Hybrid system identification using a structural approach and its model based control: An experimental validation

For implementation of various model based techniques such as in control and fault diagnosis, data-driven identification is key for enabling cheap and rapid development of models of hybrid systems of industrial interest. In the present work, a novel identification method is proposed for a class of hybrid systems which are linear and separable in the discrete variables (that is discrete states and discrete inputs). The method takes cognizance of the fact that the separable structure of the hybrid system constrains the evolution of system dynamics. In particular, the proposed method identifies models corresponding to a certain number of modes, far fewer than the total possible modes of the system. It then generates the models for the remaining modes without any further requirement for input–output data by exploiting the separable structure of the hybrid system. We experimentally validate the method by identifying the model for a three tank benchmark hybrid system followed by model predictive control using the identified model.