Abstract
Adaptive control systems are designed to achieve desired control performance when plant parameters gains are unknown, or possibly slowly changing. Highly calculation technology is developed, more important discrete-time adaptive control structure is. Because the relative degree condition for strict positive realness (SPR) of the discrete-time error transfer function is different from the condition of the continuous time case, it is hard to prove the stability of the discretized continuous-time adaptive control systems. The main contribution of this paper is the extension of an Immersion and Invariance (I&I)-based adaptive control algorithm from continuous to discrete time. The theoretical stability of the proposed discrete time I&I-based adaptive control system is proved. In order to show the effectivaness of the proposed method, numerical simultions are shown.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the IEEE Conference on Decision and Control |
| Pages | 720-725 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 2008 |
| Event | 47th IEEE Conference on Decision and Control, CDC 2008 - Cancun, Mexico Duration: 2008 Dec 9 → 2008 Dec 11 |
Other
| Other | 47th IEEE Conference on Decision and Control, CDC 2008 |
|---|---|
| Country | Mexico |
| City | Cancun |
| Period | 08/12/9 → 08/12/11 |
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ASJC Scopus subject areas
- Control and Systems Engineering
- Modelling and Simulation
- Control and Optimization
Cite this
A design method of discrete-time adaptive control systems based on immersion and invariance. / Katakura, Yuta; Ohmori, Hiromitsu.
Proceedings of the IEEE Conference on Decision and Control. 2008. p. 720-725 4738630.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A design method of discrete-time adaptive control systems based on immersion and invariance
AU - Katakura, Yuta
AU - Ohmori, Hiromitsu
PY - 2008
Y1 - 2008
N2 - Adaptive control systems are designed to achieve desired control performance when plant parameters gains are unknown, or possibly slowly changing. Highly calculation technology is developed, more important discrete-time adaptive control structure is. Because the relative degree condition for strict positive realness (SPR) of the discrete-time error transfer function is different from the condition of the continuous time case, it is hard to prove the stability of the discretized continuous-time adaptive control systems. The main contribution of this paper is the extension of an Immersion and Invariance (I&I)-based adaptive control algorithm from continuous to discrete time. The theoretical stability of the proposed discrete time I&I-based adaptive control system is proved. In order to show the effectivaness of the proposed method, numerical simultions are shown.
AB - Adaptive control systems are designed to achieve desired control performance when plant parameters gains are unknown, or possibly slowly changing. Highly calculation technology is developed, more important discrete-time adaptive control structure is. Because the relative degree condition for strict positive realness (SPR) of the discrete-time error transfer function is different from the condition of the continuous time case, it is hard to prove the stability of the discretized continuous-time adaptive control systems. The main contribution of this paper is the extension of an Immersion and Invariance (I&I)-based adaptive control algorithm from continuous to discrete time. The theoretical stability of the proposed discrete time I&I-based adaptive control system is proved. In order to show the effectivaness of the proposed method, numerical simultions are shown.
UR - http://www.scopus.com/inward/record.url?scp=62949173452&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=62949173452&partnerID=8YFLogxK
U2 - 10.1109/CDC.2008.4738630
DO - 10.1109/CDC.2008.4738630
M3 - Conference contribution
AN - SCOPUS:62949173452
SN - 9781424431243
SP - 720
EP - 725
BT - Proceedings of the IEEE Conference on Decision and Control
ER -