On dynamic uncertainty estimators

The paper is concerned with state predictors that include a disturbance dynamics capable of estimating the disturbance to be rejected. The disturbance dynamics is driven by an unknown input signal, the uncertainty input, which is the output of a dynamic feedback driven by the model error (plant minus model output). As an extension of classical observers, the paper shows the advantage of designing a dynamic feedback. A dynamic feedback can be designed to fit the structure of the uncertainty and has the advantage of increasing the relative degree of the state-predictor transfer function, without penalizing the sensitivity itself. A higher relative degree increases the high-frequency rejection rate, thus impeding neglected dynamics components of spilling through control signals to the detriment of stability. A simulated case study shows the better performance of dynamic estimators.

[1]  Joachim Rosenthal,et al.  Open problems in the area of pole placement , 1999 .

[2]  A. Tornambè.  Use of asymptotic observers having-high-gains in the state and parameter estimation , 1989, Proceedings of the 28th IEEE Conference on Decision and Control,.

[3]  Gerald Cook,et al.  Identification/Observation Using an Extended Luenberger Observer , 1982, IEEE Transactions on Industrial Electronics.

[4]  노대종,et al.  Nonlinear observer design by dynamic observer error linearization , 2001 .

[5]  Zhiqiang Gao,et al.  A comparison study of advanced state observer design techniques , 2003, Proceedings of the 2003 American Control Conference, 2003..

[6]  Bruce A. Francis,et al.  The internal model principle of control theory , 1976, Autom..

[7]  J. Meditch,et al.  Observers for systems with unknown and inaccessible inputs , 1973, CDC 1973.

[8]  Enrico S. Canuto,et al.  Robust control stability using the error loop , 2013, Int. J. Mechatronics Autom..

[9]  Faryar Jabbari,et al.  Observers for stabilization and disturbance attenuation in uncertain systems , 1993, Proceedings of 32nd IEEE Conference on Decision and Control.

[10]  Andrei Gabrielov,et al.  Pole Placement by Static Output Feedback for Generic Linear Systems , 2002, SIAM J. Control. Optim..

[11]  H.K. Khalil,et al.  Robust Feedback Linearization using Extended High-Gain Observers , 2006, Proceedings of the 45th IEEE Conference on Decision and Control.

[12]  E. Davison.  The output control of linear time-invariant multivariable systems with unmeasurable arbitrary disturbances , 1972 .

[13]  Enrico Canuto,et al.  Embedded Model Control calls for disturbance modeling and rejection. , 2012, ISA transactions.

[14]  J. Meditch,et al.  Observing systems with unmmeasurable inputs , 1973 .

[15]  Pau-Lo Hsu,et al.  General-structured unknown input observers , 1994, Proceedings of 1994 American Control Conference - ACC '94.

[16]  Zhi Wang,et al.  Robust stabilization of nonlinear uncertain systems in the presence of input unmodeled dynamics , 2000, Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187).

[17]  Enrico S. Canuto,et al.  Angular drag-free control and fine satellite-to-satellite pointing for the Next Generation Gravity Missions , 2014, 2014 European Control Conference (ECC).

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

[19]  D. Andrisani,et al.  Observer design for discrete systems with unknown exogenous inputs , 1990 .

[20]  Enrico Canuto.  Embedded Model Control: outline of the theory. , 2007, ISA transactions.

[21]  Enrico Canuto,et al.  Embedded Model Control: Design separation under uncertainty , 2014, Proceedings of the 33rd Chinese Control Conference.

[22]  M. Aoki,et al.  Estimation of the state vector of a linear stochastic system with a constrained estimator , 1967, IEEE Transactions on Automatic Control.

[23]  H. Weiss.  On the structure of the Luenberger observer in discrete-time linear stochastic systems , 1977 .

[24]  Zhiqiang Gao,et al.  Active disturbance rejection control: a paradigm shift in feedback control system design , 2006, 2006 American Control Conference.