Gauss-Newton moving horizon observer for state and parameter estimation of nonlinear networked control systems

In this paper a method to design an observer for state and parameter estimation of sampled nonlinear systems where the sensor possesses an unsynchronized time is presented. The sensor measurements are transmitted together with the relative time stamps as a packet to the observer via a communication network. These packets are subject to random delay or might even be completely lost. Using the information of a fixed number of past packets and the model equations, state and parameter estimation is expressed as a moving horizon optimization problem which is solved by using the Gauss-Newton algorithm. The performance of this method is illustrated using a simulated continuous stirred tank reactor.

[1]  Dale E. Seborg,et al.  Nonlinear Process Control , 1996 .

[2]  Johan Nilsson,et al.  Stochastic Analysis and Control of Real-Time Systems with Random Time Delays , 1996 .

[3]  Nasser E. Nahi,et al.  Optimal recursive estimation with uncertain observation , 1969, IEEE Trans. Inf. Theory.

[4]  J. Grizzle,et al.  Observer design for nonlinear systems with discrete-time measurements , 1995, IEEE Trans. Autom. Control..

[5]  Panos J. Antsaklis,et al.  Stability of model-based networked control systems with time-varying transmission times , 2004, IEEE Transactions on Automatic Control.

[6]  Philip E. Gill,et al.  Practical optimization , 1981 .

[7]  Harold L. Alexander,et al.  State estimation for distributed systems with sensing delay , 1991, Defense, Security, and Sensing.

[8]  R.M. Murray,et al.  Estimation for Nonlinear Dynamical Systems over Packet-Dropping Networks , 2007, 2007 American Control Conference.

[9]  Björn Wittenmark,et al.  Stochastic Analysis and Control of Real-time Systems with Random Time Delays , 1999 .

[10]  Richard M. Murray,et al.  State estimation over packet dropping networks using multiple description coding , 2006, Autom..

[11]  Luca Schenato,et al.  Optimal Estimation in Networked Control Systems Subject to Random Delay and Packet Drop , 2008, IEEE Transactions on Automatic Control.

[12]  Long Wang,et al.  An LMI approach to networked control systems with data packet dropout and transmission delays , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[13]  D. Mayne,et al.  Moving horizon observers and observer-based control , 1995, IEEE Trans. Autom. Control..

[14]  João Pedro Hespanha,et al.  A Survey of Recent Results in Networked Control Systems , 2007, Proceedings of the IEEE.

[15]  David Q. Mayne,et al.  Constrained state estimation for nonlinear discrete-time systems: stability and moving horizon approximations , 2003, IEEE Trans. Autom. Control..

[16]  A. B. Poore,et al.  On the dynamic behavior of continuous stirred tank reactors , 1974 .

[17]  Gene H. Golub,et al.  Matrix computations (3rd ed.) , 1996 .

[18]  M. A. Bhatti,et al.  Practical Optimization Methods , 2000 .

[19]  Jens Vortisch.  Zustandsbeobachtung nichtlinearer Systeme auf bewegtem Horizont mit Hilfe des Gauss-Newton-Verfahrens , 2003 .