Performance-optimized applied identification of separable distributed-parameter processes

Studies practical algorithms for parametric identification of cross-directional processes from input/output data. Instead of working directly with the original two-dimensional array of the high-resolution profile scans, the proposed algorithms use separation properties of the problem. It is demonstrated that by estimating and identifying in turn cross directional and time responses of the process, it is possible to obtain unbiased least-square error estimates of the model parameters. At each step, a single data sequence is used for identification which ensures high computational performance of the proposed algorithm. A theoretical proof of algorithm convergence is presented. The discussed algorithms are implemented in an industrial identification tool and the note includes a real-life example using paper machine data.