Parallel Learning in Control Systems : Derivation of Multiple Eigenvalue Filter

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概要

• 論文の詳細を見る

• In this paper, the author proposes a method for improving control performance in feedback control systems by introducing a multiple eigenvalue filter which has been deduced from parallel learning models. First, the control system model is copied to i (i=1, 2,..., k) systems corresponding to learning times k. The actuating signal of the first model is added to the actuating signal of the second model, and then the actuating signal of the second model is added to the actuating signal of the third model. Likewise, the actuating signal of the k-1-th model is added to the actuating signal of the k-th model. The thus obtained k-th model is equivalent to a system which has a filter as a series compensator that is composed of the sum of i (i=1, 2,..., k-1) multiples of the left side of the characteristic equation. In this paper, the sum is called "multiple eigenvalue filter" and it is concluded that the filter can eliminate control variable deviation without losing stability when disturbance is imposed.

• 一般社団法人日本機械学会の論文
• 1996-06-15

著者

• Isomura S

  Department Of Mechanical Engineering Takamatsu National College Of Technology

• Isomura Shuichi

  Department Of Mechanical Engineering Takamatsu National College Of Technology

関連論文

• Stability of Parallel Learning Control Systems
• Parallel Learning in Control Systems : Derivation of Multiple Eigenvalue Filter

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