This paper investigates the adaptive neural network(NN)event-triggered secure formation control problem for nonholonomic mobile robots(NMRs)subject to deception attacks.The NNs are employed to approximate unknown nonl...This paper investigates the adaptive neural network(NN)event-triggered secure formation control problem for nonholonomic mobile robots(NMRs)subject to deception attacks.The NNs are employed to approximate unknown nonlinear functions in robotic dynamics.Since the transmission channel from sensor-to-controller is vulnerable to deception attacks,a NN estimation technique is introduced to estimate the unknown deception attacks.In order to alleviate the amount of communication between controller-and-actuator,an event-triggered mechanism with relative threshold strategy is established.Then,an adaptive NN event-triggered secure formation control method is proposed.It is proved that all closed-loop signals of controlled systems are bounded and the formation tracking errors converge a neighborhood of the origin in the presence of deception attacks.The comparative simulations illustrate the effectiveness of the proposed secure formation control scheme.展开更多
This article investigates the approaching control for fixed-wing Unmanned Aerial Vehi-cle(UAV)aerial recovery in the presence of pre-specified performance requirements,complex air-flows,maneuvering flight of transport...This article investigates the approaching control for fixed-wing Unmanned Aerial Vehi-cle(UAV)aerial recovery in the presence of pre-specified performance requirements,complex air-flows,maneuvering flight of transport aircraft,and different initial deviations.First,a novelcontrol-oriented Six-Degree-Of-Freedom(6-DOF)UAV model considering airflow disturbancesis established for better consistency with the actual UAV system.Then,to achieve satisfactory per-formance in the approaching process,a Flexible Appointed-time Prescribed Performance Control(FAPPC)algorithm,with the features of user-specified time convergence,no overshoot,indepen-dence from the initial value,and singularity-free,is proposed.Specifically,to solve the singularityissue encountered by the existing PPC methods in dealing with sudden disturbances,an adaptiveadjustment signal is introduced in FAPPC to perceive the threat of increasing error and relax thepreset boundaries appropriately.Moreover,minimum learning parameter-based neural networkestimators are developed to approximate unknown lumped disturbances at a low computationalcost.Finally,the stability of the closed system is analyzed via Lyapunov synthesis,and the effective-ness and advantages of the proposed control scheme are demonstrated via simulation andHardware-In-the-Loop(HIL)experimental validation.展开更多
In response to the issues of poor adaptability to low signal-to-noise ratios(SNRs)in existing uniform linear array(ULA)multitarget estimation algorithms and the difficulty of current deep learning methods in effective...In response to the issues of poor adaptability to low signal-to-noise ratios(SNRs)in existing uniform linear array(ULA)multitarget estimation algorithms and the difficulty of current deep learning methods in effectively extracting complex-valued features from data,a cross-scale sparse attention module and a channel-hierarchical spatial pyramid attention module,which are based on the MSPANet block,are introduced into the deep neural network(DNN).This approach better extracts multiscale features of signalling components,facilitating accurate signal feature extraction under low SNR conditions.Experimental data demonstrate that this deep learning model can significantly enhance the accuracy and anti-jamming capability of direction-of-arrival(DOA)estimation in low-signal-to-noise ratio(SNR)scenarios,outperforming traditional methods such as CBF,MUSIC,and ESPRIT.The above optimization achievements possess important practical value for DOA estimation applications in fields like intelligent speech,radar detection,communication systems,and autonomous driving.展开更多
On the basis of the gain-scheduled H∞ design strategy, a novel active fault-tolerant control scheme is proposed. Under the assumption that the effects of faults on the state-space matrices of systems can be of affine...On the basis of the gain-scheduled H∞ design strategy, a novel active fault-tolerant control scheme is proposed. Under the assumption that the effects of faults on the state-space matrices of systems can be of affine parameter dependence, a reconfigurable robust H∞ linear parameter varying controller is developed. The designed controller is a function of the fault effect factors that can be derived online by using a well-trained neural network. To demonstrate the effectiveness of the proposed method, a double inverted pendulum system, with a fault in the motor tachometer loop, is considered.展开更多
Piezo-actuated stage is a core component in micro-nano manufacturing field.However,the inherent nonlinearity,such as rate-dependent hysteresis,in the piezo-actuated stage severely impacts its tracking accuracy.This st...Piezo-actuated stage is a core component in micro-nano manufacturing field.However,the inherent nonlinearity,such as rate-dependent hysteresis,in the piezo-actuated stage severely impacts its tracking accuracy.This study proposes a direct adaptive control(DAC)method to realize high precision tracking.The proposed controller is designed by a time delay recursive neural network.Compared with those existing DAC methods designed under the general Lipschitz condition,the proposed control method can be easily generalized to the actual systems,which have hysteresis behavior.Then,a hopfield neural network(HNN)estimator is proposed to adjust the parameters of the proposed controller online.Meanwhile,a modular model consisting of linear submodel,hysteresis submodel,and lumped uncertainties is established based on the HNN estimator to describe the piezoactuated stage in this study.Thus,the performance of the HNN estimator can be exhibited visually through the modeling results.The proposed control method eradicates the adverse effects on the control performance arising from the inaccuracy in establishing the offline model and improves the capability to suppress the influence of hysteresis on the tracking accuracy of piezo-actuated stage in comparison with the conventional DAC methods.The stability of the control system is studied.Finally,a series of comparison experiments with a dual neural networks-based data driven adaptive controller are carried out to demonstrate the superiority of the proposed controller.展开更多
Various methods of tyre modelling are implemented from pure theoretical to empirical or semi-empirical models based on experimental results. A new way of representing tyre data obtained from measurements is presented ...Various methods of tyre modelling are implemented from pure theoretical to empirical or semi-empirical models based on experimental results. A new way of representing tyre data obtained from measurements is presented via support vector machines （SVMs）. The feasibility of applying SVMs to steady-state tyre modelling is investigated by comparison with three-layer backpropagation （BP） neural network at pure slip and combined slip. The results indicate SVMs outperform the BP neural network in modelling the tyre characteristics with better generalization performance. The SVMsqyre is implemented in 8-DOF vehicle model for vehicle dynamics simulation by means of the PAC 2002 Magic Formula as reference. The SVMs-tyre can be a competitive and accurate method to model a tyre for vehicle dynamics simuLation.展开更多
This paper describes a channel estimation and equalization algorithm using three-layer artificial neural networks （ANNs） with feedback for multiple input multiple output wireless communication systems. An ANN struct...This paper describes a channel estimation and equalization algorithm using three-layer artificial neural networks （ANNs） with feedback for multiple input multiple output wireless communication systems. An ANN structure with feedback was designed to use different learning algorithms in the different ANN layers. This actually forms a Turbo iteration process between the different algorithms which effectively improves the estimation performance of the channel equalizer. Simulation results show that this channel equalization algorithm has better computational efficiency and faster convergence than higher order statistics based algorithms.展开更多
In this paper, multimodel and neural emulators are proposed for uncoupled multivariable nonlinear plants with unknown dynamics. The contributions of this paper are to extend the emulators to multivariable non square s...In this paper, multimodel and neural emulators are proposed for uncoupled multivariable nonlinear plants with unknown dynamics. The contributions of this paper are to extend the emulators to multivariable non square systems and to propose a systematic method to compute the multimodel synthesis parameters. The effectiveness of the proposed emulators is shown through two simulation examples. The obtained results are very satisfactory, they illustrate the performance of both emulators and show the advantages of the multimodel emulator relatively to the neural one.展开更多
基金supported by the National Natural Science Foundation of China under 62173172.
文摘This paper investigates the adaptive neural network(NN)event-triggered secure formation control problem for nonholonomic mobile robots(NMRs)subject to deception attacks.The NNs are employed to approximate unknown nonlinear functions in robotic dynamics.Since the transmission channel from sensor-to-controller is vulnerable to deception attacks,a NN estimation technique is introduced to estimate the unknown deception attacks.In order to alleviate the amount of communication between controller-and-actuator,an event-triggered mechanism with relative threshold strategy is established.Then,an adaptive NN event-triggered secure formation control method is proposed.It is proved that all closed-loop signals of controlled systems are bounded and the formation tracking errors converge a neighborhood of the origin in the presence of deception attacks.The comparative simulations illustrate the effectiveness of the proposed secure formation control scheme.
基金funded by the National Natural Science Foundation of China(Nos.62173022,61673042)the Academic Excellence Foundation of Beihang University for Ph.D.Studentsthe Outstanding Research Project of Shen Yuan Honors College,Beihang University,China(No.230123104)。
文摘This article investigates the approaching control for fixed-wing Unmanned Aerial Vehi-cle(UAV)aerial recovery in the presence of pre-specified performance requirements,complex air-flows,maneuvering flight of transport aircraft,and different initial deviations.First,a novelcontrol-oriented Six-Degree-Of-Freedom(6-DOF)UAV model considering airflow disturbancesis established for better consistency with the actual UAV system.Then,to achieve satisfactory per-formance in the approaching process,a Flexible Appointed-time Prescribed Performance Control(FAPPC)algorithm,with the features of user-specified time convergence,no overshoot,indepen-dence from the initial value,and singularity-free,is proposed.Specifically,to solve the singularityissue encountered by the existing PPC methods in dealing with sudden disturbances,an adaptiveadjustment signal is introduced in FAPPC to perceive the threat of increasing error and relax thepreset boundaries appropriately.Moreover,minimum learning parameter-based neural networkestimators are developed to approximate unknown lumped disturbances at a low computationalcost.Finally,the stability of the closed system is analyzed via Lyapunov synthesis,and the effective-ness and advantages of the proposed control scheme are demonstrated via simulation andHardware-In-the-Loop(HIL)experimental validation.
基金funded by the Xinjiang Uygur Autonomous Region Natural Science Foundation General Program(Project Number:2023D01C18)the second batch of Tianchi Talents(Leading Talents)project in Xinjiang Uygur Autonomous Region.Project leader:Lei Liu from School of Computer Science and Technology,Xinjiang University.
文摘In response to the issues of poor adaptability to low signal-to-noise ratios(SNRs)in existing uniform linear array(ULA)multitarget estimation algorithms and the difficulty of current deep learning methods in effectively extracting complex-valued features from data,a cross-scale sparse attention module and a channel-hierarchical spatial pyramid attention module,which are based on the MSPANet block,are introduced into the deep neural network(DNN).This approach better extracts multiscale features of signalling components,facilitating accurate signal feature extraction under low SNR conditions.Experimental data demonstrate that this deep learning model can significantly enhance the accuracy and anti-jamming capability of direction-of-arrival(DOA)estimation in low-signal-to-noise ratio(SNR)scenarios,outperforming traditional methods such as CBF,MUSIC,and ESPRIT.The above optimization achievements possess important practical value for DOA estimation applications in fields like intelligent speech,radar detection,communication systems,and autonomous driving.
文摘On the basis of the gain-scheduled H∞ design strategy, a novel active fault-tolerant control scheme is proposed. Under the assumption that the effects of faults on the state-space matrices of systems can be of affine parameter dependence, a reconfigurable robust H∞ linear parameter varying controller is developed. The designed controller is a function of the fault effect factors that can be derived online by using a well-trained neural network. To demonstrate the effectiveness of the proposed method, a double inverted pendulum system, with a fault in the motor tachometer loop, is considered.
基金supported by the National Natural Science Foundation of China(Grant Nos.51675228 and 51875237)the Key Project of Science and Technology Development Plan of Jilin Province,China(Grant No.20190303020SF)。
文摘Piezo-actuated stage is a core component in micro-nano manufacturing field.However,the inherent nonlinearity,such as rate-dependent hysteresis,in the piezo-actuated stage severely impacts its tracking accuracy.This study proposes a direct adaptive control(DAC)method to realize high precision tracking.The proposed controller is designed by a time delay recursive neural network.Compared with those existing DAC methods designed under the general Lipschitz condition,the proposed control method can be easily generalized to the actual systems,which have hysteresis behavior.Then,a hopfield neural network(HNN)estimator is proposed to adjust the parameters of the proposed controller online.Meanwhile,a modular model consisting of linear submodel,hysteresis submodel,and lumped uncertainties is established based on the HNN estimator to describe the piezoactuated stage in this study.Thus,the performance of the HNN estimator can be exhibited visually through the modeling results.The proposed control method eradicates the adverse effects on the control performance arising from the inaccuracy in establishing the offline model and improves the capability to suppress the influence of hysteresis on the tracking accuracy of piezo-actuated stage in comparison with the conventional DAC methods.The stability of the control system is studied.Finally,a series of comparison experiments with a dual neural networks-based data driven adaptive controller are carried out to demonstrate the superiority of the proposed controller.
基金This project is supported by Shanghai Automobile Industry Corporation Technology Foundation, China(No.0224).
文摘Various methods of tyre modelling are implemented from pure theoretical to empirical or semi-empirical models based on experimental results. A new way of representing tyre data obtained from measurements is presented via support vector machines （SVMs）. The feasibility of applying SVMs to steady-state tyre modelling is investigated by comparison with three-layer backpropagation （BP） neural network at pure slip and combined slip. The results indicate SVMs outperform the BP neural network in modelling the tyre characteristics with better generalization performance. The SVMsqyre is implemented in 8-DOF vehicle model for vehicle dynamics simulation by means of the PAC 2002 Magic Formula as reference. The SVMs-tyre can be a competitive and accurate method to model a tyre for vehicle dynamics simuLation.
基金Supported by the Basic Research Foundation of Tsinghua Na-tional Laboratory for Information Science and Technology (TNList) the Major Program of the National Natural Science Foundation of China (No. 60496311)
文摘This paper describes a channel estimation and equalization algorithm using three-layer artificial neural networks （ANNs） with feedback for multiple input multiple output wireless communication systems. An ANN structure with feedback was designed to use different learning algorithms in the different ANN layers. This actually forms a Turbo iteration process between the different algorithms which effectively improves the estimation performance of the channel equalizer. Simulation results show that this channel equalization algorithm has better computational efficiency and faster convergence than higher order statistics based algorithms.
文摘In this paper, multimodel and neural emulators are proposed for uncoupled multivariable nonlinear plants with unknown dynamics. The contributions of this paper are to extend the emulators to multivariable non square systems and to propose a systematic method to compute the multimodel synthesis parameters. The effectiveness of the proposed emulators is shown through two simulation examples. The obtained results are very satisfactory, they illustrate the performance of both emulators and show the advantages of the multimodel emulator relatively to the neural one.
