During bipedal walking,it is critical to detect and adjust the robot postures by feedback control to maintain its normal state amidst multi-source random disturbances arising from some unavoidable uncertain factors.Th...During bipedal walking,it is critical to detect and adjust the robot postures by feedback control to maintain its normal state amidst multi-source random disturbances arising from some unavoidable uncertain factors.The radical basis function（RBF）neural network model of a five-link biped robot is established,and two certain disturbances and a randomly uncertain disturbance are then mixed with the optimal torques in the network model to study the performance of the biped robot by several evaluation indices and a specific Poincar′e map.In contrast with the simulations,the response varies as desired under optimal inputting while the output is fluctuating in the situation of disturbance driving.Simulation results from noise inputting also show that the dynamics of the robot is less sensitive to the disturbance of knee joint input of the swing leg than those of the other three joints,the response errors of the biped will be increasing with higher disturbance levels,and especially there are larger output fluctuations in the knee and hip joints of the swing leg.展开更多
To elucidate the fracturing mechanism of deep hard rock under complex disturbance environments,this study investigates the dynamic failure behavior of pre-damaged granite subjected to multi-source dynamic disturbances...To elucidate the fracturing mechanism of deep hard rock under complex disturbance environments,this study investigates the dynamic failure behavior of pre-damaged granite subjected to multi-source dynamic disturbances.Blasting vibration monitoring was conducted in a deep-buried drill-and-blast tunnel to characterize in-situ dynamic loading conditions.Subsequently,true triaxial compression tests incorporating multi-source disturbances were performed using a self-developed wide-low-frequency true triaxial system to simulate disturbance accumulation and damage evolution in granite.The results demonstrate that combined dynamic disturbances and unloading damage significantly accelerate strength degradation and trigger shear-slip failure along preferentially oriented blast-induced fractures,with strength reductions up to 16.7%.Layered failure was observed on the free surface of pre-damaged granite under biaxial loading,indicating a disturbance-induced fracture localization mechanism.Time-stress-fracture-energy coupling fields were constructed to reveal the spatiotemporal characteristics of fracture evolution.Critical precursor frequency bands(105-150,185-225,and 300-325 kHz)were identified,which serve as diagnostic signatures of impending failure.A dynamic instability mechanism driven by multi-source disturbance superposition and pre-damage evolution was established.Furthermore,a grouting-based wave-absorption control strategy was proposed to mitigate deep dynamic disasters by attenuating disturbance amplitude and reducing excitation frequency.展开更多
Although function projective synchronization in complex dynamical networks has been extensively studied in the literature, few papers deal with the problem between two different complex networks with correlated random...Although function projective synchronization in complex dynamical networks has been extensively studied in the literature, few papers deal with the problem between two different complex networks with correlated random disturbances. In this paper, we present some novel techniques to analyze the problem of synchronization. A probability approach is introduced to obtain an almost sure synchronization criterion. We also present some efficient approaches to analyze the problem of exponential synchronization. For the problem of synchronization in some complex networks, our approaches not only can replace the LaSalle-type theorem but also allow improvements of existing results in the literature. Finally, some numerical examples are provided to demonstrate the effectiveness of the proposed approaches.展开更多
The guaranty of power system stability during a random disturbances, requires systematically a wide knowledge of the disturbance components in one hand and their range of variation in the other hand. The major problem...The guaranty of power system stability during a random disturbances, requires systematically a wide knowledge of the disturbance components in one hand and their range of variation in the other hand. The major problem lies in the approach taken to the identification of the main components of this type of disturbances, Control strategy will only be effective if these disturbances are acceptably modeled. A better approximation of the dynamic components of these disturbances likely to affect power systems, leading to the implementation of a highly reliable control strategy. The identification and evaluation of dynamic components of these disturbances will be the major objective of this study. The control strategy of such disturbances, random, will be develooed to adant itto a wind farm connected to an electrical network.展开更多
A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source di...A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source disturbances are addressed according to their specific characteristics as follows:(A)an MTN data-driven model,which is used for uncertainty description,is designed accompanied with the mechanism model to represent the unmanned systems;(B)an adaptive MTN filter is used to remove the influence of the internal disturbance;(C)an MTN disturbance observer is constructed to estimate and compensate for the influence of the external disturbance;(D)the Extended Kalman Filter(EKF)algorithm is utilized as the learning mechanism for MTNs.Second,to address the time-delay effect,a recursiveτstep-ahead MTN predictive model is designed utilizing recursive technology,aiming to mitigate the impact of time-delay,and the EKF algorithm is employed as its learning mechanism.Then,the MTN predictive control law is designed based on the quadratic performance index.By implementing the proposed composite controller to unmanned systems,simultaneous feedforward compensation and feedback suppression to the multi-source disturbances are conducted.Finally,the convergence of the MTN and the stability of the closed-loop system are established utilizing the Lyapunov theorem.Two exemplary applications of unmanned systems involving unmanned vehicle and rigid spacecraft are presented to validate the effectiveness of the proposed approach.展开更多
In this paper, a method of power quality disturbance classification based on random matrix theory (RMT) is proposed. The method utilizes the power quality disturbance signal to construct a random matrix. By analyzing ...In this paper, a method of power quality disturbance classification based on random matrix theory (RMT) is proposed. The method utilizes the power quality disturbance signal to construct a random matrix. By analyzing the mean spectral radius (MSR) variation of the random matrix, the type and time of occurrence of power quality disturbance are classified. In this paper, the random matrix theory is used to analyze the voltage sag, swell and interrupt perturbation signals to classify the occurrence time, duration of the disturbance signal and thedepth of voltage sag or swell. Examples show that the method has strong anti-noise ability.展开更多
Precise and timely prediction of crop yields is crucial for food security and the development of agricultural policies.However,crop yield is influenced by multiple factors within complex growth environments.Previous r...Precise and timely prediction of crop yields is crucial for food security and the development of agricultural policies.However,crop yield is influenced by multiple factors within complex growth environments.Previous research has paid relatively little attention to the interference of environmental factors and drought on the growth of winter wheat.Therefore,there is an urgent need for more effective methods to explore the inherent relationship between these factors and crop yield,making precise yield prediction increasingly important.This study was based on four type of indicators including meteorological,crop growth status,environmental,and drought index,from October 2003 to June 2019 in Henan Province as the basic data for predicting winter wheat yield.Using the sparrow search al-gorithm combined with random forest(SSA-RF)under different input indicators,accuracy of winter wheat yield estimation was calcu-lated.The estimation accuracy of SSA-RF was compared with partial least squares regression(PLSR),extreme gradient boosting(XG-Boost),and random forest(RF)models.Finally,the determined optimal yield estimation method was used to predict winter wheat yield in three typical years.Following are the findings:1)the SSA-RF demonstrates superior performance in estimating winter wheat yield compared to other algorithms.The best yield estimation method is achieved by four types indicators’composition with SSA-RF)(R^(2)=0.805,RRMSE=9.9%.2)Crops growth status and environmental indicators play significant roles in wheat yield estimation,accounting for 46%and 22%of the yield importance among all indicators,respectively.3)Selecting indicators from October to April of the follow-ing year yielded the highest accuracy in winter wheat yield estimation,with an R^(2)of 0.826 and an RMSE of 9.0%.Yield estimates can be completed two months before the winter wheat harvest in June.4)The predicted performance will be slightly affected by severe drought.Compared with severe drought year(2011)(R^(2)=0.680)and normal year(2017)(R^(2)=0.790),the SSA-RF model has higher prediction accuracy for wet year(2018)(R^(2)=0.820).This study could provide an innovative approach for remote sensing estimation of winter wheat yield.yield.展开更多
This study focuses on exploring the complex dynamical behaviors of a magnetic microrobot in a random environment.The purpose is to reveal the mechanism of influence of random disturbance on microrobot dynamics.This pa...This study focuses on exploring the complex dynamical behaviors of a magnetic microrobot in a random environment.The purpose is to reveal the mechanism of influence of random disturbance on microrobot dynamics.This paper establishes stochastic dynamic models for the microrobot before and after deformation,considering the influence of Gaussian white noise.The system responses are analyzed via steady-state probability density functions and first deformation time.The effects of different magnetic field strengths and fluid viscosities on the movement speed and angular velocity of the microrobot are studied.The results indicate that random disturbances can cause deformation of microrobots in advance compared to the deterministic case.This work contributes to the design and motion control of microrobots and enhances the theoretical foundation of microrobots.展开更多
Accurate estimation of understory terrain has significant scientific importance for maintaining ecosystem balance and biodiversity conservation.Addressing the issue of inadequate representation of spatial heterogeneit...Accurate estimation of understory terrain has significant scientific importance for maintaining ecosystem balance and biodiversity conservation.Addressing the issue of inadequate representation of spatial heterogeneity when traditional forest topographic inversion methods consider the entire forest as the inversion unit,this study pro⁃poses a differentiated modeling approach to forest types based on refined land cover classification.Taking Puerto Ri⁃co and Maryland as study areas,a multi-dimensional feature system is constructed by integrating multi-source re⁃mote sensing data:ICESat-2 spaceborne LiDAR is used to obtain benchmark values for understory terrain,topo⁃graphic factors such as slope and aspect are extracted based on SRTM data,and vegetation cover characteristics are analyzed using Landsat-8 multispectral imagery.This study incorporates forest type as a classification modeling con⁃dition and applies the random forest algorithm to build differentiated topographic inversion models.Experimental re⁃sults indicate that,compared to traditional whole-area modeling methods(RMSE=5.06 m),forest type-based classi⁃fication modeling significantly improves the accuracy of understory terrain estimation(RMSE=2.94 m),validating the effectiveness of spatial heterogeneity modeling.Further sensitivity analysis reveals that canopy structure parame⁃ters(with RMSE variation reaching 4.11 m)exert a stronger regulatory effect on estimation accuracy compared to forest cover,providing important theoretical support for optimizing remote sensing models of forest topography.展开更多
基金supported by the Science Fund for Creative Research Groups of National Natural Science Foundation of China(51221004)the National Natural Science Foundation of China(11172260,11372270,and 51375434)+2 种基金the Higher School Specialized Research Fund for the Doctoral Program(20110101110016)the Science and technology project of Zhejiang Province(2013C31086)the Fundamental Research Funds forthe Central Universities of China(2013XZZX005)
文摘During bipedal walking,it is critical to detect and adjust the robot postures by feedback control to maintain its normal state amidst multi-source random disturbances arising from some unavoidable uncertain factors.The radical basis function（RBF）neural network model of a five-link biped robot is established,and two certain disturbances and a randomly uncertain disturbance are then mixed with the optimal torques in the network model to study the performance of the biped robot by several evaluation indices and a specific Poincar′e map.In contrast with the simulations,the response varies as desired under optimal inputting while the output is fluctuating in the situation of disturbance driving.Simulation results from noise inputting also show that the dynamics of the robot is less sensitive to the disturbance of knee joint input of the swing leg than those of the other three joints,the response errors of the biped will be increasing with higher disturbance levels,and especially there are larger output fluctuations in the knee and hip joints of the swing leg.
基金supported by the National Key R&D Program of China(No.2023YFB2603602)the National Natural Science Foundation of China(Nos.52222810 and 52178383).
文摘To elucidate the fracturing mechanism of deep hard rock under complex disturbance environments,this study investigates the dynamic failure behavior of pre-damaged granite subjected to multi-source dynamic disturbances.Blasting vibration monitoring was conducted in a deep-buried drill-and-blast tunnel to characterize in-situ dynamic loading conditions.Subsequently,true triaxial compression tests incorporating multi-source disturbances were performed using a self-developed wide-low-frequency true triaxial system to simulate disturbance accumulation and damage evolution in granite.The results demonstrate that combined dynamic disturbances and unloading damage significantly accelerate strength degradation and trigger shear-slip failure along preferentially oriented blast-induced fractures,with strength reductions up to 16.7%.Layered failure was observed on the free surface of pre-damaged granite under biaxial loading,indicating a disturbance-induced fracture localization mechanism.Time-stress-fracture-energy coupling fields were constructed to reveal the spatiotemporal characteristics of fracture evolution.Critical precursor frequency bands(105-150,185-225,and 300-325 kHz)were identified,which serve as diagnostic signatures of impending failure.A dynamic instability mechanism driven by multi-source disturbance superposition and pre-damage evolution was established.Furthermore,a grouting-based wave-absorption control strategy was proposed to mitigate deep dynamic disasters by attenuating disturbance amplitude and reducing excitation frequency.
基金Project supported by the National Natural Science Foundation of China(Grant No.61273015)
文摘Although function projective synchronization in complex dynamical networks has been extensively studied in the literature, few papers deal with the problem between two different complex networks with correlated random disturbances. In this paper, we present some novel techniques to analyze the problem of synchronization. A probability approach is introduced to obtain an almost sure synchronization criterion. We also present some efficient approaches to analyze the problem of exponential synchronization. For the problem of synchronization in some complex networks, our approaches not only can replace the LaSalle-type theorem but also allow improvements of existing results in the literature. Finally, some numerical examples are provided to demonstrate the effectiveness of the proposed approaches.
文摘The guaranty of power system stability during a random disturbances, requires systematically a wide knowledge of the disturbance components in one hand and their range of variation in the other hand. The major problem lies in the approach taken to the identification of the main components of this type of disturbances, Control strategy will only be effective if these disturbances are acceptably modeled. A better approximation of the dynamic components of these disturbances likely to affect power systems, leading to the implementation of a highly reliable control strategy. The identification and evaluation of dynamic components of these disturbances will be the major objective of this study. The control strategy of such disturbances, random, will be develooed to adant itto a wind farm connected to an electrical network.
基金co-supported by the National Key R&D Program of China(No.2023YFB4704400)the Zhejiang Provincial Natural Science Foundation of China(No.LQ24F030012)the National Natural Science Foundation of China General Project(No.62373033)。
文摘A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source disturbances are addressed according to their specific characteristics as follows:(A)an MTN data-driven model,which is used for uncertainty description,is designed accompanied with the mechanism model to represent the unmanned systems;(B)an adaptive MTN filter is used to remove the influence of the internal disturbance;(C)an MTN disturbance observer is constructed to estimate and compensate for the influence of the external disturbance;(D)the Extended Kalman Filter(EKF)algorithm is utilized as the learning mechanism for MTNs.Second,to address the time-delay effect,a recursiveτstep-ahead MTN predictive model is designed utilizing recursive technology,aiming to mitigate the impact of time-delay,and the EKF algorithm is employed as its learning mechanism.Then,the MTN predictive control law is designed based on the quadratic performance index.By implementing the proposed composite controller to unmanned systems,simultaneous feedforward compensation and feedback suppression to the multi-source disturbances are conducted.Finally,the convergence of the MTN and the stability of the closed-loop system are established utilizing the Lyapunov theorem.Two exemplary applications of unmanned systems involving unmanned vehicle and rigid spacecraft are presented to validate the effectiveness of the proposed approach.
文摘In this paper, a method of power quality disturbance classification based on random matrix theory (RMT) is proposed. The method utilizes the power quality disturbance signal to construct a random matrix. By analyzing the mean spectral radius (MSR) variation of the random matrix, the type and time of occurrence of power quality disturbance are classified. In this paper, the random matrix theory is used to analyze the voltage sag, swell and interrupt perturbation signals to classify the occurrence time, duration of the disturbance signal and thedepth of voltage sag or swell. Examples show that the method has strong anti-noise ability.
基金Under the auspices of National Natural Science Foundation of China(No.52079103)。
文摘Precise and timely prediction of crop yields is crucial for food security and the development of agricultural policies.However,crop yield is influenced by multiple factors within complex growth environments.Previous research has paid relatively little attention to the interference of environmental factors and drought on the growth of winter wheat.Therefore,there is an urgent need for more effective methods to explore the inherent relationship between these factors and crop yield,making precise yield prediction increasingly important.This study was based on four type of indicators including meteorological,crop growth status,environmental,and drought index,from October 2003 to June 2019 in Henan Province as the basic data for predicting winter wheat yield.Using the sparrow search al-gorithm combined with random forest(SSA-RF)under different input indicators,accuracy of winter wheat yield estimation was calcu-lated.The estimation accuracy of SSA-RF was compared with partial least squares regression(PLSR),extreme gradient boosting(XG-Boost),and random forest(RF)models.Finally,the determined optimal yield estimation method was used to predict winter wheat yield in three typical years.Following are the findings:1)the SSA-RF demonstrates superior performance in estimating winter wheat yield compared to other algorithms.The best yield estimation method is achieved by four types indicators’composition with SSA-RF)(R^(2)=0.805,RRMSE=9.9%.2)Crops growth status and environmental indicators play significant roles in wheat yield estimation,accounting for 46%and 22%of the yield importance among all indicators,respectively.3)Selecting indicators from October to April of the follow-ing year yielded the highest accuracy in winter wheat yield estimation,with an R^(2)of 0.826 and an RMSE of 9.0%.Yield estimates can be completed two months before the winter wheat harvest in June.4)The predicted performance will be slightly affected by severe drought.Compared with severe drought year(2011)(R^(2)=0.680)and normal year(2017)(R^(2)=0.790),the SSA-RF model has higher prediction accuracy for wet year(2018)(R^(2)=0.820).This study could provide an innovative approach for remote sensing estimation of winter wheat yield.yield.
基金supported by the Research Grants Council of the Hong Kong Special Administrative Region,China(416811,416812)National Natural Science Foundation of China(61573003)part by the Scientific Research Fund of Hunan Provincial Education Department of China(15k026)
基金supported by the National Nature Science Foundation of China(Grant Nos.12072264 and 12272296)the Key International(Regional)Joint Research Program of the National Science Foundation of China(Grant No.12120101002)+1 种基金the National Science Foundation of Chongqing,China(Grant No.cstc2021jcyj-msxm X0738)the National Science Foundation of Guangdong Province,China(Grant No.2023A1515012329)。
文摘This study focuses on exploring the complex dynamical behaviors of a magnetic microrobot in a random environment.The purpose is to reveal the mechanism of influence of random disturbance on microrobot dynamics.This paper establishes stochastic dynamic models for the microrobot before and after deformation,considering the influence of Gaussian white noise.The system responses are analyzed via steady-state probability density functions and first deformation time.The effects of different magnetic field strengths and fluid viscosities on the movement speed and angular velocity of the microrobot are studied.The results indicate that random disturbances can cause deformation of microrobots in advance compared to the deterministic case.This work contributes to the design and motion control of microrobots and enhances the theoretical foundation of microrobots.
基金Supported by the National Natural Science Foundation of China(42401488,42071351)the National Key Research and Development Program of China(2020YFA0608501,2017YFB0504204)+4 种基金the Liaoning Revitalization Talents Program(XLYC1802027)the Talent Recruited Program of the Chinese Academy of Science(Y938091)the Project Supported Discipline Innovation Team of the Liaoning Technical University(LNTU20TD-23)the Liaoning Province Doctoral Research Initiation Fund Program(2023-BS-202)the Basic Research Projects of Liaoning Department of Education(JYTQN2023202)。
文摘Accurate estimation of understory terrain has significant scientific importance for maintaining ecosystem balance and biodiversity conservation.Addressing the issue of inadequate representation of spatial heterogeneity when traditional forest topographic inversion methods consider the entire forest as the inversion unit,this study pro⁃poses a differentiated modeling approach to forest types based on refined land cover classification.Taking Puerto Ri⁃co and Maryland as study areas,a multi-dimensional feature system is constructed by integrating multi-source re⁃mote sensing data:ICESat-2 spaceborne LiDAR is used to obtain benchmark values for understory terrain,topo⁃graphic factors such as slope and aspect are extracted based on SRTM data,and vegetation cover characteristics are analyzed using Landsat-8 multispectral imagery.This study incorporates forest type as a classification modeling con⁃dition and applies the random forest algorithm to build differentiated topographic inversion models.Experimental re⁃sults indicate that,compared to traditional whole-area modeling methods(RMSE=5.06 m),forest type-based classi⁃fication modeling significantly improves the accuracy of understory terrain estimation(RMSE=2.94 m),validating the effectiveness of spatial heterogeneity modeling.Further sensitivity analysis reveals that canopy structure parame⁃ters(with RMSE variation reaching 4.11 m)exert a stronger regulatory effect on estimation accuracy compared to forest cover,providing important theoretical support for optimizing remote sensing models of forest topography.
