In this paper, two approaches are developed for directly identifying single-rate models of dual-rate stochastic systems in which the input updating frequency is an integer multiple of the output sampling frequency. Th...In this paper, two approaches are developed for directly identifying single-rate models of dual-rate stochastic systems in which the input updating frequency is an integer multiple of the output sampling frequency. The first is the generalized Yule-Walker algorithm and the second is a two-stage algorithm based on the correlation technique. The basic idea is to directly identify the parameters of underlying single-rate models instead of the lifted models of dual-rate systems from the dual-rate input-output data, assuming that the measurement data are stationary and ergodic. An example is given.展开更多
In this paper,H∞state feedback control with delay information for discrete systems with multi-time-delay is discussed.Making use of linear matrix inequality(LMI)approach,a time-delay-dependent criterion for a discret...In this paper,H∞state feedback control with delay information for discrete systems with multi-time-delay is discussed.Making use of linear matrix inequality(LMI)approach,a time-delay-dependent criterion for a discrete system with multi-time-delay to satisfy H∞performance indices is induced,and then a strategy for H∞state feedback control with delay values for plant with multi-time-delay is obtained.By solving corresponding LMI,a delay-dependent state feedback controller satisfying H∞performance indices is designed.Finally,a simulation example demonstrates the validity of the proposed approach.Keywords Multi-time-delay-discrete time system-LMI-delay-dependent-H∞control Bai-Da Qu received B.S.degree in electrical automation from Fuxin Mining Institute,China in 1982,M.Eng.degree from Hefei University of Polytechnology in 1990,and Ph.D from Northerneastern University in 1999.He was an electro-mechanical engineer at Erdaohezi Mine,Heilongjiang,China from 1982 to 1990,a Lecturer,Senior Engineer,Associate Professor and Professor in Shenyang Institue of Technology from 1990 to 2002.He is currently a professor in Communication and Control Engineering School,Southern Yangtze University.His research interests include control theory and applications(robust control,H∞control,time-delay systems,complex systems),system engineering(modeling,analysis and simulation,MIS,CMIS),power-electronics and electrical driving,signal detecting and process,industrial automation.展开更多
The research field of legged robots has always relied on the bionic robotic research,especially in locomotion regulating approaches,such as foot trajectory planning,body stability regulating and energy efficiency prom...The research field of legged robots has always relied on the bionic robotic research,especially in locomotion regulating approaches,such as foot trajectory planning,body stability regulating and energy efficiency prompting.Minimizing energy consumption and keeping the stability of body are considered as two main characteristics of human walking.This work devotes to develop an energy-efficient gait control method for electrical quadruped robots with the inspiration of human walking pattern.Based on the mechanical power distribution trend,an efficient humanoid power redistribution approach is established for the electrical quadruped robot.Through studying the walking behavior acted by mankind,such as the foot trajectory and change of mechanical power,we believe that the proposed controller which includes the bionic foot movement trajectory and humanoid power redistribution method can be implemented on the electrical quadruped robot prototype.The stability and energy efficiency of the proposed controller are tested by the simulation and the single-leg prototype experiment.The results verify that the humanoid power planning approach can improve the energy efficiency of the electrical quadruped robots.展开更多
In this paper, a synchronization scheme for a class of chaotic neural networks with time-varying delays is presented. This class of chaotic neural networks covers several well-known neural networks, such as Hopfield n...In this paper, a synchronization scheme for a class of chaotic neural networks with time-varying delays is presented. This class of chaotic neural networks covers several well-known neural networks, such as Hopfield neural networks, cellular neural networks, and bidirectional associative memory networks. The obtained criteria are expressed in terms of linear matrix inequalities, thus they can be efficiently verified. A comparison between our results and the previous results shows that our results are less restrictive.展开更多
By using the stochastic martingale theory, convergence properties of stochastic gradient （SG） identification algorithms are studied under weak conditions. The analysis indicates that the parameter estimates by the S...By using the stochastic martingale theory, convergence properties of stochastic gradient （SG） identification algorithms are studied under weak conditions. The analysis indicates that the parameter estimates by the SG algorithms consistently converge to the true parameters, as long as the information vector is persistently exciting （i.e., the data product moment matrix has a bounded condition number） and that the process noises are zero mean and uncorrelated. These results remove the strict assumptions, made in existing references, that the noise variances and high-order moments exist, and the processes are stationary and ergodic and the strong persis- tent excitation condition holds. This contribution greatly relaxes the convergence conditions of stochastic gradient algorithms. The simulation results with bounded and unbounded noise variances confirm the convergence conclusions proposed.展开更多
基金This work was supported by the National Natural Science Foundation of China (No. 60574051).
文摘In this paper, two approaches are developed for directly identifying single-rate models of dual-rate stochastic systems in which the input updating frequency is an integer multiple of the output sampling frequency. The first is the generalized Yule-Walker algorithm and the second is a two-stage algorithm based on the correlation technique. The basic idea is to directly identify the parameters of underlying single-rate models instead of the lifted models of dual-rate systems from the dual-rate input-output data, assuming that the measurement data are stationary and ergodic. An example is given.
文摘In this paper,H∞state feedback control with delay information for discrete systems with multi-time-delay is discussed.Making use of linear matrix inequality(LMI)approach,a time-delay-dependent criterion for a discrete system with multi-time-delay to satisfy H∞performance indices is induced,and then a strategy for H∞state feedback control with delay values for plant with multi-time-delay is obtained.By solving corresponding LMI,a delay-dependent state feedback controller satisfying H∞performance indices is designed.Finally,a simulation example demonstrates the validity of the proposed approach.Keywords Multi-time-delay-discrete time system-LMI-delay-dependent-H∞control Bai-Da Qu received B.S.degree in electrical automation from Fuxin Mining Institute,China in 1982,M.Eng.degree from Hefei University of Polytechnology in 1990,and Ph.D from Northerneastern University in 1999.He was an electro-mechanical engineer at Erdaohezi Mine,Heilongjiang,China from 1982 to 1990,a Lecturer,Senior Engineer,Associate Professor and Professor in Shenyang Institue of Technology from 1990 to 2002.He is currently a professor in Communication and Control Engineering School,Southern Yangtze University.His research interests include control theory and applications(robust control,H∞control,time-delay systems,complex systems),system engineering(modeling,analysis and simulation,MIS,CMIS),power-electronics and electrical driving,signal detecting and process,industrial automation.
基金supported in part by the National Natural Science Foundation of China(Grant nos.61973191,91948201)Lelai Zhou acknowledges the support by the Young Scholars Program of Shandong University(YSPSDU).
文摘The research field of legged robots has always relied on the bionic robotic research,especially in locomotion regulating approaches,such as foot trajectory planning,body stability regulating and energy efficiency prompting.Minimizing energy consumption and keeping the stability of body are considered as two main characteristics of human walking.This work devotes to develop an energy-efficient gait control method for electrical quadruped robots with the inspiration of human walking pattern.Based on the mechanical power distribution trend,an efficient humanoid power redistribution approach is established for the electrical quadruped robot.Through studying the walking behavior acted by mankind,such as the foot trajectory and change of mechanical power,we believe that the proposed controller which includes the bionic foot movement trajectory and humanoid power redistribution method can be implemented on the electrical quadruped robot prototype.The stability and energy efficiency of the proposed controller are tested by the simulation and the single-leg prototype experiment.The results verify that the humanoid power planning approach can improve the energy efficiency of the electrical quadruped robots.
基金Project supported by the National Natural Science Foundation of China （Grant No 60674026）, the Science Foundation of Southern Yangtze University, China.
文摘In this paper, a synchronization scheme for a class of chaotic neural networks with time-varying delays is presented. This class of chaotic neural networks covers several well-known neural networks, such as Hopfield neural networks, cellular neural networks, and bidirectional associative memory networks. The obtained criteria are expressed in terms of linear matrix inequalities, thus they can be efficiently verified. A comparison between our results and the previous results shows that our results are less restrictive.
基金Supported by the National Natural Science Foundation of China (Grant Nos. 60574051 and 60674092) the Natural Science Foundation of Jiangsu Province, China (Grant No. BK2007017) and by Program for Innovative Research Team of Jiangnan University
文摘By using the stochastic martingale theory, convergence properties of stochastic gradient （SG） identification algorithms are studied under weak conditions. The analysis indicates that the parameter estimates by the SG algorithms consistently converge to the true parameters, as long as the information vector is persistently exciting （i.e., the data product moment matrix has a bounded condition number） and that the process noises are zero mean and uncorrelated. These results remove the strict assumptions, made in existing references, that the noise variances and high-order moments exist, and the processes are stationary and ergodic and the strong persis- tent excitation condition holds. This contribution greatly relaxes the convergence conditions of stochastic gradient algorithms. The simulation results with bounded and unbounded noise variances confirm the convergence conclusions proposed.
