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.
UR - http://www.scopus.com/inward/record.url?scp=40849135922&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=40849135922&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:40849135922
SN - 1424404568
SN - 9781424404568
T3 - IEEE International Conference on Engineering of Intelligent Systems, ICEIS 2006
BT - IEEE International Conference on Engineering of Intelligent Systems, ICEIS 2006
T2 - IEEE International Conference on Engineering of Intelligent Systems, ICEIS 2006
Y2 - 22 April 2006 through 23 April 2006
ER -