Practical issues in designing estimators under realistic model and sensor errors

Control Engineering Practice

Volumn 6, Issue 2, 1998, Pages 197-208

  Agarwal, Mukul ^a  

^a TCL   (Switzerland)

Author keywords

estimators; filter design; modelling errors; observers; parameter estimation; prediction; sensors; state estimation; state space models; tuning characteristics

Indexed keywords

MODELLING ERRORS;

CONTROL SYSTEM SYNTHESIS; EXTRAPOLATION; MEASUREMENT ERRORS; PARAMETER ESTIMATION; SENSORS; STATE SPACE METHODS;

STATE ESTIMATION;

EID: 0032004577     PISSN: 09670661     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0967-0661(98)00011-2     Document Type: Article

References (9)

1. Agarwal, M., 1993. State identification for control of poorly modeled processes. Proc. European Control Conf. Groningen, NL, pp. 2313-2318.
2. Agarwal M. Common fallacies in estimation and control under uncertainty. J. Process Control. 6(5):1996;295-307.
3. Chen Y.H. State estimation for non-linear uncertain systems. A design based on properties related to the uncertainty bound Int. J. Control. 52(5):1990;1131-1146.
4. Ciccarella G., Dalla Mora M., Germani A. A Luenberger-like observer for nonlinear systems. Int. J. Control. 57(3):1993;537-556.
5. Jazwinski A.H. Stochastic Processes and Filtering Theory. 1970;Academic Press, New York.
6. Liang D.F. Exact and approximate state estimation techniques for nonlinear dynamic systems. Control Dynamic Systems. 19:1983;1-80.
7. Ljung L., Gunnarsson S. Adaptation and tracking in system identification - a survey. Automatica. 26:1990;7-21.
8. Ljung L., Söderström T. Theory and Practice of Recursive Identification. 1983;MIT Press, London, UK.
9. Sorenson H.W. Kalman Filtering. Theory and Application:1985;IEEE Press, New York.