Abstract
In this paper, a new type of output feedback control, called a receding horizon finite memory control (RHFMC), is proposed for stochastic discrete-time state space systems with general system matrix. Constraints such as linearity and finite memory structure with respect to an input and an output, and unbiasedness from the optimal state feedback control are required in advance. The proposed RHFMC is chosen to minimize an optimal criterion with these constraints. The RHFMC is obtained in an explicit closed-form using the output and input information on the recent time interval. It is shown that the RHFMC consists of a receding horizon control and an FIR filter for the system with general system matrix. The stability of the RHFMC is investigated for stochastic systems under inequality condition on the finite terminal weighting matrix.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the SICE Annual Conference |
| Pages | 2249-2254 |
| Number of pages | 6 |
| Publication status | Published - 2005 Dec 1 |
| Externally published | Yes |
| Event | SICE Annual Conference 2005 - Okayama, Japan Duration: 2005 Aug 8 → 2005 Aug 10 |
Other
| Other | SICE Annual Conference 2005 |
|---|---|
| Country | Japan |
| City | Okayama |
| Period | 05/8/8 → 05/8/10 |
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Keywords
- Finite memory
- FIR
- General system matrix
- Output feedback
- Predictive cotnrol
- Receding horizon
ASJC Scopus subject areas
- Engineering(all)
Cite this
Receding horizon finite memory control for linear discrete-time systems with general system matrix. / Ahn, Choon Ki; Han, Soohee; Quan, Zhonghua; Kwon, Wook Hyun.
Proceedings of the SICE Annual Conference. 2005. p. 2249-2254.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Receding horizon finite memory control for linear discrete-time systems with general system matrix
AU - Ahn, Choon Ki
AU - Han, Soohee
AU - Quan, Zhonghua
AU - Kwon, Wook Hyun
PY - 2005/12/1
Y1 - 2005/12/1
N2 - In this paper, a new type of output feedback control, called a receding horizon finite memory control (RHFMC), is proposed for stochastic discrete-time state space systems with general system matrix. Constraints such as linearity and finite memory structure with respect to an input and an output, and unbiasedness from the optimal state feedback control are required in advance. The proposed RHFMC is chosen to minimize an optimal criterion with these constraints. The RHFMC is obtained in an explicit closed-form using the output and input information on the recent time interval. It is shown that the RHFMC consists of a receding horizon control and an FIR filter for the system with general system matrix. The stability of the RHFMC is investigated for stochastic systems under inequality condition on the finite terminal weighting matrix.
AB - In this paper, a new type of output feedback control, called a receding horizon finite memory control (RHFMC), is proposed for stochastic discrete-time state space systems with general system matrix. Constraints such as linearity and finite memory structure with respect to an input and an output, and unbiasedness from the optimal state feedback control are required in advance. The proposed RHFMC is chosen to minimize an optimal criterion with these constraints. The RHFMC is obtained in an explicit closed-form using the output and input information on the recent time interval. It is shown that the RHFMC consists of a receding horizon control and an FIR filter for the system with general system matrix. The stability of the RHFMC is investigated for stochastic systems under inequality condition on the finite terminal weighting matrix.
KW - Finite memory
KW - FIR
KW - General system matrix
KW - Output feedback
KW - Predictive cotnrol
KW - Receding horizon
UR - http://www.scopus.com/inward/record.url?scp=33645313790&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33645313790&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:33645313790
SP - 2249
EP - 2254
BT - Proceedings of the SICE Annual Conference
ER -