients or handcrafted heuristics.Experimental evaluations on synthetic tasks and the CIFAR-10 andMNIST datasets demonstrate that our approach consistently outperforms standard losses such as Cross-Entropy and Mean Squared Error in terms of accuracy,convergence,and adaptability.展开更多
The development of the adaptive cycle engine is a crucial direction of advanced fighter power sources in the near future.However,this new technology brings more uncertainty to the design of the control system.To addre...The development of the adaptive cycle engine is a crucial direction of advanced fighter power sources in the near future.However,this new technology brings more uncertainty to the design of the control system.To address the versatile thrust demand under complex dynamic characteristics of the adaptive cycle engine,this paper proposes a direct thrust estimation and control method based on the Model-Free Adaptive Control(MFAC)algorithm.First,an improved Sliding Mode Control-MFAC(SMC-MFAC)algorithm has been developed by introducing a sliding mode variable structure into the standard Full Format Dynamic Linearization-MFAC(FFDL-MFAC)and designing self-adaptive weight coefficients.Then a trivariate double-loop direct thrust control structure with a controller-based thrust estimator and an outer command compensation loop has been established.Through thrust feedback and command correction,accurate control under multi-mode and operation conditions is achieved.The main contribution of this paper is the improved algorithm that combines the tracking capability of the MFAC and the robustness of the SMC,thus enhancing the dynamic performance.Considering the requirements of the online thrust feedback,the designed MFAC-based thrust estimator significantly speeds up the calculation.Additionally,the proposed command correction module can achieve the adaptive thrust control without affecting the operation of the inner loop.Simulations and Hardware-in-Loop(HIL)experiments have been performed on an adaptive cycle engine component-level model to investigate the estimation and control effect under different modes and health conditions.The results demonstrate that both the thrust estimation precision and operation speed are significantly improved compared with Extended Kalman Filter(EKF).Furthermore,the system can accelerate the response of the controlled plant,reduce the overshoot,and realize the thrust recovery within the safety range when the engine encounters the degradation.展开更多
Cloud computing has become an essential technology for the management and processing of large datasets,offering scalability,high availability,and fault tolerance.However,optimizing data replication across multiple dat...Cloud computing has become an essential technology for the management and processing of large datasets,offering scalability,high availability,and fault tolerance.However,optimizing data replication across multiple data centers poses a significant challenge,especially when balancing opposing goals such as latency,storage costs,energy consumption,and network efficiency.This study introduces a novel Dynamic Optimization Algorithm called Dynamic Multi-Objective Gannet Optimization(DMGO),designed to enhance data replication efficiency in cloud environments.Unlike traditional static replication systems,DMGO adapts dynamically to variations in network conditions,system demand,and resource availability.The approach utilizes multi-objective optimization approaches to efficiently balance data access latency,storage efficiency,and operational costs.DMGO consistently evaluates data center performance and adjusts replication algorithms in real time to guarantee optimal system efficiency.Experimental evaluations conducted in a simulated cloud environment demonstrate that DMGO significantly outperforms conventional static algorithms,achieving faster data access,lower storage overhead,reduced energy consumption,and improved scalability.The proposed methodology offers a robust and adaptable solution for modern cloud systems,ensuring efficient resource consumption while maintaining high performance.展开更多
The adaptive filtering algorithm with a fixed projection order is unable to adjust its performance in response to changes in the external environment of airborne radars.To overcome this limitation,a new approach is in...The adaptive filtering algorithm with a fixed projection order is unable to adjust its performance in response to changes in the external environment of airborne radars.To overcome this limitation,a new approach is introduced,which is the variable projection order Ekblom norm-promoted adaptive algorithm(VPO-EPAA).The method begins by examining the mean squared deviation(MSD)of the EPAA,deriving a formula for its MSD.Next,it compares the MSD of EPAA at two different projection orders and selects the one that minimizes the MSD as the parameter for the current iteration.Furthermore,the algorithm’s computational complexity is analyzed theoretically.Simulation results from system identification and self-interference cancellation show that the proposed algorithm performs exceptionally well in airborne radar signal self-interference cancellation,even under various noise intensities and types of interference.展开更多
Accurate and reliable photovoltaic(PV)modeling is crucial for the performance evaluation,control,and optimization of PV systems.However,existing methods for PV parameter identification often suffer from limitations in...Accurate and reliable photovoltaic(PV)modeling is crucial for the performance evaluation,control,and optimization of PV systems.However,existing methods for PV parameter identification often suffer from limitations in accuracy and efficiency.To address these challenges,we propose an adaptive multi-learning cooperation search algorithm(AMLCSA)for efficient identification of unknown parameters in PV models.AMLCSA is a novel algorithm inspired by teamwork behaviors in modern enterprises.It enhances the original cooperation search algorithm in two key aspects:(i)an adaptive multi-learning strategy that dynamically adjusts search ranges using adaptive weights,allowing better individuals to focus on local exploitation while guiding poorer individuals toward global exploration;and(ii)a chaotic grouping reflection strategy that introduces chaotic sequences to enhance population diversity and improve search performance.The effectiveness of AMLCSA is demonstrated on single-diode,double-diode,and three PV-module models.Simulation results show that AMLCSA offers significant advantages in convergence,accuracy,and stability compared to existing state-of-the-art algorithms.展开更多
For autonomous Unmanned Aerial Vehicles(UAVs)flying in real-world scenarios,time for path planning is always limited,which is a challenge known as the anytime problem.Anytime planners address this by finding a collisi...For autonomous Unmanned Aerial Vehicles(UAVs)flying in real-world scenarios,time for path planning is always limited,which is a challenge known as the anytime problem.Anytime planners address this by finding a collision-free path quickly and then improving it until time runs out,making UAVs more adaptable to different mission scenarios.However,current anytime algorithms based on A^(*)have insufficient control over the suboptimality bounds of paths and tend to lose their anytime properties in environments with large concave obstacles.This paper proposes a novel anytime path planning algorithm,Anytime Radiation A^(*)(ARa A^(*)),which can generate a series of suboptimal paths with improved bounds through decreasing search step sizes and can generate the optimal path when time is sufficient.The ARa A^(*)features two main innovations:an adaptive variable-step-size mechanism and elliptic constraints based on waypoints.The former helps achieve fast path searching in various environments.The latter allows ARa A^(*)to control the suboptimality bounds of paths and further enhance search efficiency.Simulation experiments show that the ARa A^(*)outperforms Anytime Repairing A^(*)(ARA^(*))and Anytime D^(*)(AD^(*))in controlling suboptimality bounds and planning time,especially in environments with large concave obstacles.Final flight experiments demonstrate that the paths planned by ARa A^(*)can ensure the safe flight of quadrotors.展开更多
Meshing temperature analyses of polymer gears reported in the literature mainly concern the effects of various material combinations and loading conditions,as their impacts could be seen in the first few meshing cycle...Meshing temperature analyses of polymer gears reported in the literature mainly concern the effects of various material combinations and loading conditions,as their impacts could be seen in the first few meshing cycles.However,the effects of tooth geometry parameters could manifest as the meshing cycles increase.This study investigated the effects of tooth geometry parameters on the multi-cycle meshing temperature of polyoxymethylene(POM)worm gears,aiming to control the meshing temperature elevation by tuning the tooth geometry.Firstly,a finite element(FE)model capable of separately calculating the heat generation and simulating the heat propagation was established.Moreover,an adaptive iteration algorithm was proposed within the FE framework to capture the influence of the heat generation variation from cycle to cycle.This algorithm proved to be feasible and highly efficient compared with experimental results from the literature and simulated results via the full-iteration algorithm.Multi-cycle meshing temperature analyses were conducted on a series of POM worm gears with different tooth geometry parameters.The results reveal that,within the range of 14.5°to 25°,a pressure angle of 25°is favorable for reducing the peak surface temperature and overall body temperature of POM worm gears,which influence flank wear and load-carrying capability,respectively.However,addendum modification should be weighed because it helps with load bearing but increases the risk of severe flank wear.This paper proposes an efficient iteration algorithm for multi-cycle meshing temperature analysis of polymer gears and proves the feasibility of controlling the meshing temperature elevation during multiple cycles by tuning tooth geometry.展开更多
Solar radio burst(SRB)is one of the main natural interference sources of Global Positioning System(GPS)signals and can reduce the signal-to-noise ratio(SNR),directly affecting the tracking performance of GPS receivers...Solar radio burst(SRB)is one of the main natural interference sources of Global Positioning System(GPS)signals and can reduce the signal-to-noise ratio(SNR),directly affecting the tracking performance of GPS receivers.In this paper,a tracking algorithm based on the adaptive Kalman filter(AKF)with carrier-to-noise ratio estimation is proposed and compared with the conventional second-order phase-locked loop tracking algo-rithms and the improved Sage-Husa adaptive Kalman filter(SHAKF)algorithm.It is discovered that when the SRBs occur,the improved SHAKF and the AKF with carrier-to-noise ratio estimation enable stable tracking to loop signals.The conven-tional second-order phase-locked loop tracking algorithms fail to track the receiver signal.The standard deviation of the carrier phase error of the AKF with carrier-to-noise ratio estimation out-performs 50.51%of the improved SHAKF algorithm,showing less fluctuation and better stability.The proposed algorithm is proven to show more excellent adaptability in the severe envi-ronment caused by the SRB occurrence and has better tracking performance.展开更多
An adaptive optimal trajectory tracking controller is presented for the Solid-RocketPowered Vehicle(SRPV)with uncertain nonlinear non-affine dynamics in the framework of adaptive dynamic programming.First,considering ...An adaptive optimal trajectory tracking controller is presented for the Solid-RocketPowered Vehicle(SRPV)with uncertain nonlinear non-affine dynamics in the framework of adaptive dynamic programming.First,considering that the ascent model of the SRPV is non-affine,a model-free Single Network Adaptive Critic(SNAC)method is developed based on the dynamic neural network and the traditional SNAC method.This developed model-free SNAC method overcomes the limitation of the traditional SNAC method that can only be applied to affine systems.Then,a closed-form adaptive optimal controller is designed for the non-affine dynamics of SRPVs.This controller can adjust its parameters under different flight conditions and converge to the approximate optimal controller through online self-learning.Finally,the convergence to the approximate optimal controller is proved.The theoretical analysis of the uniformly ultimate boundedness of the tracking error is also presented.Simulation results demonstrate the effectiveness of the proposed controller.展开更多
To accomplish the reliability analyses of the correlation of multi-analytical objectives,an innovative framework of Dimensional Synchronous Modeling(DSM)and correlation analysis is developed based on the stepwise mode...To accomplish the reliability analyses of the correlation of multi-analytical objectives,an innovative framework of Dimensional Synchronous Modeling(DSM)and correlation analysis is developed based on the stepwise modeling strategy,cell array operation principle,and Copula theory.Under this framework,we propose a DSM-based Enhanced Kriging(DSMEK)algorithm to synchronously derive the modeling of multi-objective,and explore an adaptive Copula function approach to analyze the correlation among multiple objectives and to assess the synthetical reliability level.In the proposed DSMEK and adaptive Copula methods,the Kriging model is treated as the basis function of DSMEK model,the Multi-Objective Snake Optimizer(MOSO)algorithm is used to search the optimal values of hyperparameters of basis functions,the cell array operation principle is adopted to establish a whole model of multiple objectives,the goodness of fit is utilized to determine the forms of Copula functions,and the determined Copula functions are employed to perform the reliability analyses of the correlation of multi-analytical objectives.Furthermore,three examples,including multi-objective complex function approximation,aeroengine turbine bladeddisc multi-failure mode reliability analyses and aircraft landing gear system brake temperature reliability analyses,are performed to verify the effectiveness of the proposed methods,from the viewpoints of mathematics and engineering.The results show that the DSMEK and adaptive Copula approaches hold obvious advantages in terms of modeling features and simulation performance.The efforts of this work provide a useful way for the modeling of multi-analytical objectives and synthetical reliability analyses of complex structure/system with multi-output responses.展开更多
In this paper,an asymmetric bipartite consensus problem for the nonlinear multi-agent systems with cooperative and antagonistic interactions is studied under the event-triggered mechanism.For the agents described by a...In this paper,an asymmetric bipartite consensus problem for the nonlinear multi-agent systems with cooperative and antagonistic interactions is studied under the event-triggered mechanism.For the agents described by a structurally balanced signed digraph,the asymmetric bipartite consensus objective is firstly defined,assigning the agents'output to different signs and module values.Considering with the completely unknown dynamics of the agents,a novel event-triggered model-free adaptive bipartite control protocol is designed based on the agents'triggered outputs and an equivalent compact form data model.By utilizing the Lyapunov analysis method,the threshold of the triggering condition is obtained.Subsequently,the asymptotic convergence of the tracking error is deduced and a sufficient condition is obtained based on the contraction mapping principle.Finally,the simulation example further demonstrates the effectiveness of the protocol.展开更多
Polyvinyl chloride （PVC） polymerizing process is a typical complicated industrial process with the characteristics of large inertia, big time delay and nonlinearity. Firstly, for the general nonlinear and discrete t...Polyvinyl chloride （PVC） polymerizing process is a typical complicated industrial process with the characteristics of large inertia, big time delay and nonlinearity. Firstly, for the general nonlinear and discrete time system, a design scheme of model-free adaptive （MFA） controller is given. Then, particle swarm optimization （PSO） algorithm is applied to optimizing and setting the key parameters for controller tuning. After that, the MFA controller is used to control the system of polymerizing temperature. Finally, simulation results are given to show that the MAC strategy based on PSO obtains a good controlling performance index.展开更多
The operating frequency accuracy of the local oscillators is critical for the overall system performance in the communication systems.However,the high-precision oscillators could be too expensive for civil application...The operating frequency accuracy of the local oscillators is critical for the overall system performance in the communication systems.However,the high-precision oscillators could be too expensive for civil applications.In this paper,we propose a model-free adaptive frequency calibration framework for a voltage-controlled crystal oscillator(VCO)equipped with a time to digital converter(TDC),which can significantly improve the frequency accuracy of the VCO thus calibrated.The idea is to utilize a high-precision TDC to directly measure the VCO period which is then passed to a model-free method for working frequency calibration.One advantage of this method is that the working frequency calibration employs the system history of input/output(I/O)data,instead of establishing an accurate VCO voltagecontrolled oscillator model.Another advantage is the lightweight calibration method with low complexity such that it can be implemented on an MCU with limited computation capabilities.Experimental results show that the proposed calibration method can improve the frequency accuracy of a VCO from±20 ppm to±10 ppb,which indicates the promise of the modelfree adaptive frequency calibrator for VCOs.展开更多
Due to the release of gravity in the space environment, the dynamic characteristics of the space manipulator have changed compared with that of the ground, which results in the change of its tracking precision. This p...Due to the release of gravity in the space environment, the dynamic characteristics of the space manipulator have changed compared with that of the ground, which results in the change of its tracking precision. This paper presents a model-free adaptive control(MFAC) strategy to track the desired trajectory under different gravity environment. A dynamic transformation method and full form dynamic linearization(FFDL) approach are selected to dynamicly linearize the system, which can better eliminate the complex dynamics that may exist in the original system. The controlled object uses the two degrees of freedom of space manipulator and the controller only depends on the desired angle and torque of each joint of the space manipulator. Moreover, the proof of stability is also provided. Finally, simulation results are presented to demonstrate the effectiveness of the proposed strategy. It is shown that the proposed approach can achieve better trajectory tracking performance under different gravity environment without changing the control parameters, and the tracking precision can be significantly improved as compared with the proportional differential(PD) control results.展开更多
This paper provides an improved model-free adaptive control(IMFAC)strategy for solving the surface vessel trajectory tracking issue with time delay and restricted disturbance.Firstly,the original nonlinear time-delay ...This paper provides an improved model-free adaptive control(IMFAC)strategy for solving the surface vessel trajectory tracking issue with time delay and restricted disturbance.Firstly,the original nonlinear time-delay system is transformed into a structure consisting of an unknown residual term and a parameter term with control inputs using a local compact form dynamic linearization(local-CFDL).To take advantage of the resulting structure,use a discrete-time extended state observer(DESO)to estimate the unknown residual factor.Then,according to the study,the inclusion of a time delay has no effect on the linearization structure,and an improved control approach is provided,in which DESO is used to adjust for uncertainties.Furthermore,a DESO-based event-triggered model-free adaptive control(ET-DESO-MFAC)is established by designing event-triggered conditions to assure Lyapunov stability.Only when the system’s indicator fulfills the provided event-triggered condition will the control input signal be updated;otherwise,the control input will stay the same as it is at the last trigger moment.A coordinate compensation approach is developed to reduce the steady-state inaccuracy of trajectory tracking.Finally,simulation experiments are used to assess the effectiveness of the proposed technique for trajectory tracking.展开更多
基金supported in part by the Innovation and Technology Commission of Hong Kong,China(ITS/136/20,ITS/234/21,MHP/096/22,ITS/235/22)Multi-Scale Medical Robotics Center,InnoHK,China(8312051)+1 种基金Research Grants Council(RGC) of Hong Kong,China(CUHK 14217822,CUHK14207823,AoE/E-407/24-N)The Chinese University of Hong Kong(CUHK) Direct Grant。
文摘Realizing optimal control performance for continuum robots(CRs) poses huge challenges on traditional modelbased optimal control approaches due to their high degrees of freedom,complex nonlinear dynamics and soft continuum morphologies which are difficult to explicitly model.This paper proposes a model-free adaptive optimal control algorithm(ADAPT)for CRs.In our strategy,we consider CRs as a class of nonlinear continuous-time dynamical systems in the state space,wherein the position of the end-effector is considered as the state and the input torque is mapped as the control input.Then,the optimized Hamilton-Jacobi-Bellman(HJB) equation is derived by optimal control principles,and subsequently solved by the proposed ADAPT algorithm without requiring knowledge of the original system dynamics.Under some mild assumptions,the global stability and convergence of the closed-loop control approach are guaranteed.Several simulation experiments are conducted on a magnetic CR(MCR) to demonstrate the practicality and effectiveness of the ADAPT algorithm.
基金supported in part by the National Natural Science Foundation of China(62403396,62433018,62373113)the Guangdong Basic and Applied Basic Research Foundation(2023A1515011527,2023B1515120010)the Postdoctoral Fellowship Program of CPSF(GZB20240621)
文摘In this paper, the containment control problem in nonlinear multi-agent systems(NMASs) under denial-of-service(DoS) attacks is addressed. Firstly, a prediction model is obtained using the broad learning technique to train historical data generated by the system offline without DoS attacks. Secondly, the dynamic linearization method is used to obtain the equivalent linearization model of NMASs. Then, a novel model-free adaptive predictive control(MFAPC) framework based on historical and online data generated by the system is proposed, which combines the trained prediction model with the model-free adaptive control method. The development of the MFAPC method motivates a much simpler robust predictive control solution that is convenient to use in the case of DoS attacks. Meanwhile, the MFAPC algorithm provides a unified predictive framework for solving consensus tracking and containment control problems. The boundedness of the containment error can be proven by using the contraction mapping principle and the mathematical induction method. Finally, the proposed MFAPC is assessed through comparative experiments.
基金Financial support was provided by the State Grid Sichuan Electric Power Company Science and Technology Project“Key Research on Development Path Planning and Key Operation Technologies of New Rural Electrification Construction”under Grant No.52199623000G.
文摘The increasing penetration of second-life battery energy storage systems(SLBESS)in power grids presents substantial challenges to system operation and control due to the heterogeneous characteristics and uncertain degradation patterns of repurposed batteries.This paper presents a novel model-free adaptive voltage controlembedded dung beetle-inspired heuristic optimization algorithmfor optimal SLBESS capacity configuration and power dispatch.To simultaneously address the computational complexity and ensure system stability,this paper develops a comprehensive bilevel optimization framework.At the upper level,a dung beetle optimization algorithmdetermines the optimal SLBESS capacity configuration byminimizing total lifecycle costswhile incorporating the charging/discharging power trajectories derived from the model-free adaptive voltage control strategy.At the lower level,a health-priority power dispatch optimization model intelligently allocates power demands among heterogeneous battery groups based on their real-time operational states,state-of-health variations,and degradation constraints.The proposed model-free approach circumvents the need for complex battery charging/discharging power controlmodels and extensive historical data requirements whilemaintaining system stability through adaptive controlmechanisms.A novel cycle life degradation model is developed to quantify the relationship between remaining useful life,depth of discharge,and operational patterns.The integrated framework enables simultaneous strategic planning and operational control,ensuring both economic efficiency and extended battery lifespan.The effectiveness of the proposed method is validated through comprehensive case studies on hybrid energy storage systems,demonstrating superior computational efficiency,robust performance across different network configurations,and significant improvements in battery utilization compared to conventional approaches.
基金supported by Key Programs for Science and Technology Development of Henan Province(No.102102210197)the Opening Project of Key Laboratory of Mine Informatization,Henan Polytechnic University and the Doctoral Foundation of Henan Polytechnic University(No.B2010-23)
文摘A kind of adaptive sliding model control algorithm is developed to solve and improve the mathematical model dependency and un-modeled dynamics of a controlled system. The control strategy derived from a kind of data-driven control method in essence, thereby the input and output data are utilized by the controller with no information about the control system model. Theoretical analysis proves that this proposed control algorithm can improve the utilization of the estimated pseudo partial derivative information and accelerate the velocity of the convergence. The stability of the control system is further verified by rigorous mathematical analysis. This new discrete-time nonlinear systems model-free control algorithm obtained better control performance through the simulations for the linear motor position and the information tracking speed, which also achieved robust and accurate traceability.
文摘In order to solve the problems of dynamic modeling and complicated parameters identification of trajectory tracking control of the quadrotor,a data driven model-free adaptive control method based on the improved sliding mode control(ISMC)algorithm is designed,which does not depend on the precise dynamic model of the quadrotor.The design of the general sliding mode control(SMC)algorithm depends on the mathematical model of the quadrotor and has chattering problems.In this paper,according to the dynamic characteristics of the quadrotor,an adaptive update law is introduced and a saturation function is used to improve the SMC.The proposed control strategy has an inner and an outer loop control structures.The outer loop position control provides the required reference attitude angle for the inner loop.The inner loop attitude control ensures rapid convergence of the attitude angle.The effectiveness and feasibility of the algorithm are verified by mathematical simulation.The mathematical simulation results show that the designed model-free adaptive control method of the quadrotor is effective,and it can effectively realize the trajectory tracking control of the quadrotor.The design of the controller does not depend on the kinematic and dynamic models of the unmanned aerial vehicle(UAV),and has high control accuracy,stability,and robustness.
基金supported by the National Natural Science Foundation of China(NSFC)under Grant number:82171965.
文摘Designing appropriate loss functions is critical to the success of supervised learning models.However,most conventional losses are fixed and manually designed,making them suboptimal for diverse and dynamic learning scenarios.In this work,we propose an Adaptive Meta-Loss Network(Adaptive-MLN)that learns to generate taskagnostic loss functions tailored to evolving classification problems.Unlike traditional methods that rely on static objectives,Adaptive-MLN treats the loss function itself as a trainable component,parameterized by a shallow neural network.To enable flexible,gradient-free optimization,we introduce a hybrid evolutionary approach that combines GeneticAlgorithms(GA)for global exploration and Evolution Strategies(ES)for local refinement.This co-evolutionary process dynamically adjusts the loss landscape,improvingmodel generalization without relying on analytic gradients or handcrafted heuristics.Experimental evaluations on synthetic tasks and the CIFAR-10 andMNIST datasets demonstrate that our approach consistently outperforms standard losses such as Cross-Entropy and Mean Squared Error in terms of accuracy,convergence,and adaptability.
基金supported by National Natural Science Foundation of China(No.52302472)。
文摘The development of the adaptive cycle engine is a crucial direction of advanced fighter power sources in the near future.However,this new technology brings more uncertainty to the design of the control system.To address the versatile thrust demand under complex dynamic characteristics of the adaptive cycle engine,this paper proposes a direct thrust estimation and control method based on the Model-Free Adaptive Control(MFAC)algorithm.First,an improved Sliding Mode Control-MFAC(SMC-MFAC)algorithm has been developed by introducing a sliding mode variable structure into the standard Full Format Dynamic Linearization-MFAC(FFDL-MFAC)and designing self-adaptive weight coefficients.Then a trivariate double-loop direct thrust control structure with a controller-based thrust estimator and an outer command compensation loop has been established.Through thrust feedback and command correction,accurate control under multi-mode and operation conditions is achieved.The main contribution of this paper is the improved algorithm that combines the tracking capability of the MFAC and the robustness of the SMC,thus enhancing the dynamic performance.Considering the requirements of the online thrust feedback,the designed MFAC-based thrust estimator significantly speeds up the calculation.Additionally,the proposed command correction module can achieve the adaptive thrust control without affecting the operation of the inner loop.Simulations and Hardware-in-Loop(HIL)experiments have been performed on an adaptive cycle engine component-level model to investigate the estimation and control effect under different modes and health conditions.The results demonstrate that both the thrust estimation precision and operation speed are significantly improved compared with Extended Kalman Filter(EKF).Furthermore,the system can accelerate the response of the controlled plant,reduce the overshoot,and realize the thrust recovery within the safety range when the engine encounters the degradation.
文摘Cloud computing has become an essential technology for the management and processing of large datasets,offering scalability,high availability,and fault tolerance.However,optimizing data replication across multiple data centers poses a significant challenge,especially when balancing opposing goals such as latency,storage costs,energy consumption,and network efficiency.This study introduces a novel Dynamic Optimization Algorithm called Dynamic Multi-Objective Gannet Optimization(DMGO),designed to enhance data replication efficiency in cloud environments.Unlike traditional static replication systems,DMGO adapts dynamically to variations in network conditions,system demand,and resource availability.The approach utilizes multi-objective optimization approaches to efficiently balance data access latency,storage efficiency,and operational costs.DMGO consistently evaluates data center performance and adjusts replication algorithms in real time to guarantee optimal system efficiency.Experimental evaluations conducted in a simulated cloud environment demonstrate that DMGO significantly outperforms conventional static algorithms,achieving faster data access,lower storage overhead,reduced energy consumption,and improved scalability.The proposed methodology offers a robust and adaptable solution for modern cloud systems,ensuring efficient resource consumption while maintaining high performance.
基金supported by the Shan⁃dong Provincial Natural Science Foundation(No.ZR2022MF314).
文摘The adaptive filtering algorithm with a fixed projection order is unable to adjust its performance in response to changes in the external environment of airborne radars.To overcome this limitation,a new approach is introduced,which is the variable projection order Ekblom norm-promoted adaptive algorithm(VPO-EPAA).The method begins by examining the mean squared deviation(MSD)of the EPAA,deriving a formula for its MSD.Next,it compares the MSD of EPAA at two different projection orders and selects the one that minimizes the MSD as the parameter for the current iteration.Furthermore,the algorithm’s computational complexity is analyzed theoretically.Simulation results from system identification and self-interference cancellation show that the proposed algorithm performs exceptionally well in airborne radar signal self-interference cancellation,even under various noise intensities and types of interference.
基金supported by the National Natural Science Foundation of China(Grant Nos.62303197,62273214)the Natural Science Foundation of Shandong Province(ZR2024MFO18).
文摘Accurate and reliable photovoltaic(PV)modeling is crucial for the performance evaluation,control,and optimization of PV systems.However,existing methods for PV parameter identification often suffer from limitations in accuracy and efficiency.To address these challenges,we propose an adaptive multi-learning cooperation search algorithm(AMLCSA)for efficient identification of unknown parameters in PV models.AMLCSA is a novel algorithm inspired by teamwork behaviors in modern enterprises.It enhances the original cooperation search algorithm in two key aspects:(i)an adaptive multi-learning strategy that dynamically adjusts search ranges using adaptive weights,allowing better individuals to focus on local exploitation while guiding poorer individuals toward global exploration;and(ii)a chaotic grouping reflection strategy that introduces chaotic sequences to enhance population diversity and improve search performance.The effectiveness of AMLCSA is demonstrated on single-diode,double-diode,and three PV-module models.Simulation results show that AMLCSA offers significant advantages in convergence,accuracy,and stability compared to existing state-of-the-art algorithms.
基金the support of the National Natural Science Foundation of China(No.52272382)the Aeronautical Science Foundation of China(No.20200017051001)the Fundamental Research Funds for the Central Universities,China。
文摘For autonomous Unmanned Aerial Vehicles(UAVs)flying in real-world scenarios,time for path planning is always limited,which is a challenge known as the anytime problem.Anytime planners address this by finding a collision-free path quickly and then improving it until time runs out,making UAVs more adaptable to different mission scenarios.However,current anytime algorithms based on A^(*)have insufficient control over the suboptimality bounds of paths and tend to lose their anytime properties in environments with large concave obstacles.This paper proposes a novel anytime path planning algorithm,Anytime Radiation A^(*)(ARa A^(*)),which can generate a series of suboptimal paths with improved bounds through decreasing search step sizes and can generate the optimal path when time is sufficient.The ARa A^(*)features two main innovations:an adaptive variable-step-size mechanism and elliptic constraints based on waypoints.The former helps achieve fast path searching in various environments.The latter allows ARa A^(*)to control the suboptimality bounds of paths and further enhance search efficiency.Simulation experiments show that the ARa A^(*)outperforms Anytime Repairing A^(*)(ARA^(*))and Anytime D^(*)(AD^(*))in controlling suboptimality bounds and planning time,especially in environments with large concave obstacles.Final flight experiments demonstrate that the paths planned by ARa A^(*)can ensure the safe flight of quadrotors.
基金Supported by National Key R&D Program of China(Grant No.2019YFE0121300)。
文摘Meshing temperature analyses of polymer gears reported in the literature mainly concern the effects of various material combinations and loading conditions,as their impacts could be seen in the first few meshing cycles.However,the effects of tooth geometry parameters could manifest as the meshing cycles increase.This study investigated the effects of tooth geometry parameters on the multi-cycle meshing temperature of polyoxymethylene(POM)worm gears,aiming to control the meshing temperature elevation by tuning the tooth geometry.Firstly,a finite element(FE)model capable of separately calculating the heat generation and simulating the heat propagation was established.Moreover,an adaptive iteration algorithm was proposed within the FE framework to capture the influence of the heat generation variation from cycle to cycle.This algorithm proved to be feasible and highly efficient compared with experimental results from the literature and simulated results via the full-iteration algorithm.Multi-cycle meshing temperature analyses were conducted on a series of POM worm gears with different tooth geometry parameters.The results reveal that,within the range of 14.5°to 25°,a pressure angle of 25°is favorable for reducing the peak surface temperature and overall body temperature of POM worm gears,which influence flank wear and load-carrying capability,respectively.However,addendum modification should be weighed because it helps with load bearing but increases the risk of severe flank wear.This paper proposes an efficient iteration algorithm for multi-cycle meshing temperature analysis of polymer gears and proves the feasibility of controlling the meshing temperature elevation during multiple cycles by tuning tooth geometry.
基金supported by the Foundation of Key Laboratory of Micro-inertial Instrument and Advanced Navigation Technology,Ministry of Education,Chinathe National Natural Science Foundation of China (61873064)
文摘Solar radio burst(SRB)is one of the main natural interference sources of Global Positioning System(GPS)signals and can reduce the signal-to-noise ratio(SNR),directly affecting the tracking performance of GPS receivers.In this paper,a tracking algorithm based on the adaptive Kalman filter(AKF)with carrier-to-noise ratio estimation is proposed and compared with the conventional second-order phase-locked loop tracking algo-rithms and the improved Sage-Husa adaptive Kalman filter(SHAKF)algorithm.It is discovered that when the SRBs occur,the improved SHAKF and the AKF with carrier-to-noise ratio estimation enable stable tracking to loop signals.The conven-tional second-order phase-locked loop tracking algorithms fail to track the receiver signal.The standard deviation of the carrier phase error of the AKF with carrier-to-noise ratio estimation out-performs 50.51%of the improved SHAKF algorithm,showing less fluctuation and better stability.The proposed algorithm is proven to show more excellent adaptability in the severe envi-ronment caused by the SRB occurrence and has better tracking performance.
基金supported by the National Key R&D Program of China(No.2016YFB1200100)。
文摘An adaptive optimal trajectory tracking controller is presented for the Solid-RocketPowered Vehicle(SRPV)with uncertain nonlinear non-affine dynamics in the framework of adaptive dynamic programming.First,considering that the ascent model of the SRPV is non-affine,a model-free Single Network Adaptive Critic(SNAC)method is developed based on the dynamic neural network and the traditional SNAC method.This developed model-free SNAC method overcomes the limitation of the traditional SNAC method that can only be applied to affine systems.Then,a closed-form adaptive optimal controller is designed for the non-affine dynamics of SRPVs.This controller can adjust its parameters under different flight conditions and converge to the approximate optimal controller through online self-learning.Finally,the convergence to the approximate optimal controller is proved.The theoretical analysis of the uniformly ultimate boundedness of the tracking error is also presented.Simulation results demonstrate the effectiveness of the proposed controller.
基金co-supported by the National Natural Science Foundation of China(Nos.52405293,52375237)China Postdoctoral Science Foundation(No.2024M754219)Shaanxi Province Postdoctoral Research Project Funding,China。
文摘To accomplish the reliability analyses of the correlation of multi-analytical objectives,an innovative framework of Dimensional Synchronous Modeling(DSM)and correlation analysis is developed based on the stepwise modeling strategy,cell array operation principle,and Copula theory.Under this framework,we propose a DSM-based Enhanced Kriging(DSMEK)algorithm to synchronously derive the modeling of multi-objective,and explore an adaptive Copula function approach to analyze the correlation among multiple objectives and to assess the synthetical reliability level.In the proposed DSMEK and adaptive Copula methods,the Kriging model is treated as the basis function of DSMEK model,the Multi-Objective Snake Optimizer(MOSO)algorithm is used to search the optimal values of hyperparameters of basis functions,the cell array operation principle is adopted to establish a whole model of multiple objectives,the goodness of fit is utilized to determine the forms of Copula functions,and the determined Copula functions are employed to perform the reliability analyses of the correlation of multi-analytical objectives.Furthermore,three examples,including multi-objective complex function approximation,aeroengine turbine bladeddisc multi-failure mode reliability analyses and aircraft landing gear system brake temperature reliability analyses,are performed to verify the effectiveness of the proposed methods,from the viewpoints of mathematics and engineering.The results show that the DSMEK and adaptive Copula approaches hold obvious advantages in terms of modeling features and simulation performance.The efforts of this work provide a useful way for the modeling of multi-analytical objectives and synthetical reliability analyses of complex structure/system with multi-output responses.
基金supported in part by the National Natural Science Foundation of China(U1804147,61833001,61873139,61573129)the Innovative Scientists and Technicians Team of Henan Polytechnic University(T2019-2)the Innovative Scientists and Technicians Team of Henan Provincial High Education(20IRTSTHN019)。
文摘In this paper,an asymmetric bipartite consensus problem for the nonlinear multi-agent systems with cooperative and antagonistic interactions is studied under the event-triggered mechanism.For the agents described by a structurally balanced signed digraph,the asymmetric bipartite consensus objective is firstly defined,assigning the agents'output to different signs and module values.Considering with the completely unknown dynamics of the agents,a novel event-triggered model-free adaptive bipartite control protocol is designed based on the agents'triggered outputs and an equivalent compact form data model.By utilizing the Lyapunov analysis method,the threshold of the triggering condition is obtained.Subsequently,the asymptotic convergence of the tracking error is deduced and a sufficient condition is obtained based on the contraction mapping principle.Finally,the simulation example further demonstrates the effectiveness of the protocol.
基金supported by University of Science and Technology Liaoning,National Financial Security and System Equipment Engineering Research Center(No.USTLKFGJ201502)
文摘Polyvinyl chloride （PVC） polymerizing process is a typical complicated industrial process with the characteristics of large inertia, big time delay and nonlinearity. Firstly, for the general nonlinear and discrete time system, a design scheme of model-free adaptive （MFA） controller is given. Then, particle swarm optimization （PSO） algorithm is applied to optimizing and setting the key parameters for controller tuning. After that, the MFA controller is used to control the system of polymerizing temperature. Finally, simulation results are given to show that the MAC strategy based on PSO obtains a good controlling performance index.
文摘The operating frequency accuracy of the local oscillators is critical for the overall system performance in the communication systems.However,the high-precision oscillators could be too expensive for civil applications.In this paper,we propose a model-free adaptive frequency calibration framework for a voltage-controlled crystal oscillator(VCO)equipped with a time to digital converter(TDC),which can significantly improve the frequency accuracy of the VCO thus calibrated.The idea is to utilize a high-precision TDC to directly measure the VCO period which is then passed to a model-free method for working frequency calibration.One advantage of this method is that the working frequency calibration employs the system history of input/output(I/O)data,instead of establishing an accurate VCO voltagecontrolled oscillator model.Another advantage is the lightweight calibration method with low complexity such that it can be implemented on an MCU with limited computation capabilities.Experimental results show that the proposed calibration method can improve the frequency accuracy of a VCO from±20 ppm to±10 ppb,which indicates the promise of the modelfree adaptive frequency calibrator for VCOs.
基金Sponsored by the National Natural Science Foundation of China(No.51605415)Natural Science Foundation of Hebei Province(No.F2016203494,E2017203240)。
文摘Due to the release of gravity in the space environment, the dynamic characteristics of the space manipulator have changed compared with that of the ground, which results in the change of its tracking precision. This paper presents a model-free adaptive control(MFAC) strategy to track the desired trajectory under different gravity environment. A dynamic transformation method and full form dynamic linearization(FFDL) approach are selected to dynamicly linearize the system, which can better eliminate the complex dynamics that may exist in the original system. The controlled object uses the two degrees of freedom of space manipulator and the controller only depends on the desired angle and torque of each joint of the space manipulator. Moreover, the proof of stability is also provided. Finally, simulation results are presented to demonstrate the effectiveness of the proposed strategy. It is shown that the proposed approach can achieve better trajectory tracking performance under different gravity environment without changing the control parameters, and the tracking precision can be significantly improved as compared with the proportional differential(PD) control results.
基金supported by the Natural Science Foundation of Jiangsu Province(BK20201159).
文摘This paper provides an improved model-free adaptive control(IMFAC)strategy for solving the surface vessel trajectory tracking issue with time delay and restricted disturbance.Firstly,the original nonlinear time-delay system is transformed into a structure consisting of an unknown residual term and a parameter term with control inputs using a local compact form dynamic linearization(local-CFDL).To take advantage of the resulting structure,use a discrete-time extended state observer(DESO)to estimate the unknown residual factor.Then,according to the study,the inclusion of a time delay has no effect on the linearization structure,and an improved control approach is provided,in which DESO is used to adjust for uncertainties.Furthermore,a DESO-based event-triggered model-free adaptive control(ET-DESO-MFAC)is established by designing event-triggered conditions to assure Lyapunov stability.Only when the system’s indicator fulfills the provided event-triggered condition will the control input signal be updated;otherwise,the control input will stay the same as it is at the last trigger moment.A coordinate compensation approach is developed to reduce the steady-state inaccuracy of trajectory tracking.Finally,simulation experiments are used to assess the effectiveness of the proposed technique for trajectory tracking.
