Showing papers by "P.M.J. Van den Hof published in 2006"

Bang-Bang Control in Reservoir Flooding

Abstract: Various studies have shown that dynamic optimization of waterflooding using optimal control theory has a significant potential to increase Net Present Value (NPV). In these studies, gradient-based optimization methods are used, where the gradients are usually obtained with an adjoint formulation. However, the shape of the optimal injection and production settings is generally not known beforehand. The main contribution of this paper is to show that a whole variety of reservoir flooding problems can be formulated as optimal control problems that are linear in the control and that, if the only constraints are upper and lower bounds on the control, these problems will sometimes have bang-bang (on-off) optimal solutions. This is supported by a waterflooding example of a 3-dimensional reservoir in a fluvial depositional environment, modeled with 18.553 grid blocks. The valve settings of 8 injection and 4 production wells are optimized over the life of the reservoir, with the objective to maximize NPV. For various situations, the optimal solution is either bang-bang, or a bang-bang solution exists that is only slightly suboptimal. This has obvious practical implications, since bang-bang solutions can be implemented with simple on-off control valves.

Orthonormal basis selection for LPV system identification, the Fuzzy-Kolmogorov c-Max approach

Abstract: A fuzzy clustering approach is developed to select pole locations for Orthonormal Basis Functions (OBFs), used for identification of Linear Parameter Varying (LPV) systems. The identification approach is based on interpolation of locally identified Linear Time Invariant (LTI) models, using globally fixed OBFs. Selection of the optimal OBF structure, that guarantees the least worst-case local modelling error in an asymptotic sense, is accomplished through the fusion of the Kolmogorov n-width (KnW) theory and Fuzzy c-Means (FcM) clustering of observed sample system poles.

Experiment design for robust control: Why do more work than is needed?

Abstract: Experiment design for open-loop identification Optimal input design for system identification was an active area of research in the 1970’s, with different quality measures of the identified model being used for this optimal design [25, 33, 13]. The questions at that time addressed open-loop identification and the objective functions that were minimized were various measures of the parameter covariance matrix Pθ, where θ is the parameter vector of the model structure. Let the “true system” be given by: