Aiming at the robustness issue in high-speed trains(HSTs)operation control,this article proposes a model-free adaptive control(MFAC)scheme to suppress disturbance.Firstly,the dynamic linearization data model of train ...Aiming at the robustness issue in high-speed trains(HSTs)operation control,this article proposes a model-free adaptive control(MFAC)scheme to suppress disturbance.Firstly,the dynamic linearization data model of train system under the action of measurement disturbance is given,and the Kalman filter(KF)based on this model is derived under the minimum variance estimation criterion.Then,according to the KF,an anti-interference MFAC scheme is designed.This scheme only needs the input and output data of the controlled system to realize the MFAC of the train under strong disturbance.Finally,the simulation experiment of CRH380A HSTs is carried out and compared with the traditional MFAC and the MFAC with attenuation factor.The proposed control algorithm can effectively suppress the measurement disturbance,and obtain smaller tracking error and larger signal to noise ratio with better applicability.展开更多
This article presents an up-to-date tutorial review of nonlinear Bayesian estimation. State estimation for nonlinear systems has been a challenge encountered in a wide range of engineering fields, attracting decades o...This article presents an up-to-date tutorial review of nonlinear Bayesian estimation. State estimation for nonlinear systems has been a challenge encountered in a wide range of engineering fields, attracting decades of research effort. To date,one of the most promising and popular approaches is to view and address the problem from a Bayesian probabilistic perspective,which enables estimation of the unknown state variables by tracking their probabilistic distribution or statistics(e.g., mean and covariance) conditioned on a system's measurement data.This article offers a systematic introduction to the Bayesian state estimation framework and reviews various Kalman filtering(KF)techniques, progressively from the standard KF for linear systems to extended KF, unscented KF and ensemble KF for nonlinear systems. It also overviews other prominent or emerging Bayesian estimation methods including Gaussian filtering, Gaussian-sum filtering, particle filtering and moving horizon estimation and extends the discussion of state estimation to more complicated problems such as simultaneous state and parameter/input estimation.展开更多
To deal with the adverse influence of model failures on Kalman filtering （KF） estimation, it is necessary to investigate the generalized reliability theory, including the model failure detection and identification m...To deal with the adverse influence of model failures on Kalman filtering （KF） estimation, it is necessary to investigate the generalized reliability theory, including the model failure detection and identification method as well as the separability and reliability theories. Although the generalized reliability theory for the least square has been discussed for many decades, the generalized reliability theory of KF is not widely discussed. Compared with the least square, KF includes not only the measurement model, but also the dynamic model. In KF, the predicted value of the state parameters from the dynamic model is considered as pseudomeasurements and combined with the observed measurements to compose the form of the least square. According to the reliability of the least square, the generalized reliability of KF is derived. Then, the dynamic model failure of precise point positioning is simulated to demonstrate the usage of the generalized reliability theory. The results show that the adverse influence of the dynamic model failure is more severe than that of the measurement model. Moreover, it is recommended that the model failure identification should always be used even if the overall model test passes. It is shown that the derived generalized reliability measures are suitable for the generalized KF estimation.展开更多
基金The authors thank the anonymous reviewers for their valuable suggestions.This work is supported by funds National Natural Science Foundation of China(Grants No.52162048,61991404 and 62003138)National Key Research and Development Program of China(Grant No.2020YFB1713703)Jiangxi Graduate Innovation Fund Project(Grant No.YC2021-S446).
文摘Aiming at the robustness issue in high-speed trains(HSTs)operation control,this article proposes a model-free adaptive control(MFAC)scheme to suppress disturbance.Firstly,the dynamic linearization data model of train system under the action of measurement disturbance is given,and the Kalman filter(KF)based on this model is derived under the minimum variance estimation criterion.Then,according to the KF,an anti-interference MFAC scheme is designed.This scheme only needs the input and output data of the controlled system to realize the MFAC of the train under strong disturbance.Finally,the simulation experiment of CRH380A HSTs is carried out and compared with the traditional MFAC and the MFAC with attenuation factor.The proposed control algorithm can effectively suppress the measurement disturbance,and obtain smaller tracking error and larger signal to noise ratio with better applicability.
文摘This article presents an up-to-date tutorial review of nonlinear Bayesian estimation. State estimation for nonlinear systems has been a challenge encountered in a wide range of engineering fields, attracting decades of research effort. To date,one of the most promising and popular approaches is to view and address the problem from a Bayesian probabilistic perspective,which enables estimation of the unknown state variables by tracking their probabilistic distribution or statistics(e.g., mean and covariance) conditioned on a system's measurement data.This article offers a systematic introduction to the Bayesian state estimation framework and reviews various Kalman filtering(KF)techniques, progressively from the standard KF for linear systems to extended KF, unscented KF and ensemble KF for nonlinear systems. It also overviews other prominent or emerging Bayesian estimation methods including Gaussian filtering, Gaussian-sum filtering, particle filtering and moving horizon estimation and extends the discussion of state estimation to more complicated problems such as simultaneous state and parameter/input estimation.
基金supported by the National Natural Science Foundation of China (41074010)the National Science and Technology Planning Projects (2012BAC25B01)+1 种基金the Knowledge Innovation Program of the Chinese Academy of Sciences (KZCX2-EW-QN605)the President Fund of University of Chinese Academy of Sciences
文摘To deal with the adverse influence of model failures on Kalman filtering （KF） estimation, it is necessary to investigate the generalized reliability theory, including the model failure detection and identification method as well as the separability and reliability theories. Although the generalized reliability theory for the least square has been discussed for many decades, the generalized reliability theory of KF is not widely discussed. Compared with the least square, KF includes not only the measurement model, but also the dynamic model. In KF, the predicted value of the state parameters from the dynamic model is considered as pseudomeasurements and combined with the observed measurements to compose the form of the least square. According to the reliability of the least square, the generalized reliability of KF is derived. Then, the dynamic model failure of precise point positioning is simulated to demonstrate the usage of the generalized reliability theory. The results show that the adverse influence of the dynamic model failure is more severe than that of the measurement model. Moreover, it is recommended that the model failure identification should always be used even if the overall model test passes. It is shown that the derived generalized reliability measures are suitable for the generalized KF estimation.
