Improved State-Space Model Identification Using Input and Output Data with Steady State Values

Abstract

In this paper, we improve the deterministic off-line identification method (Kosaka et. al., 2002) that obtains a state-space model from input and output data with a constant steady state response. The method is composed of determining the parameters by zeroing the 0 ~ N-tuple integral values of output error between the plant and the model (Kosaka et. al., 2000) and by using Ho-Kalman's method (Zeiger et. al., 1974). The method theoretically has an assumption that plant system matrix A satisfies |A| < 1 because the method utilizes Taylor series expansion. But the method can be applied to plants without the assumption |A| < 1. The feature is that the method is performed by using a step response, which is suitable to a mechnical system where noise and vibration are undesirable, and that the identification result is accurate at low frequency range. Numerical simulations of multi-input multi-output system identification are illustrated.