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Details of Grant 

EPSRC Reference: GR/N00036/01
Title: EFFICIENT SUBOPTIMAL NONLINEAR MODEL PREDICTIVE CONTROL
Principal Investigator: Cannon, Professor MR
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department: Engineering Science
Organisation: University of Oxford
Scheme: Standard Research (Pre-FEC)
Starts: 01 September 2000 Ends: 28 February 2002 Value (£): 54,455
EPSRC Research Topic Classifications:
Control Engineering
EPSRC Industrial Sector Classifications:
No relevance to Underpinning Sectors
Related Grants:
Panel History:  
Summary on Grant Application Form
Conventional nonlinear model predictive control (NMPC) algorithms require the solution of a nonconvex nonlinear programming problem at each sampling instant. The proposed project aims to overcome the severe computational problems associated with nonconvex optimisation by developing suboptimal NMPC strategies. These will be based on two related approaches: (1) a closed-loop paradigm which transfers most of the computational burden to offline calculations through the use of approximate constraints and a feedback law which is optimal in the unconstrained case; (2) an incremental optimisation method which reduces the computation at each sample instant to a trivially solvable subproblem. Both methods ensure closed-loop stability and converge to conditions for optimality asymptotically over time. A further aspect of the project concerns the design of unconstrained stabilising feedback laws for the determination of closed-loop prediction models. This will involve the development of design methods based on control Lyapunov functions and classical optimal control theory. The derived algorithms will be applied to simulated industrial problems and laboratory-scale test-rigs.
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Organisation Website: http://www.ox.ac.uk