Linear Moving Horizon Estimation With Pre-Estimating Observer

In this note, a moving horizon estimation (MHE) strategy for detectable linear systems is proposed. Like the idea of “pre-stabilizing” model-predictive control, the states are estimated by a forward simulation with a pre-estimating observer in the MHE formulation. Compared with standard linear MHE approaches, it has more degrees of freedom to optimize the noise filtering. Tuning parameters are chosen to minimize the effects of measurement noise and model errors, which is implemented by finding tightest estimation error bounds. The performance of the proposed observer is demonstrated on one linear discrete-time example.

[1]  Jos F. Sturm,et al.  A Matlab toolbox for optimization over symmetric cones , 1999 .

[2]  D. Simon Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches , 2006 .

[3]  James B. Rawlings,et al.  Critical Evaluation of Extended Kalman Filtering and Moving-Horizon Estimation , 2005 .

[4]  Thomas Parisini,et al.  A neural state estimator with bounded errors for nonlinear systems , 1999, IEEE Trans. Autom. Control..

[5]  Giorgio Battistelli,et al.  Receding-horizon estimation for discrete-time linear systems , 2003, IEEE Trans. Autom. Control..

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

[7]  Robert H. Halstead,et al.  Matrix Computations , 2011, Encyclopedia of Parallel Computing.

[8]  D. Luenberger Observers for multivariable systems , 1966 .

[9]  A. Jazwinski Limited memory optimal filtering , 1968 .

[10]  Fuzhen Zhang The Schur complement and its applications , 2005 .

[11]  Basil Kouvaritakis,et al.  Efficient robust predictive control , 2000, IEEE Trans. Autom. Control..

[12]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[13]  D. Hinrichsen,et al.  Stochastic $H^\infty$ , 1998 .

[14]  E. Yaz Linear Matrix Inequalities In System And Control Theory , 1998, Proceedings of the IEEE.

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

[16]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[17]  D. Luenberger An introduction to observers , 1971 .

[18]  Riccardo Scattolini,et al.  A stabilizing model-based predictive control algorithm for nonlinear systems , 2001, Autom..

[19]  Eduardo D. Sontag,et al.  A concept of local observability , 1984 .

[20]  Johan Löfberg YALMIP: A MATLAB interface to SP, MAXDET and SOCP , 2001 .

[21]  Jay H. Lee,et al.  Constrained linear state estimation - a moving horizon approach , 2001, Autom..

[22]  Giorgio Battistelli,et al.  On estimation error bounds for receding-horizon filters using quadratic boundedness , 2004, IEEE Transactions on Automatic Control.

[23]  G. Kreisselmeier Adaptive observers with exponential rate of convergence , 1977 .

[24]  M. Alamir Optimization based non-linear observers revisited , 1999 .

[25]  Eric C. Kerrigan,et al.  Offset‐free receding horizon control of constrained linear systems , 2005 .

[26]  Mark Rice,et al.  A numerically robust state-space approach to stable-predictive control strategies , 1998, Autom..

[27]  Chi-Tsong Chen,et al.  Linear System Theory and Design , 1995 .

[28]  Giorgio Battistelli,et al.  Moving-horizon state estimation for nonlinear discrete-time systems: New stability results and approximation schemes , 2008, Autom..

[29]  F. Allgöwer,et al.  Remarks on moving horizon state estimation with guaranteed convergence , 2005 .