"Today’s state-of-the art methods for system and control design are model based. The ever increasing demand for higher performance and efficiency pushes the systems in a nonlinear operation mode so that nonlinear models are required for their design and control. The model quality and the model building cost are becoming limiting factors for further technological developments.To close the gap between the designers and the modellers we propose a fundamentally new approach to deliver highly structured nonlinear models meeting the designer’s needs. From a theoretical point of view, the major contribution is the development of a new nonlinear structured system identification framework. From practical point of view, the new nonlinear modelling paradigm will become an enabling technology to further push the performance and efficiency of system and control design.We follow a three step strategy to identify structured nonlinear models:- A top down approach in which we develop data driven structure revealing methods starting from initial unstructured nonlinear state space models.- A bottom up approach that identifies complex block oriented models, including parallel and feedback structures starting from the best linear approximation of the nonlinear system. These models are highly structured from the start.- An new dedicated experiment design strategy will be developed to retrieve the “best” models with the least experimental cost.Solving these problems is far beyond the actual abilities of the system identification community. However, our long standing recognized experience in frequency domain system identification in the presence of nonlinear distortions, and recent work by the PI guarantee the feasibility of the project.Structured nonlinear model building has applications in traditional industrial and emerging new high technological applications, including biomechanical and biomedical applications."