A new control method of nonlinear systems based on impulse responses of universal learning networks

Kotaro Hirasawa, Takayuki Furuzuki, Junichi Murata, Chunzhi Jin

研究成果: Article

3 引用 (Scopus)

抄録

A new control method of nonlinear dynamic systems is proposed based on the impulse responses of universal learning networks (ULNs). ULNs form a superset of neural networks. They consist of a number of interconnected nodes where the nodes may have any continuously differentiable nonlinear functions in them and each pair of nodes can be connected by multiple branches with arbitrary time delays. A generalized learning algorithm is derived for the ULNs, in which both the first order derivatives (gradients) and the higher order derivatives are incorporated. One of the distinguished features of the proposed control method is that the impulse response of the systems is considered as an extended part of the criterion function and it can be calculated by using the higher order derivatives of ULNs. By using the impulse response as the criterion function, nonlinear dynamics with not only quick response but also quick damping and small steady state error can be more easily obtained than the conventional nonlinear control systems with quadratic form criterion functions of state and control variables.

元の言語English
ページ(範囲)362-372
ページ数11
ジャーナルIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
31
発行部数3
DOI
出版物ステータスPublished - 2001 6
外部発表Yes

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Impulse response
Nonlinear systems
Derivatives
Nonlinear control systems
Learning algorithms
Time delay
Dynamical systems
Damping
Neural networks

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Artificial Intelligence
  • Human-Computer Interaction

これを引用

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AU - Murata, Junichi

AU - Jin, Chunzhi

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N2 - A new control method of nonlinear dynamic systems is proposed based on the impulse responses of universal learning networks (ULNs). ULNs form a superset of neural networks. They consist of a number of interconnected nodes where the nodes may have any continuously differentiable nonlinear functions in them and each pair of nodes can be connected by multiple branches with arbitrary time delays. A generalized learning algorithm is derived for the ULNs, in which both the first order derivatives (gradients) and the higher order derivatives are incorporated. One of the distinguished features of the proposed control method is that the impulse response of the systems is considered as an extended part of the criterion function and it can be calculated by using the higher order derivatives of ULNs. By using the impulse response as the criterion function, nonlinear dynamics with not only quick response but also quick damping and small steady state error can be more easily obtained than the conventional nonlinear control systems with quadratic form criterion functions of state and control variables.

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