An EKF-Based Nonlinear Observer with a Prescribed Degree of Stability

The topic of this article is a nonlinear observer based on a slight modification of the extended Kalman filter. The purpose of this modification is twofold: first the degree of stability can be assigned in advance and secondly this modification allows an effective treatment of the nonlinearities. Using the second method of Lyapunov it is proved that the proposed observer is an exponential observer. To examine the practical usefulness the proposed observer is applied to the highly nonlinear flux and angular velocity estimation problem for induction machines.

[1]  D. Luenberger Observing the State of a Linear System , 1964, IEEE Transactions on Military Electronics.

[2]  J. Gauthier,et al.  High gain estimation for nonlinear systems , 1992 .

[3]  D. Wiberg,et al.  An ordinary differential equation technique for continuous-time parameter estimation , 1993, IEEE Trans. Autom. Control..

[4]  H. Sorenson,et al.  Guest editorial: On applications of Kalman filtering , 1983 .

[5]  Silvio Stasi,et al.  A new EKF-based algorithm for flux estimation in induction machines , 1993, IEEE Trans. Ind. Electron..

[6]  R. Unbehauen,et al.  Linearisation Along Trajectories and the Extended Kalman Filter , 1996 .

[7]  W. Walter Differential and Integral Inequalities , 1970 .

[8]  S. Żak,et al.  Comparative study of non-linear state-observation techniques , 1987 .

[9]  R. E. Kalman,et al.  New Results in Linear Filtering and Prediction Theory , 1961 .

[10]  Brian D. O. Anderson Exponential data weighting in the Kalman-Bucy filter , 1973, Inf. Sci..

[11]  Konrad Reif,et al.  Stochastic stability of the discrete-time extended Kalman filter , 1999, IEEE Trans. Autom. Control..

[12]  Tzyh Jong Tarn,et al.  Exponential Observers for Nonlinear Dynamic Systems , 1975, Inf. Control..

[13]  J. Grizzle,et al.  The Extended Kalman Filter as a Local Asymptotic Observer for Nonlinear Discrete-Time Systems , 1992, 1992 American Control Conference.

[14]  J. Hedrick,et al.  Nonlinear Observers—A State-of-the-Art Survey , 1989 .

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

[16]  Arthur Gelb,et al.  Applied Optimal Estimation , 1974 .

[17]  A. Jazwinski Stochastic Processes and Filtering Theory , 1970 .

[18]  R. Fitzgerald Divergence of the Kalman filter , 1971 .

[19]  H. Triebel Analysis und mathematische Physik , 1989 .

[20]  Harold W. Sorenson,et al.  Recursive fading memory filtering , 1971, Inf. Sci..