Abstract
This paper proposes a direct model reference adaptive control method for linear systems with unknown parameters in the presence of input constraints. First, we used the well-known linear quadratic regulator (LQR) technique to develop a modified reference model, which is the optimal model under input constraints. Second, a model reference adaptive controller, which tracked the modified reference model instead of the reference model, was designed to compensate for parametric uncertainties. Using Lyapunov stability theory, we proved that the modified reference model tracking error converges to zero. Simulation results demonstrate the effectiveness of the proposed controller.
| Original language | English |
|---|---|
| Pages (from-to) | 890-896 |
| Number of pages | 7 |
| Journal | International Journal of Control, Automation and Systems |
| Volume | 10 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - 2012 Oct 1 |
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All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Computer Science Applications
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Adaptive control for input-constrained linear systems. / Park, Bong Seok; Lee, Jae Young; Park, Jin Bae; Choi, Yoon Ho.
In: International Journal of Control, Automation and Systems, Vol. 10, No. 5, 01.10.2012, p. 890-896.Research output: Contribution to journal › Article
TY - JOUR
T1 - Adaptive control for input-constrained linear systems
AU - Park, Bong Seok
AU - Lee, Jae Young
AU - Park, Jin Bae
AU - Choi, Yoon Ho
PY - 2012/10/1
Y1 - 2012/10/1
N2 - This paper proposes a direct model reference adaptive control method for linear systems with unknown parameters in the presence of input constraints. First, we used the well-known linear quadratic regulator (LQR) technique to develop a modified reference model, which is the optimal model under input constraints. Second, a model reference adaptive controller, which tracked the modified reference model instead of the reference model, was designed to compensate for parametric uncertainties. Using Lyapunov stability theory, we proved that the modified reference model tracking error converges to zero. Simulation results demonstrate the effectiveness of the proposed controller.
AB - This paper proposes a direct model reference adaptive control method for linear systems with unknown parameters in the presence of input constraints. First, we used the well-known linear quadratic regulator (LQR) technique to develop a modified reference model, which is the optimal model under input constraints. Second, a model reference adaptive controller, which tracked the modified reference model instead of the reference model, was designed to compensate for parametric uncertainties. Using Lyapunov stability theory, we proved that the modified reference model tracking error converges to zero. Simulation results demonstrate the effectiveness of the proposed controller.
UR - http://www.scopus.com/inward/record.url?scp=84870943529&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84870943529&partnerID=8YFLogxK
U2 - 10.1007/s12555-012-0504-4
DO - 10.1007/s12555-012-0504-4
M3 - Article
AN - SCOPUS:84870943529
VL - 10
SP - 890
EP - 896
JO - International Journal of Control, Automation and Systems
JF - International Journal of Control, Automation and Systems
SN - 1598-6446
IS - 5
ER -