Ten physical and environmental variables collected from an on-the-go soil sensor at two field sites (MF3E and MF11S) in Mississippi, USA, were analyzed to assess soil variability and the interrelationships among the m...Ten physical and environmental variables collected from an on-the-go soil sensor at two field sites (MF3E and MF11S) in Mississippi, USA, were analyzed to assess soil variability and the interrelationships among the measurements. At MF3E, moderate variability was observed in apparent electrical conductivity shallow (ECas), slope, and ECa ratio measurements, with coefficients of variation ranging from 20% to 27%. In contrast, MF11S exhibited higher variability, particularly in ECas and ECad (deep) measurements, which exceeded 30% in their coefficient of variation values, indicating significant differences in soil composition and moisture content. Correlation analysis revealed strong positive relationships between the near-infrared-to-red ratio and red reflectance (r = 0.897***) soil values at MF3E. MF11S demonstrated a strong negative correlation between ECas and ECad readings with the x-coordinate (r ***). Scatter plots and fitted models illustrated the complexity of relationships, with many showing nonlinear trends. These findings emphasize the need for continuous monitoring and advanced modeling to understand the dynamic nature of soil properties and their implications for agricultural practices. Future research should explore the underlying mechanisms driving variability in the soil characteristics to enhance soil management strategies at the study sites.展开更多
We study the correlation between detrended fluctuation analysis（DFA） and the Lempel-Ziv complexity（LZC） in nonlinear time series analysis in this paper.Typical dynamic systems including a logistic map and a Duffin...We study the correlation between detrended fluctuation analysis（DFA） and the Lempel-Ziv complexity（LZC） in nonlinear time series analysis in this paper.Typical dynamic systems including a logistic map and a Duffing model are investigated.Moreover,the influence of Gaussian random noise on both the DFA and LZC are analyzed.The results show a high correlation between the DFA and LZC,which can quantify the non-stationarity and the nonlinearity of the time series,respectively.With the enhancement of the random component,the exponent α and the normalized complexity index C show increasing trends.In addition,C is found to be more sensitive to the fluctuation in the nonlinear time series than α.Finally,the correlation between the DFA and LZC is applied to the extraction of vibration signals for a reciprocating compressor gas valve,and an effective fault diagnosis result is obtained.展开更多
Chaos theory has taught us that a system which has both nonlinearity and random input will most likely produce irregular data. If random errors are irregular data, then random error process will raise nonlinearity （K...Chaos theory has taught us that a system which has both nonlinearity and random input will most likely produce irregular data. If random errors are irregular data, then random error process will raise nonlinearity （Kantz and Schreiber （1997））. Tsai （1986） introduced a composite test for autocorrelation and heteroscedasticity in linear models with AR（1） errors. Liu （2003） introduced a composite test for correlation and heteroscedasticity in nonlinear models with DBL（p, 0, 1） errors. Therefore, the important problems in regression model axe detections of bilinearity, correlation and heteroscedasticity. In this article, the authors discuss more general case of nonlinear models with DBL（p, q, 1） random errors by score test. Several statistics for the test of bilinearity, correlation, and heteroscedasticity are obtained, and expressed in simple matrix formulas. The results of regression models with linear errors are extended to those with bilinear errors. The simulation study is carried out to investigate the powers of the test statistics. All results of this article extend and develop results of Tsai （1986）, Wei, et al （1995）, and Liu, et al （2003）.展开更多
The auto-correlation function and the cross-correlation of an autonomous stochastic system with nonlinear time-delayed feedback are investigated by using the stochastic simulation method. There are prominent differenc...The auto-correlation function and the cross-correlation of an autonomous stochastic system with nonlinear time-delayed feedback are investigated by using the stochastic simulation method. There are prominent differences be- tween the roles of quadratic time-delayed feedback and cubic time-delayed feedback on the correlations of an autonomous stochastic system. Under quadratic time-delayed feedback, the nonlinear time delay fails to improve the noisy state of the autonomous stochastic system, the auto-correlation decreases monotonously to zero, and the cross-correlation increases monotonously to zero with the decay time. Under cubic time-delayed feedback, the nonlinear time delay can improve the noisy state of the autonomous stochastic system; the auto-correlation and the cross-correlation show periodical oscillation and attenuation, finally tending to zero with the decay time. Comparing the correlations of the system between with nonfinear time-delayed feedback and linear time-delayed feedback, we find that nonlinear time-delayed feedback lowers the correlation strength of the autonomous stochastic system.展开更多
In this paper, a numerical method for correlation sensitivity analysis of a nonlinear random vibration system is presented. Based on the first passage failure model, the probability perturbation method is employed to ...In this paper, a numerical method for correlation sensitivity analysis of a nonlinear random vibration system is presented. Based on the first passage failure model, the probability perturbation method is employed to determine the statistical characteristics of failure modes and the correlation between them. The sensitivity of correlation between failure modes with respect to random parameters characterizing the uncertainty of the hysteretic loop is discussed. In a numerical example, a two-DOF shear structure with uncertain hysteretic restoring force is considered. The statistical characteristics of response, failure modes and the sensitivity of random hysteretic loop parameters are provided, and also compared with a Monte Carlo simulation.展开更多
New sigma point filtering algorithms, including the unscented Kalman filter （UKF） and the divided difference filter （DDF）, are designed to solve the nonlinear filtering problem under the condition of correlated no...New sigma point filtering algorithms, including the unscented Kalman filter （UKF） and the divided difference filter （DDF）, are designed to solve the nonlinear filtering problem under the condition of correlated noises. Based on the minimum mean square error estimation theory, the nonlinear optimal predictive and correction recursive formulas under the hypothesis that the input noise is correlated with the measurement noise are derived and can be described in a unified framework. Then, UKF and DDF with correlated noises are proposed on the basis of approximation of the posterior mean and covariance in the unified framework by using unscented transformation and second order Stirling＇s interpolation. The proposed UKF and DDF with correlated noises break through the limitation that input noise and measurement noise must be assumed to be uneorrelated in standard UKF and DDF. Two simulation examples show the effectiveness and feasibility of new algorithms for dealing with nonlinear filtering issue with correlated noises.展开更多
The Chinese ancient sage Laozi said that everything comes from ＇nothing＇. In the work [Chin. Phys. Lett. 30？（2013）？080202], infinitely many discrete integrable systems have been obtained from nothing via simple ...The Chinese ancient sage Laozi said that everything comes from ＇nothing＇. In the work [Chin. Phys. Lett. 30？（2013）？080202], infinitely many discrete integrable systems have been obtained from nothing via simple principles （Dao）. In this study, a new idea, the consistent correlated bang, is introduced to obtain nonlinear dynamic systems including some integrable ones such as the continuous nonlinear Schr？dinger equation, the （potential） Korteweg de Vries equation, the （potential） Kadomtsev–Petviashvili equation and the sine-Gordon equation. These nonlinear systems are derived from nothing via suitable ＇Dao＇, the shifted parity, the charge conjugate, the delayed time reversal, the shifted exchange, the shifted-parity-rotation and so on.展开更多
In order to achieve failure prediction without manual intervention for distributed systems, a novel failure feature analysis and extraction approach to automate failure prediction is proposed. Compared with the tradit...In order to achieve failure prediction without manual intervention for distributed systems, a novel failure feature analysis and extraction approach to automate failure prediction is proposed. Compared with the traditional methods which focus on building heuristic rules or models, the autonomic prediction approach analyzes the nonlinear correlation of failure features by recognizing failure patterns. Failure data are sorted according to the nonlinear correlation and failure signature is proposed for autonomic prediction. In addition, the Manifold Learning algorithm named supervised locally linear embedding is applied to achieve feature extraction. Based on the runtime monitoring of failure metrics, the experimental results indicate that the proposed method has better performance in terms of both correlation recognition precision and feature extraction quality and thus it can be used to design efficient autonomic failure prediction for distributed systems.展开更多
Zeroing neural dynamic(ZND)model is widely deployed for time-variant non-linear equations(TVNE).Various element-wise non-linear activation functions and integration operations are investigated to enhance the convergen...Zeroing neural dynamic(ZND)model is widely deployed for time-variant non-linear equations(TVNE).Various element-wise non-linear activation functions and integration operations are investigated to enhance the convergence performance and robustness in most proposed ZND models for solving TVNE,leading to a huge cost of hardware implementation and model complexity.To overcome these problems,the authors develop a new norm-based ZND(NBZND)model with strong robustness for solving TVNE,not applying element-wise non-linear activated functions but introducing a two-norm operation to achieve finite-time convergence.Moreover,the authors develop a discretetime NBZND model for the potential deployment of the model on digital computers.Rigorous theoretical analysis for the NBZND is provided.Simulation results substantiate the advantages of the NBZND model for solving TVNE.展开更多
This paper addresses the computational problem of fixed-interval smoothing state estimation in linear time-varying Gaussian stochastic systems.A new fixed-interval Kalman smoothing algorithm is proposed,and the corres...This paper addresses the computational problem of fixed-interval smoothing state estimation in linear time-varying Gaussian stochastic systems.A new fixed-interval Kalman smoothing algorithm is proposed,and the corresponding form of the smoother is derived.The method is able to accommodate situations where process and measurement noises are correlated,a limitation often encountered in conventional approaches.The Kalman smoothing problem discussed in this paper can be reformulated as an equivalent constrained optimization problem,where the solution corresponds to a set of linear equations defined by a specific co-efficient matrix.Through multiple permutations,the co-efficient matrix of linear equations is transformed into a block tridiagonal form,and then both sides of the linear system are multiplied by the inverse of the co-efficient matrix.This approach is based on the transformation of linear systems described in the SPIKE algorithm and is particularly well-suited for large-scale sparse block tridiagonal matrix structures.It enables efficient,parallel,and flexible solutions while maintaining a certain degree of block diagonal dominance.Compared to directly solving block tridiagonal co-efficient matrices,this method demonstrates appreciable advantages in terms of numerical stability and computational efficiency.Consequently,the new smoothing algorithm yields a new smoother that features fewer constraints and broader applicability than traditional methods.The estimates,such as smoothed state,covariance,and cross-covariance,are essential for fields,such as system identification,navigation,guidance,and control.Finally,the effectiveness of the proposed smoothing algorithm and smoother is validated through numerical simulations.展开更多
This paper considers adaptive event-triggered stabilization for a class of uncertain time-varying nonlinear systems.Remarkably,the systems contain intrinsic time-varying unknown parameters which are allowed to be non-...This paper considers adaptive event-triggered stabilization for a class of uncertain time-varying nonlinear systems.Remarkably,the systems contain intrinsic time-varying unknown parameters which are allowed to be non-differentiable and in turn can be fast-varying.Moreover,the systems admit unknown control directions.To counteract the different uncertainties,more than one compensation mechanism has to be incorporated.However,in the context of event-triggered control,ensuring the effectiveness of these compensation mechanisms under reduced execution necessitates delicate design and analysis.This paper proposes a tight and powerful strategy for adaptive event-triggered control(ETC)by integrating the state-of-the-art adaptive techniques.In particular,the strategy substantially mitigates the conservatism caused by repetitive inequality-based treatments of uncertainties.Specifically,by leveraging the congelation-of-variables method and tuning functions,the conservatism in the treatment of the fast-varying parameters is significantly reduced.With multiple Nussbaum functions employed to handle unknown control directions,a set of dynamic compensations is designed to counteract unknown amplitudes of control coefficients without relying on inequality-based treatments.Moreover,a dedicated dynamic compensation is introduced to deal with the control coefficient coupled with the execution error,based on which a relativethreshold event-triggering mechanism(ETM)is rigorously validated.It turns out that the adaptive event-triggered controller achieves the closed-loop convergence while guaranteeing a uniform lower bound for inter-execution times.Simulation results verify the effectiveness and superiority of the proposed strategy.展开更多
Abstract--In this paper, an adaptive neural network (NN) control approach is proposed for nonlinear pure-feedback sys- tems with time-varying full state constraints. The pure-feedback systems of this paper are assumed...Abstract--In this paper, an adaptive neural network (NN) control approach is proposed for nonlinear pure-feedback sys- tems with time-varying full state constraints. The pure-feedback systems of this paper are assumed to possess nonlinear function uncertainties. By using the mean value theorem, pure-feedback systems can be transformed into strict feedback forms. For the newly generated systems, NNs are employed to approximate unknown items. Based on the adaptive control scheme and backstepping algorithm, an intelligent controller is designed. At the same time, time-varying Barrier Lyapunov functions (BLFs) with error variables are adopted to avoid violating full state constraints in every step of the backstepping design. All closed- loop signals are uniformly ultimately bounded and the output tracking error converges to the neighborhood of zero, which can be verified by using the Lyapunov stability theorem. Two simulation examples reveal the performance of the adaptive NN control approach. Index TermsmAdaptive control, neural networks (NNs), non- linear pure-feedback systems, time-varying constraints.展开更多
In this paper, adaptive variable structure neural control is presented for a class of uncertain multi-input multi-output （MIMO） nonlinear systems with state time-varying delays and unknown nonlinear dead-zones. The ...In this paper, adaptive variable structure neural control is presented for a class of uncertain multi-input multi-output （MIMO） nonlinear systems with state time-varying delays and unknown nonlinear dead-zones. The unknown time-varying delay uncer- tainties are compensated for using appropriate Lyapunov-Krasovskii functionals in the design. The approach removes the assumption of linear function outside the deadband without necessarily constructing a dead-zone inverse as an added contribution. By utilizing the integral-type Lyapunov function and introducing an adaptive compensation term for the upper bound of the residual and optimal approximation error as well as the dead-zone disturbance, the closed-loop control system is proved to be semi-globally uniformly ultimately bounded. In addition, a modified adaptive control algorithm is given in order to avoid the high-frequency chattering phenomenon. Simulation results demonstrate the effectiveness of the approach.展开更多
This paper aims to solve the robust iterative learning control(ILC)problems for nonlinear time-varying systems in the presence of nonrepetitive uncertainties.A new optimization-based method is proposed to design and a...This paper aims to solve the robust iterative learning control(ILC)problems for nonlinear time-varying systems in the presence of nonrepetitive uncertainties.A new optimization-based method is proposed to design and analyze adaptive ILC,for which robust convergence analysis via a contraction mapping approach is realized by leveraging properties of substochastic matrices.It is shown that robust tracking tasks can be realized for optimization-based adaptive ILC,where the boundedness of system trajectories and estimated parameters can be ensured,regardless of unknown time-varying nonlinearities and nonrepetitive uncertainties.Two simulation tests,especially implemented for an injection molding process,demonstrate the effectiveness of our robust optimization-based ILC results.展开更多
This paper tackles the robust leaderless Time-Varying Formation(TVF)control problem for the Unmanned Aerial Vehicle(UAV)swarm system with Lipschitz nonlinear dynamics,external disturbances and directed switching topol...This paper tackles the robust leaderless Time-Varying Formation(TVF)control problem for the Unmanned Aerial Vehicle(UAV)swarm system with Lipschitz nonlinear dynamics,external disturbances and directed switching topologies.In comparison with the previous achievements on formation control problems,the UAV swarm system with Lipschitz nonlinear dynamics can accomplish the pre-designed TVF while tracking a pre-given trajectory which is produced by a virtual leader UAV in the presence of external disturbances.Firstly,by applying the consensus theory,a TVF controller is developed with the local neighborhood status information,the errors of real time status of all UAVs,the expected formation configuration and the pre-given trajectory under directed switching topologies.Secondly,through a certain matrix variable substitution,the UAV swarm system formation control issue is transformed into a lower dimensional asymptotically stable control issue.Thirdly,by introducing the minimum dwell time,the design steps of formation control algorithm are further acquired.In the meantime,the stability of the UAV swarm system is analyzed through the construction of a piecewise continuous Lyapunov functional and via the Linear Matrix Inequalities(LMIs)method.Finally,the comparison results of a numerical simulation are elaborated to verify the validity of the proposed approach.展开更多
Particle filtering （PF） is being applied successfully in nonlinear and/or non-Gaussian system failure prognosis. However, for failure prediction of many complex systems whose dynamic state evolution models involve t...Particle filtering （PF） is being applied successfully in nonlinear and/or non-Gaussian system failure prognosis. However, for failure prediction of many complex systems whose dynamic state evolution models involve time-varying parameters, the tradi- tional PF-based prognosis framework will probably generate serious deviations in results since it implements prediction through iterative calculation using the state models. To address the problem, this paper develops a novel integrated PF-LSSVR frame- work based on PF and least squares support vector regression （LSSVR） for nonlinear system failure prognosis. This approach employs LSSVR for long-term observation series prediction and applies PF-based dual estimation to collaboratively estimate the values of system states and parameters of the corresponding future time instances. Meantime, the propagation of prediction un- certainty is emphatically taken into account. Therefore, PF-LSSVR avoids over-dependency on system state models in prediction phase. With a two-sided failure definition, the probability distribution of system remaining useful life （RUL） is accessed and the corresponding methods of calculating performance evaluation metrics are put forward. The PF-LSSVR framework is applied to a three-vessel water tank system failure prognosis and it has much higher prediction accuracy and confidence level than traditional PF-based framework.展开更多
In this paper,the leader–follower consensus of feedforward nonlinear multi-agent systems is achieved by designing the distributed output feedback controllers with a time-varying gain.The agents dynamics are assumed t...In this paper,the leader–follower consensus of feedforward nonlinear multi-agent systems is achieved by designing the distributed output feedback controllers with a time-varying gain.The agents dynamics are assumed to be in upper triangular structure and satisfy Lipschitz conditions with an unknown constant multiplied by a time-varying function.A time-varying gain,which increases monotonously and tends to infinity,is proposed to construct a compensator for each follower agent.Based on a directed communication topology,the distributed output feedback controller with a time-varying gain is designed for each follower agent by only using the output information of the follower and its neighbors.It is proved by the Lyapunov theorem that the leader–follower consensus of the multi-agent system is achieved by the proposed consensus protocol.The effectiveness of the proposed time-varying gain method is demonstrated by a circuit system.展开更多
The adaptive control of nonlinear systems that are linear in the unknown but time-varying parameters are treated in this paper.Since satisfactory transient performance is an important factor,multiple models are requir...The adaptive control of nonlinear systems that are linear in the unknown but time-varying parameters are treated in this paper.Since satisfactory transient performance is an important factor,multiple models are required as these parameters change abruptly in the parameter space.In this paper we consider both the multiple models with switching and tuning methodology as well as multiple models with second level adaptation for this class of systems.We demonstrate that the latter approach is better than the former.展开更多
This paper studies delay dependent robust stability and the stabilization problem of nonlinear perturbed systems with time varying delay. A new set of sufficient conditions for the stability of open as well as close l...This paper studies delay dependent robust stability and the stabilization problem of nonlinear perturbed systems with time varying delay. A new set of sufficient conditions for the stability of open as well as close loop systems are obtained in the sense of Lyapunov-Krasovskii. To reduce the conservatism, the work exploits the idea of splitting the delay interval into multiple equal regions so that less information on the time delay can be imposed to derive the results. The derived criterion not only improves the upper bounds of the time delay but also does not require the derivative of the delay to be known at prior. Easily testable sufficient criteria are presented in terms of linear matrix inequalities. It is shown that the derived conditions are very less conservative while comparing the maximum allowable upper bound of delay with the existing results in literature.展开更多
文摘Ten physical and environmental variables collected from an on-the-go soil sensor at two field sites (MF3E and MF11S) in Mississippi, USA, were analyzed to assess soil variability and the interrelationships among the measurements. At MF3E, moderate variability was observed in apparent electrical conductivity shallow (ECas), slope, and ECa ratio measurements, with coefficients of variation ranging from 20% to 27%. In contrast, MF11S exhibited higher variability, particularly in ECas and ECad (deep) measurements, which exceeded 30% in their coefficient of variation values, indicating significant differences in soil composition and moisture content. Correlation analysis revealed strong positive relationships between the near-infrared-to-red ratio and red reflectance (r = 0.897***) soil values at MF3E. MF11S demonstrated a strong negative correlation between ECas and ECad readings with the x-coordinate (r ***). Scatter plots and fitted models illustrated the complexity of relationships, with many showing nonlinear trends. These findings emphasize the need for continuous monitoring and advanced modeling to understand the dynamic nature of soil properties and their implications for agricultural practices. Future research should explore the underlying mechanisms driving variability in the soil characteristics to enhance soil management strategies at the study sites.
基金Project supported by the National Natural Science Foundation of China (Grant No. 51175316)the Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20103108110006)
文摘We study the correlation between detrended fluctuation analysis（DFA） and the Lempel-Ziv complexity（LZC） in nonlinear time series analysis in this paper.Typical dynamic systems including a logistic map and a Duffing model are investigated.Moreover,the influence of Gaussian random noise on both the DFA and LZC are analyzed.The results show a high correlation between the DFA and LZC,which can quantify the non-stationarity and the nonlinearity of the time series,respectively.With the enhancement of the random component,the exponent α and the normalized complexity index C show increasing trends.In addition,C is found to be more sensitive to the fluctuation in the nonlinear time series than α.Finally,the correlation between the DFA and LZC is applied to the extraction of vibration signals for a reciprocating compressor gas valve,and an effective fault diagnosis result is obtained.
文摘Chaos theory has taught us that a system which has both nonlinearity and random input will most likely produce irregular data. If random errors are irregular data, then random error process will raise nonlinearity （Kantz and Schreiber （1997））. Tsai （1986） introduced a composite test for autocorrelation and heteroscedasticity in linear models with AR（1） errors. Liu （2003） introduced a composite test for correlation and heteroscedasticity in nonlinear models with DBL（p, 0, 1） errors. Therefore, the important problems in regression model axe detections of bilinearity, correlation and heteroscedasticity. In this article, the authors discuss more general case of nonlinear models with DBL（p, q, 1） random errors by score test. Several statistics for the test of bilinearity, correlation, and heteroscedasticity are obtained, and expressed in simple matrix formulas. The results of regression models with linear errors are extended to those with bilinear errors. The simulation study is carried out to investigate the powers of the test statistics. All results of this article extend and develop results of Tsai （1986）, Wei, et al （1995）, and Liu, et al （2003）.
基金Supported by the National Natural Science Foundation of China under Grant No.11265012Yunnan Province Open Key Laboratory of Mechanics in Colleges and Universities
文摘The auto-correlation function and the cross-correlation of an autonomous stochastic system with nonlinear time-delayed feedback are investigated by using the stochastic simulation method. There are prominent differences be- tween the roles of quadratic time-delayed feedback and cubic time-delayed feedback on the correlations of an autonomous stochastic system. Under quadratic time-delayed feedback, the nonlinear time delay fails to improve the noisy state of the autonomous stochastic system, the auto-correlation decreases monotonously to zero, and the cross-correlation increases monotonously to zero with the decay time. Under cubic time-delayed feedback, the nonlinear time delay can improve the noisy state of the autonomous stochastic system; the auto-correlation and the cross-correlation show periodical oscillation and attenuation, finally tending to zero with the decay time. Comparing the correlations of the system between with nonfinear time-delayed feedback and linear time-delayed feedback, we find that nonlinear time-delayed feedback lowers the correlation strength of the autonomous stochastic system.
基金National Natural Science Foundation of ChinaUnder Grant No: 50535010
文摘In this paper, a numerical method for correlation sensitivity analysis of a nonlinear random vibration system is presented. Based on the first passage failure model, the probability perturbation method is employed to determine the statistical characteristics of failure modes and the correlation between them. The sensitivity of correlation between failure modes with respect to random parameters characterizing the uncertainty of the hysteretic loop is discussed. In a numerical example, a two-DOF shear structure with uncertain hysteretic restoring force is considered. The statistical characteristics of response, failure modes and the sensitivity of random hysteretic loop parameters are provided, and also compared with a Monte Carlo simulation.
基金Projects(61135001, 61075029, 61074155) supported by the National Natural Science Foundation of ChinaProject(20110491690) supported by the Postdocteral Science Foundation of China
文摘New sigma point filtering algorithms, including the unscented Kalman filter （UKF） and the divided difference filter （DDF）, are designed to solve the nonlinear filtering problem under the condition of correlated noises. Based on the minimum mean square error estimation theory, the nonlinear optimal predictive and correction recursive formulas under the hypothesis that the input noise is correlated with the measurement noise are derived and can be described in a unified framework. Then, UKF and DDF with correlated noises are proposed on the basis of approximation of the posterior mean and covariance in the unified framework by using unscented transformation and second order Stirling＇s interpolation. The proposed UKF and DDF with correlated noises break through the limitation that input noise and measurement noise must be assumed to be uneorrelated in standard UKF and DDF. Two simulation examples show the effectiveness and feasibility of new algorithms for dealing with nonlinear filtering issue with correlated noises.
基金Supported by the Global Change Research Program of China under Grant No 2015CB953904the National Natural Science Foundation of China under Grant No 11435005+1 种基金the Shanghai Knowledge Service Platform for Trustworthy Internet of Things under Grant No ZF1213the K.C.Wong Magna Fund in Ningbo University
文摘The Chinese ancient sage Laozi said that everything comes from ＇nothing＇. In the work [Chin. Phys. Lett. 30？（2013）？080202], infinitely many discrete integrable systems have been obtained from nothing via simple principles （Dao）. In this study, a new idea, the consistent correlated bang, is introduced to obtain nonlinear dynamic systems including some integrable ones such as the continuous nonlinear Schr？dinger equation, the （potential） Korteweg de Vries equation, the （potential） Kadomtsev–Petviashvili equation and the sine-Gordon equation. These nonlinear systems are derived from nothing via suitable ＇Dao＇, the shifted parity, the charge conjugate, the delayed time reversal, the shifted exchange, the shifted-parity-rotation and so on.
基金Supported by the National High Technology Research and Development Programme of China （ No. 2007AA01Z401 ） and the National Natural Science Foundation of China （No. 90718003, 60973027）.
文摘In order to achieve failure prediction without manual intervention for distributed systems, a novel failure feature analysis and extraction approach to automate failure prediction is proposed. Compared with the traditional methods which focus on building heuristic rules or models, the autonomic prediction approach analyzes the nonlinear correlation of failure features by recognizing failure patterns. Failure data are sorted according to the nonlinear correlation and failure signature is proposed for autonomic prediction. In addition, the Manifold Learning algorithm named supervised locally linear embedding is applied to achieve feature extraction. Based on the runtime monitoring of failure metrics, the experimental results indicate that the proposed method has better performance in terms of both correlation recognition precision and feature extraction quality and thus it can be used to design efficient autonomic failure prediction for distributed systems.
基金Natural Science Foundation of China,Grant/Award Number:62206109Guangdong Basic and Applied Basic Research Foundation,Grant/Award Number:2022A1515010976+1 种基金Young Scholar Program of Pazhou Lab,Grant/Award Number:PZL2021KF0022National College Student Innovation and Entrepreneurship Training Program,Grant/Award Number:202410559070。
文摘Zeroing neural dynamic(ZND)model is widely deployed for time-variant non-linear equations(TVNE).Various element-wise non-linear activation functions and integration operations are investigated to enhance the convergence performance and robustness in most proposed ZND models for solving TVNE,leading to a huge cost of hardware implementation and model complexity.To overcome these problems,the authors develop a new norm-based ZND(NBZND)model with strong robustness for solving TVNE,not applying element-wise non-linear activated functions but introducing a two-norm operation to achieve finite-time convergence.Moreover,the authors develop a discretetime NBZND model for the potential deployment of the model on digital computers.Rigorous theoretical analysis for the NBZND is provided.Simulation results substantiate the advantages of the NBZND model for solving TVNE.
文摘This paper addresses the computational problem of fixed-interval smoothing state estimation in linear time-varying Gaussian stochastic systems.A new fixed-interval Kalman smoothing algorithm is proposed,and the corresponding form of the smoother is derived.The method is able to accommodate situations where process and measurement noises are correlated,a limitation often encountered in conventional approaches.The Kalman smoothing problem discussed in this paper can be reformulated as an equivalent constrained optimization problem,where the solution corresponds to a set of linear equations defined by a specific co-efficient matrix.Through multiple permutations,the co-efficient matrix of linear equations is transformed into a block tridiagonal form,and then both sides of the linear system are multiplied by the inverse of the co-efficient matrix.This approach is based on the transformation of linear systems described in the SPIKE algorithm and is particularly well-suited for large-scale sparse block tridiagonal matrix structures.It enables efficient,parallel,and flexible solutions while maintaining a certain degree of block diagonal dominance.Compared to directly solving block tridiagonal co-efficient matrices,this method demonstrates appreciable advantages in terms of numerical stability and computational efficiency.Consequently,the new smoothing algorithm yields a new smoother that features fewer constraints and broader applicability than traditional methods.The estimates,such as smoothed state,covariance,and cross-covariance,are essential for fields,such as system identification,navigation,guidance,and control.Finally,the effectiveness of the proposed smoothing algorithm and smoother is validated through numerical simulations.
基金supported in part by the National Natural Science Foundation of China(62033007)the Fundamental Research Program of Shandong Province(ZR2023ZD37).
文摘This paper considers adaptive event-triggered stabilization for a class of uncertain time-varying nonlinear systems.Remarkably,the systems contain intrinsic time-varying unknown parameters which are allowed to be non-differentiable and in turn can be fast-varying.Moreover,the systems admit unknown control directions.To counteract the different uncertainties,more than one compensation mechanism has to be incorporated.However,in the context of event-triggered control,ensuring the effectiveness of these compensation mechanisms under reduced execution necessitates delicate design and analysis.This paper proposes a tight and powerful strategy for adaptive event-triggered control(ETC)by integrating the state-of-the-art adaptive techniques.In particular,the strategy substantially mitigates the conservatism caused by repetitive inequality-based treatments of uncertainties.Specifically,by leveraging the congelation-of-variables method and tuning functions,the conservatism in the treatment of the fast-varying parameters is significantly reduced.With multiple Nussbaum functions employed to handle unknown control directions,a set of dynamic compensations is designed to counteract unknown amplitudes of control coefficients without relying on inequality-based treatments.Moreover,a dedicated dynamic compensation is introduced to deal with the control coefficient coupled with the execution error,based on which a relativethreshold event-triggering mechanism(ETM)is rigorously validated.It turns out that the adaptive event-triggered controller achieves the closed-loop convergence while guaranteeing a uniform lower bound for inter-execution times.Simulation results verify the effectiveness and superiority of the proposed strategy.
基金supported in part by the National Natural Science Foundation of China(61622303,61603164,61773188)the Program for Liaoning Innovative Research Team in University(LT2016006)+1 种基金the Fundamental Research Funds for the Universities of Liaoning Province(JZL201715402)the Program for Distinguished Professor of Liaoning Province
文摘Abstract--In this paper, an adaptive neural network (NN) control approach is proposed for nonlinear pure-feedback sys- tems with time-varying full state constraints. The pure-feedback systems of this paper are assumed to possess nonlinear function uncertainties. By using the mean value theorem, pure-feedback systems can be transformed into strict feedback forms. For the newly generated systems, NNs are employed to approximate unknown items. Based on the adaptive control scheme and backstepping algorithm, an intelligent controller is designed. At the same time, time-varying Barrier Lyapunov functions (BLFs) with error variables are adopted to avoid violating full state constraints in every step of the backstepping design. All closed- loop signals are uniformly ultimately bounded and the output tracking error converges to the neighborhood of zero, which can be verified by using the Lyapunov stability theorem. Two simulation examples reveal the performance of the adaptive NN control approach. Index TermsmAdaptive control, neural networks (NNs), non- linear pure-feedback systems, time-varying constraints.
基金supported by National Natural Science Foundationof China (No. 60774017 and No. 60874045)
文摘In this paper, adaptive variable structure neural control is presented for a class of uncertain multi-input multi-output （MIMO） nonlinear systems with state time-varying delays and unknown nonlinear dead-zones. The unknown time-varying delay uncer- tainties are compensated for using appropriate Lyapunov-Krasovskii functionals in the design. The approach removes the assumption of linear function outside the deadband without necessarily constructing a dead-zone inverse as an added contribution. By utilizing the integral-type Lyapunov function and introducing an adaptive compensation term for the upper bound of the residual and optimal approximation error as well as the dead-zone disturbance, the closed-loop control system is proved to be semi-globally uniformly ultimately bounded. In addition, a modified adaptive control algorithm is given in order to avoid the high-frequency chattering phenomenon. Simulation results demonstrate the effectiveness of the approach.
基金supported by the National Natural Science Foundation of China(61873013,61922007)。
文摘This paper aims to solve the robust iterative learning control(ILC)problems for nonlinear time-varying systems in the presence of nonrepetitive uncertainties.A new optimization-based method is proposed to design and analyze adaptive ILC,for which robust convergence analysis via a contraction mapping approach is realized by leveraging properties of substochastic matrices.It is shown that robust tracking tasks can be realized for optimization-based adaptive ILC,where the boundedness of system trajectories and estimated parameters can be ensured,regardless of unknown time-varying nonlinearities and nonrepetitive uncertainties.Two simulation tests,especially implemented for an injection molding process,demonstrate the effectiveness of our robust optimization-based ILC results.
基金co-supported by the Key-area Research and Development Program of Guangdong ProvinceChina(No.2019B090915001)+2 种基金National Key R&D Program of China(No.2018YFB1308000),National Natural Science Funds of China(Nos.61772508,U1913202,U1813205,U1713213)CAS Key Technology Talent Program,Shenzhen Technology ProjectChina(Nos.JCYJ20180507182610734,JSGG20191129094012321).
文摘This paper tackles the robust leaderless Time-Varying Formation(TVF)control problem for the Unmanned Aerial Vehicle(UAV)swarm system with Lipschitz nonlinear dynamics,external disturbances and directed switching topologies.In comparison with the previous achievements on formation control problems,the UAV swarm system with Lipschitz nonlinear dynamics can accomplish the pre-designed TVF while tracking a pre-given trajectory which is produced by a virtual leader UAV in the presence of external disturbances.Firstly,by applying the consensus theory,a TVF controller is developed with the local neighborhood status information,the errors of real time status of all UAVs,the expected formation configuration and the pre-given trajectory under directed switching topologies.Secondly,through a certain matrix variable substitution,the UAV swarm system formation control issue is transformed into a lower dimensional asymptotically stable control issue.Thirdly,by introducing the minimum dwell time,the design steps of formation control algorithm are further acquired.In the meantime,the stability of the UAV swarm system is analyzed through the construction of a piecewise continuous Lyapunov functional and via the Linear Matrix Inequalities(LMIs)method.Finally,the comparison results of a numerical simulation are elaborated to verify the validity of the proposed approach.
基金Aeronautical Science Foundation of China (20100751010, 2010ZD11007)
文摘Particle filtering （PF） is being applied successfully in nonlinear and/or non-Gaussian system failure prognosis. However, for failure prediction of many complex systems whose dynamic state evolution models involve time-varying parameters, the tradi- tional PF-based prognosis framework will probably generate serious deviations in results since it implements prediction through iterative calculation using the state models. To address the problem, this paper develops a novel integrated PF-LSSVR frame- work based on PF and least squares support vector regression （LSSVR） for nonlinear system failure prognosis. This approach employs LSSVR for long-term observation series prediction and applies PF-based dual estimation to collaboratively estimate the values of system states and parameters of the corresponding future time instances. Meantime, the propagation of prediction un- certainty is emphatically taken into account. Therefore, PF-LSSVR avoids over-dependency on system state models in prediction phase. With a two-sided failure definition, the probability distribution of system remaining useful life （RUL） is accessed and the corresponding methods of calculating performance evaluation metrics are put forward. The PF-LSSVR framework is applied to a three-vessel water tank system failure prognosis and it has much higher prediction accuracy and confidence level than traditional PF-based framework.
基金the National Natural Science Foundation of China(Nos.61973189,62073190)the Research Fund for the Taishan Scholar Project of Shandong Province of China(No.ts20190905)the Natural Science Foundation of Shandong Province of China(No.ZR2020ZD25).
文摘In this paper,the leader–follower consensus of feedforward nonlinear multi-agent systems is achieved by designing the distributed output feedback controllers with a time-varying gain.The agents dynamics are assumed to be in upper triangular structure and satisfy Lipschitz conditions with an unknown constant multiplied by a time-varying function.A time-varying gain,which increases monotonously and tends to infinity,is proposed to construct a compensator for each follower agent.Based on a directed communication topology,the distributed output feedback controller with a time-varying gain is designed for each follower agent by only using the output information of the follower and its neighbors.It is proved by the Lyapunov theorem that the leader–follower consensus of the multi-agent system is achieved by the proposed consensus protocol.The effectiveness of the proposed time-varying gain method is demonstrated by a circuit system.
文摘The adaptive control of nonlinear systems that are linear in the unknown but time-varying parameters are treated in this paper.Since satisfactory transient performance is an important factor,multiple models are required as these parameters change abruptly in the parameter space.In this paper we consider both the multiple models with switching and tuning methodology as well as multiple models with second level adaptation for this class of systems.We demonstrate that the latter approach is better than the former.
文摘This paper studies delay dependent robust stability and the stabilization problem of nonlinear perturbed systems with time varying delay. A new set of sufficient conditions for the stability of open as well as close loop systems are obtained in the sense of Lyapunov-Krasovskii. To reduce the conservatism, the work exploits the idea of splitting the delay interval into multiple equal regions so that less information on the time delay can be imposed to derive the results. The derived criterion not only improves the upper bounds of the time delay but also does not require the derivative of the delay to be known at prior. Easily testable sufficient criteria are presented in terms of linear matrix inequalities. It is shown that the derived conditions are very less conservative while comparing the maximum allowable upper bound of delay with the existing results in literature.
