IDENTIFICATION OF FULLY PARAMETERIZED LINEAR AND NONLINEAR STATE-SPACE SYSTEMS BY PROJECTED GRADIENT SEARCH

摘要

A nonlinear optimization-based identification procedure for fully parameterized multivariable state-space models is presented. The method can be used to identify linear time-invariant, linear parameter-varying, composite local linear, bilinear, Hamerstein and Wiener systems. The nonuniqueness of the full parameterization is dealt with by a projected gradient search to solve the nonlinear optimization problem. Both white and nonwhite measurement noise at the output can be dealt with in a maximum likelihood setting. It is proposed to use subspace identification methods to initialize the nonlinear optimization problem. A computationally efficient and numerically reliable implementation of the procedure is discussed in detail.