Overview

After studying this chapter you will be able to

• derive the data equation that relates block Hankel matrices constructed from input-output data;

• exploit the special structure of the data equation for impulse input signals to identify a state-space model via subspace methods;

• use subspace identification for general input signals;

• use instrumental variables in subspace identification to deal with process and measurement noise;

• derive subspace identification schemes for various noise models;

• use the RQ factorization for a computationally efficient implementation of subspace identification schemes; and

• relate different subspace identification schemes via the solution of a least-squares problem.