Okko H. Bosgra
Other affiliations: Eindhoven University of Technology
Bio: Okko H. Bosgra is an academic researcher from Delft University of Technology. The author has contributed to research in topics: Robust control & Control theory. The author has an hindex of 31, co-authored 127 publications receiving 3526 citations. Previous affiliations of Okko H. Bosgra include Eindhoven University of Technology.
Papers published on a yearly basis
TL;DR: It is shown how to exploit these generalized basis functions to increase the speed of convergence in a series expansion, i.e., to obtain a good approximation by retaining only a finite number of expansion coefficients.
Abstract: In many areas of signal, system, and control theory, orthogonal functions play an important role in issues of analysis and design. In this paper, it is shown that there exist orthogonal functions that, in a natural way, are generated by stable linear dynamical systems and that compose an orthonormal basis for the signal space l/sub 2sup n/. To this end, use is made of balanced realizations of inner transfer functions. The orthogonal functions can be considered as generalizations of, for example, the pulse functions, Laguerre functions, and Kautz functions, and give rise to an alternative series expansion of rational transfer functions. It is shown how we can exploit these generalized basis functions to increase the speed of convergence in a series expansion, i.e., to obtain a good approximation by retaining only a finite number of expansion coefficients. Consequences for identification of expansion coefficients are analyzed, and a bound is formulated on the error that is made when approximating a system by a finite number of expansion coefficients. >
TL;DR: In this paper, the authors presented an approach to reduce the impact of geological uncertainties in the field development phase known as robust optimization (RO), which uses a set of realizations that reflect the range of possible geological structures honoring the statistics of the geological uncertainties.
Abstract: Dynamic optimization of water flooding using optimal control theory has a significant potential to increase ultimate recovery, as has been shown in various studies. However, optimal control strategies often lack robustness to geological uncertainties. We present an approach to reduce the impact of geological uncertainties in the field development phase known as robust optimization (RO). RO uses a set of realizations that reflect the range of possible geological structures honoring the statistics of the geological uncertainties. In our study we used 100 realizations of a 3dimensional reservoir in a fluvial depositional environment with known main flow direction. We optimized the rates of the 8 injection and 4 production wells over the life of the reservoir, with the objective to maximize the average net present value (NPV). We used a gradient-based optimization method where the gradients are obtained with an adjoint formulation. We compared the results of the RO procedure to two alternative approaches: a nominal optimization (NO) and a reactive control approach. In the reactive approach each production well is shut in when production is no longer profitable. The NO procedure is based on a single realization. In our study, it is performed on each of the 100 realizations in the set individually, resulting in 100 different NO production strategies. The control strategies were applied to each realization, from which the average NPV's, the standard deviation, the cumulative distribution functions and the probability density functions were determined. The RO results displayed a much smaller variance than the alternatives, indicating an increased robustness to geological uncertainty. Moreover, the RO procedure significantly improved the expected NPV compared to the alternative methods: on average 9.5% higher than using reactive control and 5.9% higher than the average of the nominal optimization strategies.
TL;DR: In this article, a linear parameter varying (LPV) control technique was proposed for position-dependent controllers that adapt themselves in order to achieve optimal closed-loop performance in IC-manufacturing.
TL;DR: In this paper, the authors present a control concept that iteratively improves the transient behaviour of processes that are repetitive in nature, which is called iterative learning control (ILC).
Abstract: Iterative Learning Control (ILC) is a powerful control concept that iteratively improves the transient behaviour of processes that are repetitive in nature. Although most of the published ILC schem...
TL;DR: In this article, a practically feasible procedure to design MIMO feedback controllers for electromechanical positioning devices, using H ∞ /μ techniques, is presented, where weighting filters are proposed to straightforwardly and effectively impose performance and uncertainty specifications.
TL;DR: Though beginning its third decade of active research, the field of ILC shows no sign of slowing down and includes many results and learning algorithms beyond the scope of this survey.
Abstract: This article surveyed the major results in iterative learning control (ILC) analysis and design over the past two decades. Problems in stability, performance, learning transient behavior, and robustness were discussed along with four design techniques that have emerged as among the most popular. The content of this survey was selected to provide the reader with a broad perspective of the important ideas, potential, and limitations of ILC. Indeed, the maturing field of ILC includes many results and learning algorithms beyond the scope of this survey. Though beginning its third decade of active research, the field of ILC shows no sign of slowing down.
••01 Nov 2007
TL;DR: The iterative learning control (ILC) literature published between 1998 and 2004 is categorized and discussed, extending the earlier reviews presented by two of the authors.
Abstract: In this paper, the iterative learning control (ILC) literature published between 1998 and 2004 is categorized and discussed, extending the earlier reviews presented by two of the authors. The papers includes a general introduction to ILC and a technical description of the methodology. The selected results are reviewed, and the ILC literature is categorized into subcategories within the broader division of application-focused and theory-focused results.
TL;DR: A classification of a number of decentralized, distributed and hierarchical control architectures for large scale systems is proposed and attention is focused on the design approaches based on Model Predictive Control.
•27 Jul 2017
TL;DR: Predictive Control for Linear and Hybrid Systems is an ideal reference for graduate, postgraduate and advanced control practitioners interested in theory and/or implementation aspects of predictive control.
Abstract: Model Predictive Control (MPC), the dominant advanced control approach in industry over the past twenty-five years, is presented comprehensively in this unique book. With a simple, unified approach, and with attention to real-time implementation, it covers predictive control theory including the stability, feasibility, and robustness of MPC controllers. The theory of explicit MPC, where the nonlinear optimal feedback controller can be calculated efficiently, is presented in the context of linear systems with linear constraints, switched linear systems, and, more generally, linear hybrid systems. Drawing upon years of practical experience and using numerous examples and illustrative applications, the authors discuss the techniques required to design predictive control laws, including algorithms for polyhedral manipulations, mathematical and multiparametric programming and how to validate the theoretical properties and to implement predictive control policies. The most important algorithms feature in an accompanying free online MATLAB toolbox, which allows easy access to sample solutions. Predictive Control for Linear and Hybrid Systems is an ideal reference for graduate, postgraduate and advanced control practitioners interested in theory and/or implementation aspects of predictive control.