Two-stage identification and its application to robust LQG controller design

Consider an unknown, possibly unstable and linear time-invariant (LTI) system with output disturbance. Based on the concept of learning identification introduced in Teo et al. (1995) and the iterative scheme proposed in Zang et al. (1992), a new scheme for mutually enhanced data-based least squares identification and model-based LQG robust control design is presented. One of the novel features of this scheme is that both the identification and the robust control design tasks can be performed by minimizing a single cost function. To illustrate the effectiveness of the proposed scheme, a numerical example is presented.