Author
Henk B. Verbruggen
Bio: Henk B. Verbruggen is an academic researcher from Delft University of Technology. The author has contributed to research in topics: Fuzzy logic & Fuzzy control system. The author has an hindex of 31, co-authored 148 publications receiving 3258 citations.
Papers published on a yearly basis
Papers
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TL;DR: An overview of neuro-fuzzy modeling methods for nonlinear system identification is given, with an emphasis on the tradeoff between accuracy and interpretability.
299 citations
TL;DR: Methods for constructing fuzzy models from process data are reviewed, and attention is paid to the choice of a suitable fuzzy model structure for the identification task.
288 citations
TL;DR: Comparisons with a nonlinear predictive control scheme based on iterative numerical optimization show that the proposed method requires fewer computations and achieves better performance.
154 citations
TL;DR: Recent work focusing on the use of Takagi–Sugeno fuzzy models in combination with MBPC is described, including a branch-and-bound method with iterative grid-size reduction and control based on a local linear model.
139 citations
TL;DR: This paper addresses the optimization in fuzzy model predictive control with four different methods for the construction of the optimization problem, making difference between the cases when a single linear model or a set of linear models are used.
Abstract: This paper addresses the optimization in fuzzy model predictive control. When the prediction model is a nonlinear fuzzy model, nonconvex, time-consuming optimization is necessary, with no guarantee of finding an optimal solution. A possible way around this problem is to linearize the fuzzy model at the current operating point and use linear predictive control (i.e., quadratic programming). For long-range predictive control, however, the influence of the linearization error may significantly deteriorate the performance. In our approach, this is remedied by linearizing the fuzzy model along the predicted input and output trajectories. One can further improve the model prediction by iteratively applying the optimized control sequence to the fuzzy model and linearizing along the so obtained simulated trajectories. Four different methods for the construction of the optimization problem are proposed, making difference between the cases when a single linear model or a set of linear models are used. By choosing an appropriate method, the user can achieve a desired tradeoff between the control performance and the computational load. The proposed techniques have been tested and evaluated using two simulated industrial benchmarks: pH control in a continuous stirred tank reactor and a high-purity distillation column.
131 citations
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Book•
01 Jan 2009TL;DR: A survey of previous comparisons and theoretical work descriptions of methods dataset descriptions criteria for comparison and methodology (including validation) empirical results machine learning on machine learning can be found in this article, where the authors also discuss their own work.
Abstract: Survey of previous comparisons and theoretical work descriptions of methods dataset descriptions criteria for comparison and methodology (including validation) empirical results machine learning on machine learning.
2,325 citations
TL;DR: An overview of the literature concerning positively invariant sets and their application to the analysis and synthesis of control systems is provided.
2,186 citations
TL;DR: A survey on recent developments (or state of the art) of analysis and design of model based fuzzy control systems based on the so-called Takagi-Sugeno fuzzy models or fuzzy dynamic models.
Abstract: Fuzzy logic control was originally introduced and developed as a model free control design approach. However, it unfortunately suffers from criticism of lacking of systematic stability analysis and controller design though it has a great success in industry applications. In the past ten years or so, prevailing research efforts on fuzzy logic control have been devoted to model-based fuzzy control systems that guarantee not only stability but also performance of closed-loop fuzzy control systems. This paper presents a survey on recent developments (or state of the art) of analysis and design of model based fuzzy control systems. Attention will be focused on stability analysis and controller design based on the so-called Takagi-Sugeno fuzzy models or fuzzy dynamic models. Perspectives of model based fuzzy control in future are also discussed
1,575 citations
Book•
30 Apr 1998
TL;DR: Fuzzy Modeling for Control addresses fuzzy modeling from the systems and control engineering point of view and focuses on the selection of appropriate model structures, on the acquisition of dynamic fuzzy models from process measurements, and on the design of nonlinear controllers based on fuzzy models.
Abstract: From the Publisher:
Fuzzy Modeling for Control addresses fuzzy modeling from the systems and control engineering point of view. It focuses on the selection of appropriate model structures, on the acquisition of dynamic fuzzy models from process measurements (fuzzy identification), and on the design of nonlinear controllers based on fuzzy models. The main features of the presented techniques are illustrated by means of simple examples. In addition, three real-world applications are described. Finally, software tools for building fuzzy models from measurements are available from the author.
1,183 citations
TL;DR: In this article, a review of the past and recent developments in system identification of nonlinear dynamical structures is presented, highlighting their assets and limitations and identifying future directions in this research area.
1,000 citations