A New Approach to Receding Horizon State Estimation for LTI Systems in the Presence of Non-uniform Sampled Measurements

This paper proposes a recursive solution as an estimation strategy that incorporates non-uniform sampled measurements for a Linear Time-Invariant (LTI) Systems. The estimator is based on a modified Receding Horizon Estimator. The proposed approach allows system states to be recursively estimated, reducing estimation error by including measurements available at different sampling times, using a well-known structure. A discussion of the observability of the system in the presence of non-uniform measurements and the convergence conditions of the proposed estimator are also presented. Finally, numerical simulation demonstrates the effectiveness of the proposed estimator in comparison with a method using a Kalman filter with augmented state widely reported in the literature.

[1]  G. Goodwin,et al.  Riccati equations in optimal filtering of nonstabilizable systems having singular state transition matrices , 1986 .

[2]  Alireza Khosravian,et al.  State estimation for nonlinear systems with delayed output measurements , 2015, 2015 54th IEEE Conference on Decision and Control (CDC).

[3]  Dan Zhang,et al.  Asynchronous State Estimation for Discrete-Time Switched Complex Networks With Communication Constraints , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[4]  Chenglin Wen,et al.  Finite horizon H∞ filtering for networked measurement system , 2013 .

[5]  Biao Huang,et al.  State estimation incorporating infrequent, delayed and integral measurements , 2015, Autom..

[6]  Hui Wang,et al.  Output feedback stabilization of stochastic feedforward systems with unknown control coefficients and unknown output function , 2018, Autom..

[7]  Niket S. Kaisare,et al.  Incorporating delayed and infrequent measurements in Extended Kalman Filter based nonlinear state estimation , 2011 .

[8]  Damien Koenig,et al.  Robust fault detection for Uncertain Unknown Inputs LPV system , 2014 .

[9]  Biao Huang,et al.  Kalman filtering approach to multi-rate information fusion in the presence of irregular sampling rate and variable measurement delay ☆ , 2017 .

[10]  S. Andrew Gadsden,et al.  Gaussian filters for parameter and state estimation: A general review of theory and recent trends , 2017, Signal Process..

[11]  Keck Voon Ling,et al.  Receding horizon recursive state estimation , 1999, IEEE Trans. Autom. Control..

[12]  Alireza Khosravian,et al.  State estimation for invariant systems on Lie groups with delayed output measurements , 2016, Autom..

[13]  James B. Rawlings,et al.  Application of MHE to large-scale nonlinear processes with delayed lab measurements , 2015, Comput. Chem. Eng..

[14]  Sing Kiong Nguang,et al.  Distributed Filtering for Discrete-Time T–S Fuzzy Systems With Incomplete Measurements , 2018, IEEE Transactions on Fuzzy Systems.

[15]  C. Vasseur,et al.  Piecewise continuous hybrid systems based observer design for linear systems with variable sampling periods and delay output , 2015, Signal Process..

[16]  Juan Diego Sanchez-Torres,et al.  A Soft Sensor for Biomass in a Batch Process with Delayed Measurements , 2016 .

[17]  Qing-Long Han,et al.  H∞ control of LPV systems with randomly multi-step sensor delays , 2014 .

[18]  P. T. Nam,et al.  Partial state estimation for linear systems with output and input time delays. , 2014, ISA transactions.

[19]  Jing Zhang,et al.  Observer-enhanced distributed moving horizon state estimation subject to communication delays , 2014 .

[20]  Jinde Cao,et al.  Robust H∞ State-feedback Control for Nonlinear Uncertain Systems with Mixed Time-varying Delays , 2018 .

[21]  Jhon A. Isaza-Hurtado,et al.  State Estimation Using Non-uniform and Delayed Information: A Review , 2018, Int. J. Autom. Comput..

[22]  Jing Zeng,et al.  Distributed moving horizon state estimation: Simultaneously handling communication delays and data losses , 2015, Syst. Control. Lett..

[23]  Tao Yu,et al.  Nonlinear state estimation for fermentation process using cubature Kalman filter to incorporate delayed measurements , 2015 .

[24]  Denis Dochain,et al.  Review and classification of recent observers applied in chemical process systems , 2015, Comput. Chem. Eng..

[25]  Josep M. Guerrero,et al.  Industrial Applications of the Kalman Filter: A Review , 2013, IEEE Transactions on Industrial Electronics.

[26]  William W. Hager,et al.  Updating the Inverse of a Matrix , 1989, SIAM Rev..

[27]  Magdi S. Mahmoud,et al.  State estimation with asynchronous multi-rate multi-smart sensors , 2012, Inf. Sci..