z-logo
open-access-imgOpen Access
Infinite-dimensional nonlinear predictive control design for open-channel hydraulic systems
Author(s) -
Didier Georges
Publication year - 2009
Publication title -
networks and heterogeneous media
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.732
H-Index - 34
eISSN - 1556-181X
pISSN - 1556-1801
DOI - 10.3934/nhm.2009.4.267
Subject(s) - model predictive control , nonlinear system , shallow water equations , computer science , channel (broadcasting) , mathematics , control theory (sociology) , operator (biology) , mathematical optimization , flow (mathematics) , control (management) , mathematical analysis , geometry , physics , quantum mechanics , artificial intelligence , computer network , biochemistry , chemistry , repressor , transcription factor , gene
A nonlinear predictive control design based on Saint Venant equations is presented in this paper in order to regulate both water depth and water flow rate in a single pool of an open-channel hydraulic system. Thanks to variational calculus, some necessary optimality conditions are given. The adjoint partial differential equations of Saint Venant partial differential equations are also derived. The resulting two-point boundary value problem is solved numerically by using both time and space discretization and operator approximations based on nonlinear time-implicit finite differences. The practical effectiveness of the control design is demonstrated by a simulation example. A extension of the predictive control scheme to a multi-pool system is proposed by using a decomposition-coordination approach based on two-level algorithm and the use of an augmented Lagrangian, which can take advantage of communication networks used for distributed control. This approach may be easily applied to other problems governed by hyperbolic PDEs, such as road traffic systems.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom