制御系の並列学習 : 重根フィルタの提案

抄録

This paper proposes a method that improves control performance in feedback control systems by introducing a multiple eigenvalue filter which has been deduced from parallel learning models. First, the controlled system model is copied to i (i=1, 2, …, k) systems corresponding to learning times. 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. Thus obtained k-th models are equivalent to the system which has a filter as a series compensator composed of the sum of i (i=0, 1, 2, …, k-1) multiple of the left side of the characteristic equation. In this paper, the sum is called a "multiple eigenvalue filter" and it is concluded that the filter is effective to eliminate control variable deviation without losing stability when disturbance is imposed.