In this paper,a new recursive least squares(RLS)identification algorithm with variable-direction forgetting(VDF)is proposed for multi-output systems.The objective is to enhance parameter estimation performance under n...In this paper,a new recursive least squares(RLS)identification algorithm with variable-direction forgetting(VDF)is proposed for multi-output systems.The objective is to enhance parameter estimation performance under non-persistent excitation.The proposed algorithm performs oblique projection decomposition of the information matrix,such that forgetting is applied only to directions where new information is received.Theoretical proofs show that even without persistent excitation,the information matrix remains lower and upper bounded,and the estimation error variance converges to be within a finite bound.Moreover,detailed analysis is made to compare with a recently reported VDF algorithm that exploits eigenvalue decomposition(VDF-ED).It is revealed that under non-persistent excitation,part of the forgotten subspace in the VDF-ED algorithm could discount old information without receiving new data,which could produce a more ill-conditioned information matrix than our proposed algorithm.Numerical simulation results demonstrate the efficacy and advantage of our proposed algorithm over this recent VDF-ED algorithm.展开更多
This paper studies the parameter identification problem of chaotic systems. Adaptive identification laws are pro- posed to estimate the parameters of uncertain chaotic systems. It proves that the asymptotical identifi...This paper studies the parameter identification problem of chaotic systems. Adaptive identification laws are pro- posed to estimate the parameters of uncertain chaotic systems. It proves that the asymptotical identification is ensured by a persistently exciting condition. Additionally, the method can be applied to identify the uncertain parameters with any number. Numerical simulations are given to validate the theoretical analysis.展开更多
基金Supported by National Natural Science Foundation of China （60805039） and Nanjing University of Posts and Telecommunications r Introduced Talents （NY207028） The author would like to thank Professor Tian Yu-Ping. for fruitful discussions and his helpful remarks that improved the presentation of the paper.
文摘追踪 nonholonomic 的控制问题的输出锁住形式系统在这份报纸被学习，没有坚持的刺激，全球指数的输出追踪者被介绍或对零不收敛在参考书轨道上。首先，变化时间的并列转变被介绍避免操作指数地收敛的信号。在串联系统和线性使不安的系统的理论的帮助下，然后，全球指数的产量追踪者成功地被获得。没有坚持的刺激的流行条件，建议控制器的新特征是追踪 nonholonomic 的控制问题的输出锁住形式系统的 A 也是可溶解的在以前的工作在引用信号上收敛到零。建议方法与一辆大蓬车借助于 nonholonomic 被示威并且讨论活动机器人和汽车。
基金supported by the National Natural Science Foundation of China(61803163,61991414,61873301)。
文摘In this paper,a new recursive least squares(RLS)identification algorithm with variable-direction forgetting(VDF)is proposed for multi-output systems.The objective is to enhance parameter estimation performance under non-persistent excitation.The proposed algorithm performs oblique projection decomposition of the information matrix,such that forgetting is applied only to directions where new information is received.Theoretical proofs show that even without persistent excitation,the information matrix remains lower and upper bounded,and the estimation error variance converges to be within a finite bound.Moreover,detailed analysis is made to compare with a recently reported VDF algorithm that exploits eigenvalue decomposition(VDF-ED).It is revealed that under non-persistent excitation,part of the forgotten subspace in the VDF-ED algorithm could discount old information without receiving new data,which could produce a more ill-conditioned information matrix than our proposed algorithm.Numerical simulation results demonstrate the efficacy and advantage of our proposed algorithm over this recent VDF-ED algorithm.
基金Project supported in part by National Natural Science Foundation of China (Grant Nos. 11047114 and 60974081)in part by the Key Project of Chinese Ministry of Education (Grant No. 210141)
文摘This paper studies the parameter identification problem of chaotic systems. Adaptive identification laws are pro- posed to estimate the parameters of uncertain chaotic systems. It proves that the asymptotical identification is ensured by a persistently exciting condition. Additionally, the method can be applied to identify the uncertain parameters with any number. Numerical simulations are given to validate the theoretical analysis.
