Multivariable process identification based on frequency domain measures

Multi-input multi-output (MIMO) process identification is studied, where the purpose of identification is control system design. An identification procedure is presented by which one can estimate not only a nominal parametric process model, but also an upper bound of the model errors in the frequency domain. The basic steps of this method consists of high-order model estimation and subsequent model reduction. In this framework, fundamental problems such as input design and model structure selection can easily be solved. The method is also numerically simple and reliable. A simulation example is given to illustrate the method.<>