In some practical applications modeled by discrete-event systems(DES),the observations of events may be no longer deterministic due to sensor faults/failures,packet loss,and/or measurement uncertainties.In this contex...In some practical applications modeled by discrete-event systems(DES),the observations of events may be no longer deterministic due to sensor faults/failures,packet loss,and/or measurement uncertainties.In this context,it is interesting to reconsider the infinite-step opacity(∞-SO)and K-step opacity(K-SO)of a DES under abnormal conditions as mentioned.In this paper,the authors extend the notions of∞-SO and K-SO defined in the standard setting to the framework of nondeterministic observations(i.e.,the event-observation mechanism is state-dependent and nondeterministic).Obviously,the extended notions of∞-SO and K-SO are more general than the previous standard ones.To effectively verify them,a matrix-based current state estimator in the context of this advanced framework is constructed using the Boolean semi-tensor product(BSTP)technique.Accordingly,the necessary and sufficient conditions for verifying these two extended versions of opacity are provided as well as their complexity analysis.Finally,several examples are given to illustrate the obtained theoretical results.展开更多
Adaptive cluster sampling （ACS） has been widely used for data collection of environment and natural resources. However, the randomness of its final sample size often impedes the use of this method. To control the fi...Adaptive cluster sampling （ACS） has been widely used for data collection of environment and natural resources. However, the randomness of its final sample size often impedes the use of this method. To control the final sample sizes, in this study, a k-step ACS based on Horvitz-Thompson （HT） estimator was developed and an unbiased estimator was derived. The k-step ACS-HT was assessed first using a simulated example and then using a real survey for numbers of plants for three species that were characterized by clustered and patchily spatial distributions. The effectiveness of this sampling design method was assessed in comparison with ACS Hansen-Hurwitz （ACS-HH） and ACS- HT estimators, and k-step ACS-HT estimator. The effectiveness of using different k- step sizes was also compared. The results showed that k-step ACS^HT estimator was most effective and ACS-HH was the least. Moreover, stable sample mean and variance estimates could be obtained after a certain number of steps, but depending on plant species, k-step ACS without replacement was slightly more effective than that with replacement. In k-step ACS, the variance estimate of one-step ACS is much larger than other k-step ACS （k 〉 1）, but it is smaller than ACS. This implies that k-step ACS is more effective than traditional ACS, besides, the final sample size can be controlled easily in population with big clusters.展开更多
本文针对多频窄带未知和时变扰动,基于内模原理和Y-K参数化方法,提出一种反馈鲁棒自适应振动的主动控制算法。该算法通过设计PID中央鲁棒控制器,有效解决了次级通道模型未知情况下的鲁棒控制器参数设计问题。同时提出一种变步长最小均方...本文针对多频窄带未知和时变扰动,基于内模原理和Y-K参数化方法,提出一种反馈鲁棒自适应振动的主动控制算法。该算法通过设计PID中央鲁棒控制器,有效解决了次级通道模型未知情况下的鲁棒控制器参数设计问题。同时提出一种变步长最小均方(Variable Step Size Least Mean Square,VSSLMS)方法,可以在保证稳态误差的基础上大幅提升收敛速度,并通过系统辨识实验验证了所提VSSLMS方法相较于其他VSSLMS算法在收敛性能上的优越性。通过结构微振动主动控制实时实验,对比验证了单独采用滤波x最小均方(Least Mean Square,LMS)自适应控制算法、基于LMS算法的鲁棒自适应控制算法和基于VSSLMS算法的鲁棒自适应控制算法的抑振效果。实验结果表明,本文基于VSSLMS算法的鲁棒自适应控制算法在面向双频正弦窄带扰动以及其频谱、幅值突变情况时,都具有较好的收敛性和鲁棒性。展开更多
基金supported by the National Natural Science Foundation of China under Grant Nos.61903274,61873342,61973175the Tianjin Natural Science Foundation of China under Grant No.18JCQNJC74000。
文摘In some practical applications modeled by discrete-event systems(DES),the observations of events may be no longer deterministic due to sensor faults/failures,packet loss,and/or measurement uncertainties.In this context,it is interesting to reconsider the infinite-step opacity(∞-SO)and K-step opacity(K-SO)of a DES under abnormal conditions as mentioned.In this paper,the authors extend the notions of∞-SO and K-SO defined in the standard setting to the framework of nondeterministic observations(i.e.,the event-observation mechanism is state-dependent and nondeterministic).Obviously,the extended notions of∞-SO and K-SO are more general than the previous standard ones.To effectively verify them,a matrix-based current state estimator in the context of this advanced framework is constructed using the Boolean semi-tensor product(BSTP)technique.Accordingly,the necessary and sufficient conditions for verifying these two extended versions of opacity are provided as well as their complexity analysis.Finally,several examples are given to illustrate the obtained theoretical results.
文摘Adaptive cluster sampling （ACS） has been widely used for data collection of environment and natural resources. However, the randomness of its final sample size often impedes the use of this method. To control the final sample sizes, in this study, a k-step ACS based on Horvitz-Thompson （HT） estimator was developed and an unbiased estimator was derived. The k-step ACS-HT was assessed first using a simulated example and then using a real survey for numbers of plants for three species that were characterized by clustered and patchily spatial distributions. The effectiveness of this sampling design method was assessed in comparison with ACS Hansen-Hurwitz （ACS-HH） and ACS- HT estimators, and k-step ACS-HT estimator. The effectiveness of using different k- step sizes was also compared. The results showed that k-step ACS^HT estimator was most effective and ACS-HH was the least. Moreover, stable sample mean and variance estimates could be obtained after a certain number of steps, but depending on plant species, k-step ACS without replacement was slightly more effective than that with replacement. In k-step ACS, the variance estimate of one-step ACS is much larger than other k-step ACS （k 〉 1）, but it is smaller than ACS. This implies that k-step ACS is more effective than traditional ACS, besides, the final sample size can be controlled easily in population with big clusters.
文摘本文针对多频窄带未知和时变扰动,基于内模原理和Y-K参数化方法,提出一种反馈鲁棒自适应振动的主动控制算法。该算法通过设计PID中央鲁棒控制器,有效解决了次级通道模型未知情况下的鲁棒控制器参数设计问题。同时提出一种变步长最小均方(Variable Step Size Least Mean Square,VSSLMS)方法,可以在保证稳态误差的基础上大幅提升收敛速度,并通过系统辨识实验验证了所提VSSLMS方法相较于其他VSSLMS算法在收敛性能上的优越性。通过结构微振动主动控制实时实验,对比验证了单独采用滤波x最小均方(Least Mean Square,LMS)自适应控制算法、基于LMS算法的鲁棒自适应控制算法和基于VSSLMS算法的鲁棒自适应控制算法的抑振效果。实验结果表明,本文基于VSSLMS算法的鲁棒自适应控制算法在面向双频正弦窄带扰动以及其频谱、幅值突变情况时,都具有较好的收敛性和鲁棒性。
基金国家自然科学基金(the National Natural Science Foundation of China under Grant No.5027150)湖南省教育厅一般项目(the Common Project of Bureau of Education of Hunan Province under Grant No.05C410)
