Abstract
The use of Intelligent control schemes in Nonlinear Model Based Control (NMBC) has gained widespread popularity. Neural Networks, in particular, have been used extensively to model the dynamics of nonlinear plants. However, in most cases, these models do not lend themselves to easy maneuvering for controller design.Therefore, a common need is being felt to develop intelligent control strategies that lead to computationally simple control laws. To achieve this objective, the present study combines the approximation power of Higher-Order Neural Networks (HONN) with the control-oriented nature of the recently developed U-model. By introducing the U-model equivalence of a Higher-Order Neural Unit (HONU), the control law synthesis part is reduced to a simple polynomial root-solving procedure. The proposed scheme is based on the robust Internal Model Control (IMC) structure and is suitable for stable nonlinear plants with uncertain dynamics. The main feature of the proposed structure is its ability to capture higher-order nonlinear properties of the input pattern space while allowing the synthesis of a simple control law. The scheme is therefore expected to prove extremely useful in the area of nonlinear adaptive control. The effectiveness of the proposed scheme is demonstrated through application to various nonlinear models.
| 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
Higher-order neural network based root-solving controller for adaptive tracking of stable nonlinear plants. / Butt, Naveed Razzaq; Shafiq, Muhammad.
IEEE International Conference on Engineering of Intelligent Systems, ICEIS 2006. 2006. 1703175.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Higher-order neural network based root-solving controller for adaptive tracking of stable nonlinear plants
AU - Butt, Naveed Razzaq
AU - Shafiq, Muhammad
PY - 2006
Y1 - 2006
N2 - The use of Intelligent control schemes in Nonlinear Model Based Control (NMBC) has gained widespread popularity. Neural Networks, in particular, have been used extensively to model the dynamics of nonlinear plants. However, in most cases, these models do not lend themselves to easy maneuvering for controller design.Therefore, a common need is being felt to develop intelligent control strategies that lead to computationally simple control laws. To achieve this objective, the present study combines the approximation power of Higher-Order Neural Networks (HONN) with the control-oriented nature of the recently developed U-model. By introducing the U-model equivalence of a Higher-Order Neural Unit (HONU), the control law synthesis part is reduced to a simple polynomial root-solving procedure. The proposed scheme is based on the robust Internal Model Control (IMC) structure and is suitable for stable nonlinear plants with uncertain dynamics. The main feature of the proposed structure is its ability to capture higher-order nonlinear properties of the input pattern space while allowing the synthesis of a simple control law. The scheme is therefore expected to prove extremely useful in the area of nonlinear adaptive control. The effectiveness of the proposed scheme is demonstrated through application to various nonlinear models.
AB - The use of Intelligent control schemes in Nonlinear Model Based Control (NMBC) has gained widespread popularity. Neural Networks, in particular, have been used extensively to model the dynamics of nonlinear plants. However, in most cases, these models do not lend themselves to easy maneuvering for controller design.Therefore, a common need is being felt to develop intelligent control strategies that lead to computationally simple control laws. To achieve this objective, the present study combines the approximation power of Higher-Order Neural Networks (HONN) with the control-oriented nature of the recently developed U-model. By introducing the U-model equivalence of a Higher-Order Neural Unit (HONU), the control law synthesis part is reduced to a simple polynomial root-solving procedure. The proposed scheme is based on the robust Internal Model Control (IMC) structure and is suitable for stable nonlinear plants with uncertain dynamics. The main feature of the proposed structure is its ability to capture higher-order nonlinear properties of the input pattern space while allowing the synthesis of a simple control law. The scheme is therefore expected to prove extremely useful in the area of nonlinear adaptive control. The effectiveness of the proposed scheme is demonstrated through application to various nonlinear models.
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M3 - Conference contribution
AN - SCOPUS:40849135922
SN - 1424404568
SN - 9781424404568
BT - IEEE International Conference on Engineering of Intelligent Systems, ICEIS 2006
ER -