|COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring.|
Predictive control of mechatronic systems
If you have a question about this talk, please contact Dr Jason Z JIANG.
Control of mechatronic systems poses typical challenges i.e. nonlinear and fast dynamics with unknown optimal reference trajectories which vary significantly in course of operation. In this context, learning control for production machines has recently garnered attention from the research community and special sessions in conferences and journals have been organized. We tackle such systems by a two-level learning control approach with (non)linear model-based control at lower level tracking references learned/adapted by a high level iterative leaning controller.
Moreover, many complex machines are non-collocated vibrating systems i.e. actuators and sensors do not act at the same point. We show that classical compensators are incapable to control the induced oscillations and that model-based controller synthesis and tuning for can deliver the required performance.
The above two methodologies are experimentally validated over a wet-clutch engagement and harvester height control. If time permits, new ideas on extensions of closed-loop formulations of MPC for constrained systems will be touched on.
This talk is part of the CUED Control Group Seminars series.
This talk is included in these lists:
Note that ex-directory lists are not shown.
Other listsIndividual in the Labour Market Research Group Beyond Academics PLACEB-O 'In Conversation' Seminar Series
Other talksOptical properties of exotic epitaxial nanostructures with respect to nanophotonic applications The unitary Fermi Gas Raising productivity in arable agriculture: The roles of NIAB and agronomic change A public lecture by Professor Samuel Moyn Recent advances in lattice materials Creativity, Circulation and Copyright: Sonic and Visual Media in the Digital Age