Abstract
Adaptive tracking of nonlinear dynamic plants is presently an active area of research. The main difficulty felt in establishing the tracking of nonlinear dynamic plants is the computational complexity in controller design. This paper presents novel adaptive tracking techniques for a class of nonlinear dynamic plants based on a control oriented model known as U-model. The use of U-model reduces the computational complexity of the controller design that occurs when using other modelling frame works such as NARMAX (Nonlinear Autoregressive Moving Average with eXogenous inputs) model. Four different adaptive control techniques based on U-model are discussed namely adaptive inverse control (AIC), adaptive internal model control (AIMC), AIC with pole-placement and AIMC with pole-placement. The proposed techniques are implemented in real-time on a laboratory scale experimental setup for speed control of DC-motor. The results of the experiments are provided.
| Original language | English |
|---|---|
| Title of host publication | IEEE International Conference on Engineering of Intelligent Systems, ICEIS 2006 |
| Publication status | Published - 2006 |
| Event | IEEE International Conference on Engineering of Intelligent Systems, ICEIS 2006 - Islamabad, Pakistan Duration: Apr 22 2006 → Apr 23 2006 |
Other
| Other | IEEE International Conference on Engineering of Intelligent Systems, ICEIS 2006 |
|---|---|
| Country | Pakistan |
| City | Islamabad |
| Period | 4/22/06 → 4/23/06 |
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ASJC Scopus subject areas
- Control and Systems Engineering
- Electrical and Electronic Engineering
Cite this
Innovative inverse control techniques for adaptive tracking of nonlinear dynamic plants. / Khan, Tahir; Shafiq, Muhammad.
IEEE International Conference on Engineering of Intelligent Systems, ICEIS 2006. 2006. 1703149.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Innovative inverse control techniques for adaptive tracking of nonlinear dynamic plants
AU - Khan, Tahir
AU - Shafiq, Muhammad
PY - 2006
Y1 - 2006
N2 - Adaptive tracking of nonlinear dynamic plants is presently an active area of research. The main difficulty felt in establishing the tracking of nonlinear dynamic plants is the computational complexity in controller design. This paper presents novel adaptive tracking techniques for a class of nonlinear dynamic plants based on a control oriented model known as U-model. The use of U-model reduces the computational complexity of the controller design that occurs when using other modelling frame works such as NARMAX (Nonlinear Autoregressive Moving Average with eXogenous inputs) model. Four different adaptive control techniques based on U-model are discussed namely adaptive inverse control (AIC), adaptive internal model control (AIMC), AIC with pole-placement and AIMC with pole-placement. The proposed techniques are implemented in real-time on a laboratory scale experimental setup for speed control of DC-motor. The results of the experiments are provided.
AB - Adaptive tracking of nonlinear dynamic plants is presently an active area of research. The main difficulty felt in establishing the tracking of nonlinear dynamic plants is the computational complexity in controller design. This paper presents novel adaptive tracking techniques for a class of nonlinear dynamic plants based on a control oriented model known as U-model. The use of U-model reduces the computational complexity of the controller design that occurs when using other modelling frame works such as NARMAX (Nonlinear Autoregressive Moving Average with eXogenous inputs) model. Four different adaptive control techniques based on U-model are discussed namely adaptive inverse control (AIC), adaptive internal model control (AIMC), AIC with pole-placement and AIMC with pole-placement. The proposed techniques are implemented in real-time on a laboratory scale experimental setup for speed control of DC-motor. The results of the experiments are provided.
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M3 - Conference contribution
SN - 1424404568
SN - 9781424404568
BT - IEEE International Conference on Engineering of Intelligent Systems, ICEIS 2006
ER -