Nonlinear programming strategies for state estimation and model pbkp_redictive control

Abstract:

Sensitivity-based strategies for on-line moving horizon estimation (MHE) and nonlinear model pbkp_redictive control (NMPC) are presented both from a stability and computational perspective. These strategies make use of full-space interior-point nonlinear programming (NLP) algorithms and NLP sensitivity concepts. In particular, NLP sensitivity allows us to partition the solution of the optimization problems into background and negligible on-line computations, thus avoiding the problem of computational delay even with large dynamic models. We demonstrate these developments through a distributed polymerization reactor model containing around 10,000 differential and algebraic equations (DAEs). © 2009 Springer Berlin Heidelberg.

Año de publicación:

2009

Keywords:

• Interior-point methods
• Large-scale
• Sparse linear algebra
• sensitivity
• NMPC
• nonlinear programming
• MHE

Fuente:

scopus