Model based control and calibration for powertrain applications
The diesel engine in a submarine is subject to time varying exhaust manifold pressure disturbances caused by waves, which leads to undesirable surging of engine speed and temperature when a conventional speed governor approach is used to schedule the engine inputs. Simultaneously, within the automotive community there has been considerable interest in the deveolpment of model based controllers to ensure tighter performance specifications (in terms of fuel economy and emissions) can be met in highly actuated engines.
This project considers the development of novel model predictive controllers that are computationally tractable and able to guarantee constraint satisfaction in the presence of model uncertainty. To achieve this aim, new results on model reduction and problems formulation have been formatted and will continue to be refined over the course of the project.
As model based controllers typically have greater degrees of freedom than conventional in situ contorl architectures, we are also investigating tools that will alleviate the calibration burden associated with deploying MPC in production, as well as multi-objective optimisation approaches for the selection of structural parameters within the controller.
Students interested in working on this project will have a strong background in control systems, ideally with knowledge in model predictive control and the desire/experience with implementing controllers in experimental rigs.
Leader: Chris Manzie
Staff: Rohan Shekhar Michael Brear
Students: Gokul Sankar Tim Broomhead Noam Olshina Will Clarke
Collaborators: Peter Hield (DSTG) Hayato Nakada (TMC)
Sponsors: Defence Science and Technology Group Toyota Motor Company
Optimisation of resources and infrastructure
applied control theory; control and signal processing; optimisation; stationary power generation