This paper is concerned with the question of, for a physical plant to be controlled, whether or not its internal dynamics and external disturbances can be realistically estimated in real time from its input–output data. A positive answer would have significant implications on control system design, because it means that an accurate model of the plant is perhaps no longer required. Based on the extended state observer, it is shown that, for an nth order plant, the answer to the above question is indeed yes. In particular, it is shown that the estimation error converges to the origin asymptotically when the model of the plant is given. In face of large dynamic uncertainties, the estimation error is shown to be bounded. Furthermore, it is demonstrated that the error upper bound monotonously decreases with the bandwidth. Note that this is not another parameter estimation algorithm in the framework of adaptive control. It applies to a large class of nonlinear, time-varying processes with unknown dynamics. The solution is deceivingly simple and easy to implement. The results of analysis are further verified through simulation and hardware tests.

References

References
1.
Basile
,
G.
, and
Marro
,
G.
, 1969, “
On the Observability of Linear, Time-Invariant Systems With Unknown Inputs
,”
J. Optim. Theory Appl.
,
2
(
6
), pp.
410
415
.
2.
Chen
,
J.
,
Patton
,
R. J.
, and
Zhang
,
H.
, 1995, “
Design of Unknown Input Observers and Robust Fault Detection Filters
,”
Int. J. Control
,
63
(
1
), pp.
85
105
.
3.
Bickel
,
R.
, and
Tomizuka
,
M.
, 1999, “
Passivity-Based Versus Disturbance Observer Based Robot Control: Equivalence and Stability
,”
ASME J. Dyn. Syst., Meas., Control
,
121
, pp.
41
47
.
4.
Schrijver
,
E.
, and
van Dijk
,
J.
, 2002, “
Disturbance Observers for Rigid Mechanical Systems: Equivalence, Stability, and Design
,”
ASME J. Dyn. Syst., Meas., Control
,
124
(
4
), pp.
539
548
.
5.
Kwon
,
S.
, and
Chung
,
W. K.
, 2003, “
Combined Synthesis of State Estimator and Perturbation Observer
,”
ASME J. Dyn. Syst., Meas., Control
,
125
, pp.
19
26
.
6.
Han
,
J.
, 1995, “
A Class of Extended State Observers for Uncertain Systems
,”
Control Decision
,
10
(
1
), pp.
85
88
. (In Chinese)
7.
Gao
,
Z.
,
Huang
,
Y.
, and
Han
,
J.
, 2001, “
An Alternative Paradigm for Control System Design
,”
Proceedings of IEEE Conference on Decision and Control
, pp.
4578
4585
.
8.
Gao
,
Z.
, 2003, “
Scaling and Parameterization Based Controller Tuning
,”
Proceedings of the American Control Conference
, pp.
4989
4996
.
9.
Gao
,
Z.
, 2006, “
Active Disturbance Rejection Control: A Paradigm Shift in Feedback Control System Design
,”
Proceedings of the American Control Conference
, pp.
2399
2405
.
10.
Han
,
J.
, 2009, “
From PID to Active Disturbance Rejection Control
,”
IEEE Trans. Ind. Electron. Control Instrum.
,
56
(
3
), pp.
1
7
.
11.
Yao
,
B.
,
Al-Majed
,
M.
, and
Tomizuka
,
M.
, 1997, “
High-Performance Robust Motion Control of Machine Tools: An Adaptive Robust Control Approach and Comparative Experiments
,”
IEEE/ASME Trans. Mechatron.
,
2
, pp.
63
76
.
12.
Su
,
Y. X.
,
Duan
,
B. Y.
,
Zheng
,
C. H.
,
Zhang
,
Y. F.
,
Chen
,
G. D.
, and
Mi
,
J. W.
, 2004, “
Disturbance-Rejection High-Precision Motion Control of a Stewart Platform
,”
IEEE Trans. Control Syst. Technol.
,
12
(
3
), pp.
364
374
.
13.
Zheng
,
Q.
,
Dong
,
L.
,
Lee
,
D. H.
, and
Gao
,
Z.
, 2009, “
Active Disturbance Rejection Control and Implementation for MEMS Gyroscopes
,”
IEEE Trans. Control Syst. Technol.
,
17
(
6
), pp.
1432
1438
.
14.
Zheng
,
Q.
,
Chen
,
Z.
, and
Gao
,
Z.
, 2009, “
A Practical Dynamic Decoupling Control Approach
,”
Control Eng. Pract.
,
17
(
9
), pp.
1016
1025
.
15.
Zheng
,
Q.
, and
Goforth
,
F. J.
, 2010, “
An Active Disturbance Rejection Based Control Approach for Hysteretic Systems
,”
Proceedings of the 49th IEEE Conference on Decision and Control
,
pp. 3748
3753
.
16.
Manual for Model 220 Industrial Emulator/Servo Trainer, Educational Control Products, 5725 Ostin Avenue, Woodland Hills, CA 91367, 1995.
You do not currently have access to this content.