Sampled-data model validation: An algorithm and experimental application

The application of robust control theory requires representative models containing unknown bounded perturbations and unknown bounded noise/disturbance signals. Model validation is a means of assessing the applicability of a given model with respect to experimental data. We consider a sampled-data approach, using a continuous time model, including unknown perturbations and signals, and a discrete experimental datum of finite length. The sampled-data model validation problem can be formulated as a linear matrix inequality problem. A computationally tractable algorithm, which employs data decimation and exploits the problem structure, is presented in the paper. This method is applied to a 2-D heating experiment.