M Maarten Steinbuch

Bio: M Maarten Steinbuch is an academic researcher from Eindhoven University of Technology. The author has contributed to research in topics: Control theory & Feed forward. The author has an hindex of 51, co-authored 630 publications receiving 11892 citations. Previous affiliations of M Maarten Steinbuch include Nanyang Technological University & Delft University of Technology.

Stability analysis of velocity feedback for the plasma vertical instability in fusion tokamaks

Abstract: The source of the vertical instability in tokamaks is relatively well understood, and stabilizing controllers have been successfully implemented in numerous tokamaks. Usually these controllers are designed based on a plasma model that assumes the plasma has zero mass. In reality the plasma mass is small but positive, so that some of the controller designs yield an unstable closed loop. In this work we expand on [1], further investigating the discrepancy between the massless plasma model and the plasma with mass model. It turns out that conclusions about closed loop behavior depend on the controller’s asymptotic behavior at infinite frequency. Simulations on models of KSTAR and ITER are performed with varying presence of passive stabilizing structures, including the possibility of superconductive control coils. It is shown that erroneous conclusions regarding asymptotic stabilization only result from non proper controllers. The results confirm that neither pure velocity feedback nor a proper or strictly proper form of velocity feedback can asymptotically stabilize the vertical instability.

Numerically reliable identification of complex systems

Abstract: • A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers.

An analogy between the Van der Pol equation and the Greitzer model

Abstract: This paper presents the idea to exploit the similarity between the Van der Pol equation and the Greitzer lumped parameter model. The Greitzer model is a widely used nonlinear model to describe surge transients in turbocompressors. One of the difficulties in applying this model is the identification of the model parameters. Usually, a priori knowledge is combined with a tuning procedure for the model parameters to match simulation results with experimental data. In contrast to the Greitzer model, there are various analytical approximations available for the period time of the Van der Pol oscillator. We propose to use the similarity with the Van der Pol equation and apply the available approximations for the identification of the model parameters in the Greitzer model. In this paper we will focus on demonstrating the similarity between both models. For this purpose we will introduce a coordinate transformation for the Greitzer model. In the subsequent parameter study we show that the transformed model exhibits the same qualitative behavior as the original compression system model. Furthermore, the parameter study reveals a linear relation between the most important model parameter and the limit cycle period time. These results form a solid basis for further research into exploiting the similarity with the Van der Pol system.

Motion control by linear feedback methods

Abstract: Maarten Steinbuch Technische Universiteit Eindhoven 15.

Essentials of Robust Control

Abstract: 1. Introduction. 2. Linear Algebra. 3. Linear Systems. 4. H2 and Ha Spaces. 5. Internal Stability. 6. Performance Specifications and Limitations. 7. Balanced Model Reduction. 8. Uncertainty and Robustness. 9. Linear Fractional Transformation. 10. m and m- Synthesis. 11. Controller Parameterization. 12. Algebraic Riccati Equations. 13. H2 Optimal Control. 14. Ha Control. 15. Controller Reduction. 16. Ha Loop Shaping. 17. Gap Metric and ...u- Gap Metric. 18. Miscellaneous Topics. Bibliography. Index.

Progress in electrical energy storage system: A critical review

Abstract: Electrical energy storage technologies for stationary applications are reviewed. Particular attention is paid to pumped hydroelectric storage, compressed air energy storage, battery, flow battery, fuel cell, solar fuel, superconducting magnetic energy storage, flywheel, capacitor/supercapacitor, and thermal energy storage. Comparison is made among these technologies in terms of technical characteristics, applications and deployment status.

A survey of iterative learning control

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.

Social LSTM: Human Trajectory Prediction in Crowded Spaces

Abstract: Pedestrians follow different trajectories to avoid obstacles and accommodate fellow pedestrians. Any autonomous vehicle navigating such a scene should be able to foresee the future positions of pedestrians and accordingly adjust its path to avoid collisions. This problem of trajectory prediction can be viewed as a sequence generation task, where we are interested in predicting the future trajectory of people based on their past positions. Following the recent success of Recurrent Neural Network (RNN) models for sequence prediction tasks, we propose an LSTM model which can learn general human movement and predict their future trajectories. This is in contrast to traditional approaches which use hand-crafted functions such as Social forces. We demonstrate the performance of our method on several public datasets. Our model outperforms state-of-the-art methods on some of these datasets. We also analyze the trajectories predicted by our model to demonstrate the motion behaviour learned by our model.

Reinforcement learning in robotics: A survey

Abstract: Reinforcement learning offers to robotics a framework and set of tools for the design of sophisticated and hard-to-engineer behaviors. Conversely, the challenges of robotic problems provide both inspiration, impact, and validation for developments in reinforcement learning. The relationship between disciplines has sufficient promise to be likened to that between physics and mathematics. In this article, we attempt to strengthen the links between the two research communities by providing a survey of work in reinforcement learning for behavior generation in robots. We highlight both key challenges in robot reinforcement learning as well as notable successes. We discuss how contributions tamed the complexity of the domain and study the role of algorithms, representations, and prior knowledge in achieving these successes. As a result, a particular focus of our paper lies on the choice between model-based and model-free as well as between value-function-based and policy-search methods. By analyzing a simple problem in some detail we demonstrate how reinforcement learning approaches may be profitably applied, and we note throughout open questions and the tremendous potential for future research.