Some recent publications of our research group are featured on this page. For complete publication lists, visit our Google scholar pages or contact us!
Data-driven feedback linearisation using model predictive control
Linearising the dynamics of nonlinear mechanical systems is an important and open research area. In this paper, we adopt a data-driven and feedback control approach to tackle this problem. A model predictive control architecture is developed that builds upon data-driven dynamic models obtained using nonlinear system identification.
Video analysis of nonlinear systems with extended Kalman filtering for modal identification
This study proposes to carry out the experimental modal analysis of nonlinear systemsunder the assumption of almost invariant modal shapes by coupling video analysisfrom a high speed/resolution camera and extended Kalman filtering. A clamped-clamped beam with a local nonlinearity is considered, and its vibrations are measured by detecting and tracking a large set of (virtual) sensors bonded to the beam outer surface. Specific image processing and video tracking techniques are employed.
Nonlinear dynamic model updating and upgrading using sine-sweep vibration data
Dynamic modelling is a core activity in mechanical engineering. It is typically carried out in a computational environment, involving idealising assumptions of diverse kinds. The most notable assumption commonly adopted in the field is the excitation-to-response linearity of the mechanical vibrations. This common practice contrasts with the day-to-day experience of test engineers. In this paper, a coherent set of techniques is described to locate, characterise and model nonlinearities using sine-sweep vibration data.