A projection approach to covariance equivalent realizations of discrete systems Academic Article uri icon

abstract

  • Covariance equivalent realization theory has been used recently in continuous systems for model reduction [1]-[4] and controller reduction [2], [5]. In model reduction, this technique produces a reduced-order model that matches q + 1 output covariances and q Markov parameters of the full-order model. In controller reduction, it produces a reduced controller that is close to matching q + 1 output covariances of the full-order controller, and q Markov parameters of the closed-loop system. For discrete systems, a method was devised to produce a reduced-order model that matches the q + 1 covariances [6] but not any Markov parameters. This method requires a factorization to obtain the input matrix, and since the dimension of this matrix factor depends upon rank properties not known a priori, this method may not maintain the original dimension of the input vector. Hence, this method [6] is obviously not suitable for controller reduction. A new projection method is described that matches q covariances and q Markov parameters of the original system while maintaining the correct dimension of the input vector. 1986 IEEE

published proceedings

  • IEEE Transactions on Automatic Control

author list (cited authors)

  • Wagie, D., & Skelton, R.

citation count

  • 46

complete list of authors

  • Wagie, D||Skelton, R

publication date

  • December 1986