Hybrid Modeling for Mechanical Systems: Methodologies and Applications

Abstract

A serial hybrid modeling approach is applied to mechanical systems. Here, hybrid means that models are based on combined structural and empirical approaches. The main system behavior is described by a physical model, while complex internal forces are modeled by black box neural networks. For a special class of systems this methodology is extended and a novel approach is presented modeling the whole system behavior by hierarchical neural networks, that fit the relation between system outputs and internal system variables. Useful information about the nonlinear system can be extracted from the resulting models. The power of hybrid modeling is illustrated with experimental results and some important issues considering the practical implementation are dealt with.