Subsystem decomposition for distributed state estimation of nonlinear systems

In this work, we investigate the subsystem decomposition problem for distributed state estimation of nonlinear systems. A systematic procedure for subsystem decomposition for distributed state estimation is proposed. Key steps in the procedure include observability test of the entire system, observable states identification for each output measurement, relative degree analysis and sensitivity analysis between measured outputs and states. A few examples used to illustrate the methods used in different steps and the entire procedure demonstrate the effectiveness and applicability of the proposed methods/procedure.

[1]  Prodromos Daoutidis,et al.  Input/output hierarchical clustering in process networks based on relative degrees , 2015, 2015 American Control Conference (ACC).

[2]  José M. F. Moura,et al.  Distributing the Kalman Filter for Large-Scale Systems , 2007, IEEE Transactions on Signal Processing.

[3]  Jakob Stoustrup,et al.  Control configuration selection for multivariable descriptor systems , 2012, 2012 American Control Conference (ACC).

[4]  Jing Zhang,et al.  Two triggered information transmission algorithms for distributed moving horizon state estimation , 2014, Syst. Control. Lett..

[5]  Panagiotis D. Christofides,et al.  Selection of control configurations for economic model predictive control systems , 2014 .

[6]  Panagiotis D. Christofides,et al.  Distributed model predictive control: A tutorial review and future research directions , 2013, Comput. Chem. Eng..

[7]  Ali Elkamel,et al.  Selection of control structure for distributed model predictive control in the presence of model errors , 2010 .

[8]  Francis J. Doyle,et al.  Distributed model predictive control of an experimental four-tank system , 2007 .

[9]  Prodromos Daoutidis,et al.  Automated synthesis of control configurations for process networks based on structural coupling , 2015 .

[10]  Reza Olfati-Saber,et al.  Distributed Kalman filtering for sensor networks , 2007, 2007 46th IEEE Conference on Decision and Control.

[11]  D. R. Lewin,et al.  A constrained genetic algorithm for decentralized control system structure selection and optimization , 2003, Autom..

[12]  Jinfeng Liu,et al.  Distributed moving horizon state estimation for nonlinear systems with bounded uncertainties , 2013 .

[13]  Riccardo Scattolini,et al.  Architectures for distributed and hierarchical Model Predictive Control - A review , 2009 .

[14]  James B. Rawlings,et al.  Coordinating multiple optimization-based controllers: New opportunities and challenges , 2008 .

[15]  Marcello Farina,et al.  Distributed Moving Horizon Estimation for Linear Constrained Systems , 2010, IEEE Transactions on Automatic Control.

[16]  Prodromos Daoutidis,et al.  Structural evaluation of control configurations for multivariable nonlinear processes , 1992 .

[17]  Ruggero Carli,et al.  Distributed Kalman filtering based on consensus strategies , 2008, IEEE Journal on Selected Areas in Communications.

[18]  A. Germani,et al.  A Luenberger-like observer for nonlinear systems , 1993 .