Autonomous Behaviour and Learning in an Uncertain World
Lead Research Organisation:
UNIVERSITY OF OXFORD
Department Name: Engineering Science
Abstract
Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.
Publications
Ahmadi M
(2015)
A convex approach to hydrodynamic analysis
Ahmadi M
(2015)
Barrier functionals for output functional estimation of PDEs
Ahmadi M
(2017)
Safety verification for distributed parameter systems using barrier functionals
in Systems & Control Letters
Ahmadi M
(2016)
Dissipation inequalities for the analysis of a class of PDEs
in Automatica
Ahmadi M
(2014)
Barrier Functionals for Output Functional Estimation of PDEs
Ahmadi M
(2015)
A Convex Approach to Hydrodynamic Analysis
Anderson J
(2012)
Robust nonlinear stability and performance analysis of an F/A-18 aircraft model using sum of squares programming
in International Journal of Robust and Nonlinear Control
Anghel M
(2013)
Algorithmic Construction of Lyapunov Functions for Power System Stability Analysis
in IEEE Transactions on Circuits and Systems I: Regular Papers
| Description | 1) Sparse efficient sampling based on informatics criteria can provide stable control algorithms and enable scalable multi-agent coordination. 2) Control mechanisms may be learned from data without a physical mechanism known 3) Guarantees of stability may be derived for probabilistic control methods 4) Bayesian optimisation allows for rapid learning of unknown functions. |
| Exploitation Route | via existing industry partners industry partners & academic publication. |
| Sectors | Aerospace Defence and Marine Digital/Communication/Information Technologies (including Software) Energy Other |
| URL | http://www.robots.ox.ac.uk/~parg/aisp |
| Description | Our control models have been integrated into drilling simulation by the industrial partner Schlumberger. These models show how, using sparse data, we can effectively use AI techniques to improve reliable control model creation - offering formal some guarantees as well. The extensions of this work can be useful in many areas, from finance to autonomous vehicles. |
| First Year Of Impact | 2014 |
| Sector | Energy,Financial Services, and Management Consultancy |
| Impact Types | Economic |
| Title | active sampling for control systems |
| Description | sparse observations for active control |
| Type Of Material | Computer model/algorithm |
| Year Produced | 2013 |
| Provided To Others? | No |
| Description | Collaboration with Schlumberger |
| Organisation | Schlumberger Limited |
| Department | Schlumberger Cambridge Research |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | working closely to ensure industrial relevance and disseminate materials |
| Collaborator Contribution | providing data and expertise - funding two studentships as a knock on from this project |
| Impact | papers, software and know-how |
| Start Year | 2012 |