This paper presents a discrete-time model to describe the movements of a group of trains, in which some operational strategies, including traction operation, braking operation and impact of stochastic disturbance, are...This paper presents a discrete-time model to describe the movements of a group of trains, in which some operational strategies, including traction operation, braking operation and impact of stochastic disturbance, are defined. To show the dynamic characteristics of train traffic flow with stochastic disturbance, some numerical experiments on a railway line are simulated. The computational results show that the discrete-time movement model can well describe the movements of trains on a rail line with the moving-block signalling system. Comparing with the results of no disturbance, it finds that the traffic capacity of the rail line will decrease with the influence of stochastic disturbance. Additionally, the delays incurred by stochastic disturbance can be propagated to the subsequent trains, and then prolong their traversing time on the rail line. It can provide auxiliary information for rescheduling trains When the stochastic disturbance occurs on the railway.展开更多
For a class of discrete-time systems with unmodeled dynamics and bounded disturbance, the design and analysis of robust indirect model reference adaptive control （MRAC） with normalized adaptive law are investigated....For a class of discrete-time systems with unmodeled dynamics and bounded disturbance, the design and analysis of robust indirect model reference adaptive control （MRAC） with normalized adaptive law are investigated. The main work includes three parts. Firstly, it is shown that the constructed parameter estimation algorithm not only possesses the same properties as those of traditional estimation algorithms, but also avoids the possibility of division by zero. Secondly, by establishing a relationship between the plant parameter estimate and the controller parameter estimate, some similar properties of the latter are also established. Thirdly, by using the relationship between the normalizing signal and all the signals of the closed-loop system, and some important mathematical tools on discrete-time systems, as in the continuous-time case, a systematic stability and robustness analysis approach to the discrete indirect robust MRAC scheme is developed rigorously.展开更多
This paper aims to present a simulation model for heterogeneous high-speed train traffic flow based on an improved discrete-time model（IDTM）.In the proposed simulation model,four train control strategies,including d...This paper aims to present a simulation model for heterogeneous high-speed train traffic flow based on an improved discrete-time model（IDTM）.In the proposed simulation model,four train control strategies,including departing strategy,traveling strategy,braking strategy,overtaking strategy,are well defined to optimize train movements.Based on the proposed simulation model,some characteristics of train traffic flow are investigated.Numerical results indicate that the departure time intervals,the station dwell time,the section length,and the ratio of fast trains have different influence on traffic capacity and train average velocity.The results can provide some theoretical support for the strategy making of railway departments.展开更多
This paper proposes a new method to chaotify the discrete-time fuzzy hyperbolic model （DFHM） with uncertain parameters. A simple nonlinear state feedback controller is designed for this purpose. By revised Marotto t...This paper proposes a new method to chaotify the discrete-time fuzzy hyperbolic model （DFHM） with uncertain parameters. A simple nonlinear state feedback controller is designed for this purpose. By revised Marotto theorem, it is proven that the chaos generated by this controller satisfies the Li-Yorke definition. An example is presented to demonstrate the effectiveness of the approach.展开更多
In this paper, a new simulation approach for solving the mixed train scheduling problem on the high-speed double-track rail line is presented. Based on the discrete-time movement model, we propose control strategies f...In this paper, a new simulation approach for solving the mixed train scheduling problem on the high-speed double-track rail line is presented. Based on the discrete-time movement model, we propose control strategies for mixed train movement with different speeds on a high-speed double-track rail line, including braking strategy, priority rule, travelling strategy, and departing rule. A new detailed algorithm is also presented based on the proposed control strategies for mixed train movement. Moreover, we analyze the dynamic properties of rail traffic flow on a high-speed rail line. Using our proposed method, we can effectively simulate the mixed train schedule on a rail line. The numerical results demonstrate that an appropriate decrease of the departure interval can enhance the capacity, and a suitable increase of the distance between two adjacent stations can enhance the average speed. Meanwhile, the capacity and the average speed will be increased by appropriately enhancing the ratio of faster train number to slower train number from 1.展开更多
Community detection in signed networks has been studied widely in recent years. In this paper, a discrete difference equation is proposed to imitate the consistently changing phases of the nodes. During the interactio...Community detection in signed networks has been studied widely in recent years. In this paper, a discrete difference equation is proposed to imitate the consistently changing phases of the nodes. During the interaction, each node will update its phase based on the difference equation. Each node has many different nodes connected with it, and these neighbors have different influences on it. The similarity between two nodes is applied to describe the influences between them. Nodes with high positive similarities will get together and nodes with negative similarities will be far away from each other.Communities are detected ultimately when the phases of the nodes are stable. Experiments on real world and synthetic signed networks show the efficiency of detection performance. Moreover, the presented method gains better detection performance than two existing good algorithms.展开更多
For a large class of discrete-time multivariable plants with arbitrary relative degrees,the design and analysis of the direct model reference adaptive control scheme are investigated under less restrictive assumptions...For a large class of discrete-time multivariable plants with arbitrary relative degrees,the design and analysis of the direct model reference adaptive control scheme are investigated under less restrictive assumptions.The algorithm is based on a new parametrization derived from the high frequency gain matrix factorization Kp=LDU under the condition that the signs of the leading principal minors of/fp are known.By reproving the discrete-time Lp and L2σnorm relationship between inputs and outputs,establishing the properties of discrete-time adaptive law,defining the normalizing signal,and relating the signal with all signals in the closed-loop system,the stability and convergence of the discrete-time multivariable model reference adaptive control scheme are analyzed rigorously in a systematic fashion as in the continuous-time case.展开更多
Recently,an approach for the rapid detection of small oscillation faults based on deterministic learning theory was proposed for continuous-time systems.In this paper,a fault detection scheme is proposed for a class o...Recently,an approach for the rapid detection of small oscillation faults based on deterministic learning theory was proposed for continuous-time systems.In this paper,a fault detection scheme is proposed for a class of nonlinear discrete-time systems via deterministic learning.By using a discrete-time extension of deterministic learning algorithm,the general fault functions(i.e.,the internal dynamics)underlying normal and fault modes of nonlinear discrete-time systems are locally-accurately approximated by discrete-time dynamical radial basis function(RBF)networks.Then,a bank of estimators with the obtained knowledge of system dynamics embedded is constructed,and a set of residuals are obtained and used to measure the differences between the dynamics of the monitored system and the dynamics of the trained systems.A fault detection decision scheme is presented according to the smallest residual principle,i.e.,the occurrence of a fault can be detected in a discrete-time setting by comparing the magnitude of residuals.The fault detectability analysis is carried out and the upper bound of detection time is derived.A simulation example is given to illustrate the effectiveness of the proposed scheme.展开更多
This paper investigates a sliding-mode model predictive control （MPC） algorithm with auxiliary contractive sliding vector constraint for constrained nonlinear discrete-time systems. By adding contractive constraint ...This paper investigates a sliding-mode model predictive control （MPC） algorithm with auxiliary contractive sliding vector constraint for constrained nonlinear discrete-time systems. By adding contractive constraint into the optimization problem in regular sliding-mode MPC algorithm, the value of the sliding vector is decreased to zero asymptotically, which means that the system state is driven into a vicinity of sliding surface with a certain width. Then, the system state moves along the sliding surface to the equilibrium point within the vicinity. By applying the proposed algorithm, the stability of the closed-loop system is guaranteed. A numerical example of a continuous stirred tank reactor （CSTR） system is given to verify the feasibility and effectiveness of the proposed method.展开更多
The robust exponential stability of a larger class of discrete-time recurrent neural networks (RNNs) is explored in this paper. A novel neural network model, named standard neural network model (SNNM), is introduced t...The robust exponential stability of a larger class of discrete-time recurrent neural networks (RNNs) is explored in this paper. A novel neural network model, named standard neural network model (SNNM), is introduced to provide a general framework for stability analysis of RNNs. Most of the existing RNNs can be transformed into SNNMs to be analyzed in a unified way. Applying Lyapunov stability theory method and S-Procedure technique, two useful criteria of robust exponential stability for the discrete-time SNNMs are derived. The conditions presented are formulated as linear matrix inequalities (LMIs) to be easily solved using existing efficient convex optimization techniques. An example is presented to demonstrate the transformation procedure and the effectiveness of the results.展开更多
We propose a new approach for analyzing the global asymptotic stability of the extended discrete-time bidirectional associative memory （BAM） neural networks. By using the Euler rule, we discretize the continuous-tim...We propose a new approach for analyzing the global asymptotic stability of the extended discrete-time bidirectional associative memory （BAM） neural networks. By using the Euler rule, we discretize the continuous-time BAM neural networks as the extended discrete-time BAM neural networks with non-threshold activation functions. Here we present some conditions under which the neural networks have unique equilibrium points. To judge the global asymptotic stability of the equilibrium points, we introduce a new neural network model - standard neural network model （SNNM）. For the SNNMs, we derive the sufficient conditions for the global asymptotic stability of the equilibrium points, which are formulated as some linear matrix inequalities （LMIs）. We transform the discrete-time BAM into the SNNM and apply the general result about the SNNM to the determination of global asymptotic stability of the discrete-time BAM. The approach proposed extends the known stability results, has lower conservativeness, can be verified easily, and can also be applied to other forms of recurrent neural networks.展开更多
The objective of this paper is to present the advantages of Model reference adaptive control (MRAC) motion cueing algorithm against the classical motion cueing algorithm in terms of biomechanical reactions of the part...The objective of this paper is to present the advantages of Model reference adaptive control (MRAC) motion cueing algorithm against the classical motion cueing algorithm in terms of biomechanical reactions of the participants during the critical maneuvers like chicane in driving simulator real-time. This study proposes a method and an experimental validation to analyze the vestibular and neuromuscular dynamics responses of the drivers with respect to the type of the control used at the hexapod driving simulator. For each situation, the EMG (electromyography) data were registered from arm muscles of the drivers (flexor carpi radialis, brachioradialis). In addition, the roll velocity perception thresholds (RVT) and roll velocities (RV) were computed from the real-time vestibular level measurements from the drivers via a motion-tracking sensor. In order to process the data of the EMG and RVT, Pearson’s correlation and a two-way ANOVA with a significance level of 0.05 were assigned. Moreover, the relationships of arm muscle power and roll velocity with vehicle CG (center of gravity) lateral displacement were analyzed in order to assess the agility/alertness level of the drivers as well as the vehicle loss of control characteristics with a confidence interval of 95%. The results showed that the MRAC algorithm avoided the loss of adhesion, loss of control (LOA, LOC) more reasonably compared to the classical motion cueing algorithm. According to our findings, the LOA avoidance decreased the neuromuscular-visual cues level conflict with MRAC algorithm. It also revealed that the neuromuscular-vehicle dynamics conflict has influence on visuo-vestibular conflict;however, the visuo-vestibular cue conflict does not influence the neuromuscular-vehicle dynamics interactions.展开更多
In this study, we propose a novel discrete-time coupled model to generate oscillatory responses via periodic points with a high periodic order. Our coupled system comprises one-dimensional oscillators based on the Rul...In this study, we propose a novel discrete-time coupled model to generate oscillatory responses via periodic points with a high periodic order. Our coupled system comprises one-dimensional oscillators based on the Rulkov map and a single globally coupled oscillator. Because the waveform of a one-dimensional oscillator has sharply defined peaks, the coupled system can be applied to dynamic image segmentation. Our proposed system iteratively transforms the coupling of each oscillator based on an input value that corresponds to the pixel value of an input image. This approach enables our system to segment image regions in which pixel values gradually change with respect to a connected region. We conducted a bifurcation analysis of a single oscillator and a three-coupled model. Through simulations, we demonstrated that our system works well for gray-level images with three isolated image regions.展开更多
In the forward channel of a networked control system （NCS）, by defining the network states as a hidden Markov chain and quantizing the network-induced delays to a discrete sequence distributing over a finite time in...In the forward channel of a networked control system （NCS）, by defining the network states as a hidden Markov chain and quantizing the network-induced delays to a discrete sequence distributing over a finite time interval, the relation between the network states and the network-induced delays is modelled as a discrete-time hidden Markov model （DTHMM）. The expectation maximization （EM） algorithm is introduced to derive the maximumlikelihood estimation （MLE） of the parameters of the DTHMM. Based on the derived DTHMM, the Viterbi algorithm is introduced to predict the controller-to-actuator （C-A） delay during the current sampling period. The simulation experiments demonstrate the effectiveness of the modelling and predicting methods proposed.展开更多
This paper proposes an associative memory model based on a coupled system of Gaussian maps. A one-dimensional Gaussian map describes a discrete-time dynamical system, and the coupled system of Gaussian maps can genera...This paper proposes an associative memory model based on a coupled system of Gaussian maps. A one-dimensional Gaussian map describes a discrete-time dynamical system, and the coupled system of Gaussian maps can generate various phenomena including asymmetric fixed and periodic points. The Gaussian associative memory can effectively recall one of the stored patterns, which were triggered by an input pattern by associating the asymmetric two-periodic points observed in the coupled system with the binary values of output patterns. To investigate the Gaussian associative memory model, we formed its reduced model and analyzed the bifurcation structure. Pseudo-patterns were observed for the proposed model along with other conventional associative memory models, and the obtained patterns were related to the high-order or quasi-periodic points and the chaotic trajectories. In this paper, the structure of the Gaussian associative memory and its reduced models are introduced as well as the results of the bifurcation analysis are presented. Furthermore, the output sequences obtained from simulation of the recalling process are presented. We discuss the mechanism and the characteristics of the Gaussian associative memory based on the results of the analysis and the simulations conducted.展开更多
In this paper, assuming that there are s types of insurance contracts in an insurance company, we study the asymptotic of the finite-time ruin probability for the discrete-time multi-risk model.
A novel neural network model, termed the discrete-time delayed standard neural network model （DDSNNM）, and similar to the nominal model in linear robust control theory, is suggested to facilitate the stability analy...A novel neural network model, termed the discrete-time delayed standard neural network model （DDSNNM）, and similar to the nominal model in linear robust control theory, is suggested to facilitate the stability analysis of discrete-time recurrent neural networks （RNNs） and to ease the synthesis of controllers for discrete-time nonlinear systems. The model is composed of a discrete-time linear dynamic system and a bounded static delayed （or non-delayed） nonlinear operator. By combining various Lyapunov functionals with the S-procedure, sufficient conditions for the global asymptotic stability and global exponential stability of the DDSNNM are derived, which are formulated as linear or nonlinear matrix inequalities. Most discrete-time delayed or non-delayed RNNs, or discrete-time neural-network-based nonlinear control systems can be transformed into the DDSNNMs for stability analysis and controller synthesis in a unified way. Two application examples are given where the DDSNNMs are employed to analyze the stability of the discrete-time cellular neural networks （CNNs） and to synthesize the neuro-controllers for the discrete-time nonlinear systems, respectively. Through these examples, it is demonstrated that the DDSNNM not only makes the stability analysis of the RNNs much easier, but also provides a new approach to the synthesis of the controllers for the nonlinear systems.展开更多
First,we devise in this paper,a multi-regions discrete-time model which describes the spatial-temporal spread of an epidemic which starts from one region and enters to regions which are connected with their neighbors ...First,we devise in this paper,a multi-regions discrete-time model which describes the spatial-temporal spread of an epidemic which starts from one region and enters to regions which are connected with their neighbors by any kind of anthropological movement.We suppose homogeneous Susceptible-Infected-Removed(SIR)populations,and we consider in our simulations,a grid of colored cells,which represents the whole domain affected by the epidemic while each cell can represent a sub-domain or region.Second,in order to minimize the number of infected individuals in one region,we propose an optimal control approach based on a travel-blocking vicinity strategy which aims to control only one cell by restricting movements of infected people coming from all neighboring cells.Thus,we show the influence of the optimal control approach on the controlled cell.We should also note that the cellular modeling approach we propose here,can also describes infection dynamics of regions which are not necessarily attached one to an other,even if no empty space can be viewed between cells.The theoretical method we follow for the characterization of the travel-locking optimal controls,is based on a discrete version of Pontryagin's maximum principle while the numerical approach applied to the multi-points boundary value problems we obtain here,is based on discrete progressive-regressive iterative schemes.We illustrate our modeling and control approaches by giving an example of 100 regions.展开更多
Consider a discrete-time risk model with insurance and financial risks in a stochastic economic environment. Assume that the insurance and financial risks form a sequence of independent and identically distributed ran...Consider a discrete-time risk model with insurance and financial risks in a stochastic economic environment. Assume that the insurance and financial risks form a sequence of independent and identically distributed random vectors with a generic random vector following a wide type of dependence structure. An asymptotic formula for the finite-time ruin probability with subexponential insurance risks is derived. In doing so, the subexponentiality of the product of two dependent random variables is investigated simultaneously.展开更多
Efficient transient analysis is critical in rotor dynamics.This study proposes the super-element(SE)differential-quadraturediscrete-time transfer matrix method(DQ-DT-TMM),a novel approach that eliminates the requireme...Efficient transient analysis is critical in rotor dynamics.This study proposes the super-element(SE)differential-quadraturediscrete-time transfer matrix method(DQ-DT-TMM),a novel approach that eliminates the requirement for initial componentaccelerations and effectively handles beam and solid finite element(FE)models with high-dimensional degrees of freedom(DOFs)in rotor systems.The primary methodologies of this approach include:(1)For the beam substructure FE dynamicequation,the Craig-Bampton method is employed for the order reduction of internal coordinates,followed by the differentialquadraturemethod for temporal discretization.Using SE technology,the internal accelerations are condensed into theboundary accelerations,and the transfer equation and matrix for beam SEs are derived.(2)For the solid substructureFE dynamic equation formulated in the rotating reference frame,in addition to applying the procedures used for beamsubstructures,rigid multipoint constraints are introduced to condense the boundary coordinates for hybrid modeling withlumped parameter components.The transfer equation is subsequently formulated in the inertial reference frame,enabling thederivation of the transfer matrix for solid SEs.Comparative analysis with full-order FE models in commercial softwaredemonstrates the advantages of the SE DQ-DT-TMM for linear rotor systems:(i)Accurately captures system dynamics usingonly a few primary modes.(ii)Achieves a 99.68%reduction in computational time for a beam model with 1120 elements and a99.98%reduction for a solid model with 75361 elements.(iii)Effectively recovers dynamic responses at any system node usingrecovery techniques.This research develops a computationally efficient framework for the transient analysis of large-scale rotorsystems,effectively addressing the challenges associated with high-dimensional DOF models in conventional DT-TMMs.展开更多
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 70901006 and 60634010)the State Key Laboratory of Rail Traffic Control and Safety (Grant Nos. RCS2009ZT001 and RCS2008ZZ001)Beijing Jiaotong University, and the Innovation Foundation of Science and Technology for Excellent Doctorial Candidate of Beijing Jiaotong University (Grant No. 141034522)
文摘This paper presents a discrete-time model to describe the movements of a group of trains, in which some operational strategies, including traction operation, braking operation and impact of stochastic disturbance, are defined. To show the dynamic characteristics of train traffic flow with stochastic disturbance, some numerical experiments on a railway line are simulated. The computational results show that the discrete-time movement model can well describe the movements of trains on a rail line with the moving-block signalling system. Comparing with the results of no disturbance, it finds that the traffic capacity of the rail line will decrease with the influence of stochastic disturbance. Additionally, the delays incurred by stochastic disturbance can be propagated to the subsequent trains, and then prolong their traversing time on the rail line. It can provide auxiliary information for rescheduling trains When the stochastic disturbance occurs on the railway.
基金supported by National Natural Science Foundation of China (No. 60774010, 10971256, 60974028)Natural Science Foundation of Jiangsu Province (No. BK2009083)+2 种基金Program for Fundamental Research of Natural Sciences in Universities of Jiangsu Province(No. 07KJB510114)Shandong Provincial Natural Science Foundation of China (No. ZR2009GM008)Natural Science Foundation of Jining University (No. 2009KJLX02)
文摘For a class of discrete-time systems with unmodeled dynamics and bounded disturbance, the design and analysis of robust indirect model reference adaptive control （MRAC） with normalized adaptive law are investigated. The main work includes three parts. Firstly, it is shown that the constructed parameter estimation algorithm not only possesses the same properties as those of traditional estimation algorithms, but also avoids the possibility of division by zero. Secondly, by establishing a relationship between the plant parameter estimate and the controller parameter estimate, some similar properties of the latter are also established. Thirdly, by using the relationship between the normalizing signal and all the signals of the closed-loop system, and some important mathematical tools on discrete-time systems, as in the continuous-time case, a systematic stability and robustness analysis approach to the discrete indirect robust MRAC scheme is developed rigorously.
基金Supported by the National Basic Research Program of China under Grant No.2012CB725400the National Natural Science Foundation of China under Grant No.71222101+1 种基金the Research Foundation of State Key Laboratory of Rail Traffic Control and Safety under Grant No.RCS2014ZT16the Fundamental Research Funds for the Central Universities No.2015YJS088,Beijing Jiaotong University
文摘This paper aims to present a simulation model for heterogeneous high-speed train traffic flow based on an improved discrete-time model（IDTM）.In the proposed simulation model,four train control strategies,including departing strategy,traveling strategy,braking strategy,overtaking strategy,are well defined to optimize train movements.Based on the proposed simulation model,some characteristics of train traffic flow are investigated.Numerical results indicate that the departure time intervals,the station dwell time,the section length,and the ratio of fast trains have different influence on traffic capacity and train average velocity.The results can provide some theoretical support for the strategy making of railway departments.
基金Project supported by the National Natural Science Foundation of China (Grant Nos 60325311,60534010,60572070 and 60521003)the Program for Cheung Kong Scholars and Innovative Research Team in University (Grant No IRT0421)
文摘This paper proposes a new method to chaotify the discrete-time fuzzy hyperbolic model （DFHM） with uncertain parameters. A simple nonlinear state feedback controller is designed for this purpose. By revised Marotto theorem, it is proven that the chaos generated by this controller satisfies the Li-Yorke definition. An example is presented to demonstrate the effectiveness of the approach.
基金Project supported by the National Basic Research Program of China(Grant No.2012CB725400)the National Natural Science Foundation of China(Grant No.71131001-1)the Research Foundation of State Key Laboratory of Rail Traffic Control and Safety,Beijing Jiaotong University,China(Grant Nos.RCS2012ZZ001 and RCS2012ZT001)
文摘In this paper, a new simulation approach for solving the mixed train scheduling problem on the high-speed double-track rail line is presented. Based on the discrete-time movement model, we propose control strategies for mixed train movement with different speeds on a high-speed double-track rail line, including braking strategy, priority rule, travelling strategy, and departing rule. A new detailed algorithm is also presented based on the proposed control strategies for mixed train movement. Moreover, we analyze the dynamic properties of rail traffic flow on a high-speed rail line. Using our proposed method, we can effectively simulate the mixed train schedule on a rail line. The numerical results demonstrate that an appropriate decrease of the departure interval can enhance the capacity, and a suitable increase of the distance between two adjacent stations can enhance the average speed. Meanwhile, the capacity and the average speed will be increased by appropriately enhancing the ratio of faster train number to slower train number from 1.
基金supported by the National Natural Science Foundation of China(Grant Nos.11261034,71561020,61503203,and 11326239)the Higher School Science and Technology Research Project of Inner Mongolia,China(Grant No.NJZY13119)the Natural Science Foundation of Inner Mongolia,China(Grant Nos.2015MS0103 and 2014BS0105)
文摘Community detection in signed networks has been studied widely in recent years. In this paper, a discrete difference equation is proposed to imitate the consistently changing phases of the nodes. During the interaction, each node will update its phase based on the difference equation. Each node has many different nodes connected with it, and these neighbors have different influences on it. The similarity between two nodes is applied to describe the influences between them. Nodes with high positive similarities will get together and nodes with negative similarities will be far away from each other.Communities are detected ultimately when the phases of the nodes are stable. Experiments on real world and synthetic signed networks show the efficiency of detection performance. Moreover, the presented method gains better detection performance than two existing good algorithms.
基金Program for New Century Excellent Talents in Universities of China(No.NCET-05-0607)National Natural Science Foundation ofChina(No.60774010).
文摘For a large class of discrete-time multivariable plants with arbitrary relative degrees,the design and analysis of the direct model reference adaptive control scheme are investigated under less restrictive assumptions.The algorithm is based on a new parametrization derived from the high frequency gain matrix factorization Kp=LDU under the condition that the signs of the leading principal minors of/fp are known.By reproving the discrete-time Lp and L2σnorm relationship between inputs and outputs,establishing the properties of discrete-time adaptive law,defining the normalizing signal,and relating the signal with all signals in the closed-loop system,the stability and convergence of the discrete-time multivariable model reference adaptive control scheme are analyzed rigorously in a systematic fashion as in the continuous-time case.
基金This work was supported by the National Science Fund for Distinguished Young Scholars(No.61225014)the National Major Scientific Instruments Development Project(No.61527811)+2 种基金the National Natural Science Foundation of China(Nos.61304084,61374119)the Guangdong Natural Science Foundation(No.2014A030312005)the Space Intelligent Control Key Laboratory of Science and Technology for National Defense.
文摘Recently,an approach for the rapid detection of small oscillation faults based on deterministic learning theory was proposed for continuous-time systems.In this paper,a fault detection scheme is proposed for a class of nonlinear discrete-time systems via deterministic learning.By using a discrete-time extension of deterministic learning algorithm,the general fault functions(i.e.,the internal dynamics)underlying normal and fault modes of nonlinear discrete-time systems are locally-accurately approximated by discrete-time dynamical radial basis function(RBF)networks.Then,a bank of estimators with the obtained knowledge of system dynamics embedded is constructed,and a set of residuals are obtained and used to measure the differences between the dynamics of the monitored system and the dynamics of the trained systems.A fault detection decision scheme is presented according to the smallest residual principle,i.e.,the occurrence of a fault can be detected in a discrete-time setting by comparing the magnitude of residuals.The fault detectability analysis is carried out and the upper bound of detection time is derived.A simulation example is given to illustrate the effectiveness of the proposed scheme.
基金supported by Fundamental Research Funds for the Central Universities(Nos. CDJXS10170008 and CDJXS10171101)
文摘This paper investigates a sliding-mode model predictive control （MPC） algorithm with auxiliary contractive sliding vector constraint for constrained nonlinear discrete-time systems. By adding contractive constraint into the optimization problem in regular sliding-mode MPC algorithm, the value of the sliding vector is decreased to zero asymptotically, which means that the system state is driven into a vicinity of sliding surface with a certain width. Then, the system state moves along the sliding surface to the equilibrium point within the vicinity. By applying the proposed algorithm, the stability of the closed-loop system is guaranteed. A numerical example of a continuous stirred tank reactor （CSTR） system is given to verify the feasibility and effectiveness of the proposed method.
基金the National Natural Science Foundation of China (No. 60504024)the Research Project of Zhejiang Provin-cial Education Department (No. 20050905), China
文摘The robust exponential stability of a larger class of discrete-time recurrent neural networks (RNNs) is explored in this paper. A novel neural network model, named standard neural network model (SNNM), is introduced to provide a general framework for stability analysis of RNNs. Most of the existing RNNs can be transformed into SNNMs to be analyzed in a unified way. Applying Lyapunov stability theory method and S-Procedure technique, two useful criteria of robust exponential stability for the discrete-time SNNMs are derived. The conditions presented are formulated as linear matrix inequalities (LMIs) to be easily solved using existing efficient convex optimization techniques. An example is presented to demonstrate the transformation procedure and the effectiveness of the results.
基金This project was supported by the National Natural Science Foundation of China (60074008) .
文摘We propose a new approach for analyzing the global asymptotic stability of the extended discrete-time bidirectional associative memory （BAM） neural networks. By using the Euler rule, we discretize the continuous-time BAM neural networks as the extended discrete-time BAM neural networks with non-threshold activation functions. Here we present some conditions under which the neural networks have unique equilibrium points. To judge the global asymptotic stability of the equilibrium points, we introduce a new neural network model - standard neural network model （SNNM）. For the SNNMs, we derive the sufficient conditions for the global asymptotic stability of the equilibrium points, which are formulated as some linear matrix inequalities （LMIs）. We transform the discrete-time BAM into the SNNM and apply the general result about the SNNM to the determination of global asymptotic stability of the discrete-time BAM. The approach proposed extends the known stability results, has lower conservativeness, can be verified easily, and can also be applied to other forms of recurrent neural networks.
文摘The objective of this paper is to present the advantages of Model reference adaptive control (MRAC) motion cueing algorithm against the classical motion cueing algorithm in terms of biomechanical reactions of the participants during the critical maneuvers like chicane in driving simulator real-time. This study proposes a method and an experimental validation to analyze the vestibular and neuromuscular dynamics responses of the drivers with respect to the type of the control used at the hexapod driving simulator. For each situation, the EMG (electromyography) data were registered from arm muscles of the drivers (flexor carpi radialis, brachioradialis). In addition, the roll velocity perception thresholds (RVT) and roll velocities (RV) were computed from the real-time vestibular level measurements from the drivers via a motion-tracking sensor. In order to process the data of the EMG and RVT, Pearson’s correlation and a two-way ANOVA with a significance level of 0.05 were assigned. Moreover, the relationships of arm muscle power and roll velocity with vehicle CG (center of gravity) lateral displacement were analyzed in order to assess the agility/alertness level of the drivers as well as the vehicle loss of control characteristics with a confidence interval of 95%. The results showed that the MRAC algorithm avoided the loss of adhesion, loss of control (LOA, LOC) more reasonably compared to the classical motion cueing algorithm. According to our findings, the LOA avoidance decreased the neuromuscular-visual cues level conflict with MRAC algorithm. It also revealed that the neuromuscular-vehicle dynamics conflict has influence on visuo-vestibular conflict;however, the visuo-vestibular cue conflict does not influence the neuromuscular-vehicle dynamics interactions.
文摘In this study, we propose a novel discrete-time coupled model to generate oscillatory responses via periodic points with a high periodic order. Our coupled system comprises one-dimensional oscillators based on the Rulkov map and a single globally coupled oscillator. Because the waveform of a one-dimensional oscillator has sharply defined peaks, the coupled system can be applied to dynamic image segmentation. Our proposed system iteratively transforms the coupling of each oscillator based on an input value that corresponds to the pixel value of an input image. This approach enables our system to segment image regions in which pixel values gradually change with respect to a connected region. We conducted a bifurcation analysis of a single oscillator and a three-coupled model. Through simulations, we demonstrated that our system works well for gray-level images with three isolated image regions.
基金supported in part by the National Natural Science Foundation of China (60774098 60843003+3 种基金 50905172)the Science Foundation of Anhui Province (090412071 090412040)the University of Science and Technology of China Initiative Foundation
文摘In the forward channel of a networked control system （NCS）, by defining the network states as a hidden Markov chain and quantizing the network-induced delays to a discrete sequence distributing over a finite time interval, the relation between the network states and the network-induced delays is modelled as a discrete-time hidden Markov model （DTHMM）. The expectation maximization （EM） algorithm is introduced to derive the maximumlikelihood estimation （MLE） of the parameters of the DTHMM. Based on the derived DTHMM, the Viterbi algorithm is introduced to predict the controller-to-actuator （C-A） delay during the current sampling period. The simulation experiments demonstrate the effectiveness of the modelling and predicting methods proposed.
文摘This paper proposes an associative memory model based on a coupled system of Gaussian maps. A one-dimensional Gaussian map describes a discrete-time dynamical system, and the coupled system of Gaussian maps can generate various phenomena including asymmetric fixed and periodic points. The Gaussian associative memory can effectively recall one of the stored patterns, which were triggered by an input pattern by associating the asymmetric two-periodic points observed in the coupled system with the binary values of output patterns. To investigate the Gaussian associative memory model, we formed its reduced model and analyzed the bifurcation structure. Pseudo-patterns were observed for the proposed model along with other conventional associative memory models, and the obtained patterns were related to the high-order or quasi-periodic points and the chaotic trajectories. In this paper, the structure of the Gaussian associative memory and its reduced models are introduced as well as the results of the bifurcation analysis are presented. Furthermore, the output sequences obtained from simulation of the recalling process are presented. We discuss the mechanism and the characteristics of the Gaussian associative memory based on the results of the analysis and the simulations conducted.
基金Supported by the National Natural Science Foundation of China(Grant Nos 111010611137107761175041)
文摘In this paper, assuming that there are s types of insurance contracts in an insurance company, we study the asymptotic of the finite-time ruin probability for the discrete-time multi-risk model.
基金This work was supported by the National Natural Science Foundation of China （Grant No. 60504024） the Research Project of Zhejiang Provincial Education Department （Grant No. 20050905）.
文摘A novel neural network model, termed the discrete-time delayed standard neural network model （DDSNNM）, and similar to the nominal model in linear robust control theory, is suggested to facilitate the stability analysis of discrete-time recurrent neural networks （RNNs） and to ease the synthesis of controllers for discrete-time nonlinear systems. The model is composed of a discrete-time linear dynamic system and a bounded static delayed （or non-delayed） nonlinear operator. By combining various Lyapunov functionals with the S-procedure, sufficient conditions for the global asymptotic stability and global exponential stability of the DDSNNM are derived, which are formulated as linear or nonlinear matrix inequalities. Most discrete-time delayed or non-delayed RNNs, or discrete-time neural-network-based nonlinear control systems can be transformed into the DDSNNMs for stability analysis and controller synthesis in a unified way. Two application examples are given where the DDSNNMs are employed to analyze the stability of the discrete-time cellular neural networks （CNNs） and to synthesize the neuro-controllers for the discrete-time nonlinear systems, respectively. Through these examples, it is demonstrated that the DDSNNM not only makes the stability analysis of the RNNs much easier, but also provides a new approach to the synthesis of the controllers for the nonlinear systems.
基金This work is supported by the Systems Theory Network(Reseau Theorie des Systemes),and Hassan II Academy of Sciences and Technologies-Morocco.
文摘First,we devise in this paper,a multi-regions discrete-time model which describes the spatial-temporal spread of an epidemic which starts from one region and enters to regions which are connected with their neighbors by any kind of anthropological movement.We suppose homogeneous Susceptible-Infected-Removed(SIR)populations,and we consider in our simulations,a grid of colored cells,which represents the whole domain affected by the epidemic while each cell can represent a sub-domain or region.Second,in order to minimize the number of infected individuals in one region,we propose an optimal control approach based on a travel-blocking vicinity strategy which aims to control only one cell by restricting movements of infected people coming from all neighboring cells.Thus,we show the influence of the optimal control approach on the controlled cell.We should also note that the cellular modeling approach we propose here,can also describes infection dynamics of regions which are not necessarily attached one to an other,even if no empty space can be viewed between cells.The theoretical method we follow for the characterization of the travel-locking optimal controls,is based on a discrete version of Pontryagin's maximum principle while the numerical approach applied to the multi-points boundary value problems we obtain here,is based on discrete progressive-regressive iterative schemes.We illustrate our modeling and control approaches by giving an example of 100 regions.
基金Supported in part by the Natural National Science Foundation of China under Grant No.11671012the Natural Science Foundation of Anhui Province under Grant No.1808085MA16the Provincial Natural Science Research Project of Anhui Colleges under Grant No.KJ2017A024 and KJ2017A028
文摘Consider a discrete-time risk model with insurance and financial risks in a stochastic economic environment. Assume that the insurance and financial risks form a sequence of independent and identically distributed random vectors with a generic random vector following a wide type of dependence structure. An asymptotic formula for the finite-time ruin probability with subexponential insurance risks is derived. In doing so, the subexponentiality of the product of two dependent random variables is investigated simultaneously.
基金supported by the National Natural Science Foundation of China(Grant/Award No.92266201).
文摘Efficient transient analysis is critical in rotor dynamics.This study proposes the super-element(SE)differential-quadraturediscrete-time transfer matrix method(DQ-DT-TMM),a novel approach that eliminates the requirement for initial componentaccelerations and effectively handles beam and solid finite element(FE)models with high-dimensional degrees of freedom(DOFs)in rotor systems.The primary methodologies of this approach include:(1)For the beam substructure FE dynamicequation,the Craig-Bampton method is employed for the order reduction of internal coordinates,followed by the differentialquadraturemethod for temporal discretization.Using SE technology,the internal accelerations are condensed into theboundary accelerations,and the transfer equation and matrix for beam SEs are derived.(2)For the solid substructureFE dynamic equation formulated in the rotating reference frame,in addition to applying the procedures used for beamsubstructures,rigid multipoint constraints are introduced to condense the boundary coordinates for hybrid modeling withlumped parameter components.The transfer equation is subsequently formulated in the inertial reference frame,enabling thederivation of the transfer matrix for solid SEs.Comparative analysis with full-order FE models in commercial softwaredemonstrates the advantages of the SE DQ-DT-TMM for linear rotor systems:(i)Accurately captures system dynamics usingonly a few primary modes.(ii)Achieves a 99.68%reduction in computational time for a beam model with 1120 elements and a99.98%reduction for a solid model with 75361 elements.(iii)Effectively recovers dynamic responses at any system node usingrecovery techniques.This research develops a computationally efficient framework for the transient analysis of large-scale rotorsystems,effectively addressing the challenges associated with high-dimensional DOF models in conventional DT-TMMs.
