Author
M Maarten Steinbuch
Other affiliations: Nanyang Technological University, Delft University of Technology, Bosch ...read more
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.
Papers published on a yearly basis
Papers
More filters
TL;DR: In this article, a carriage drive for wide format printing systems (WFPS) is proposed, which reverses the carriage by buffering the kinetic energy of the carriage resulting in a fast reversal and a high energy efficiency for higher productive WFPS.
1 citations
01 Jan 2009
TL;DR: A new traceable metrological AFM with a measuring volume of 1x1x1 mm is being developed for the Dutch metrology institute (NMi - Van Swinden Laboratory).
Abstract: A new traceable metrological AFM with a measuring volume of 1x1x1 mm is being developed for the Dutch metrology institute (NMi - Van Swinden Laboratory) The instrument consists of a low-hysteresis elastic translation stage and a custom designed, kinematically mounted AFM measurement head Here a short overview of the translation stage design will be given and some preliminary results presented
1 citations
TL;DR: In this paper, a switching robust controller that guarantees both stability and transparency for a predefined range of environment stiffness is proposed, taking into account during the design process the fact that environment estimators will have limited accuracy due to noise and uncertainty.
1 citations
01 Jan 2015
TL;DR: The final author version and the galley proof are versions of the publication after peer review that features the final layout of the paper including the volume, issue and page numbers.
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.
1 citations
01 Jan 2009
TL;DR: In this article, an island track-and-kill system is proposed to deal with both the identification of magnetic island and its position in the plasma, as well as with the steering of the ECRH beam in order to deposit the power at the magnetic island.
Abstract: High power electron cyclotron waves (ECRH or microwaves) are recognized as an ideal tool for control of plasma profiles and instabilities, like magnetic islands, where one exploits their effect on the plasma in terms of localized heating and current drive. Deposition of the ECRH power onto the magnetic instability must be performed precisely within certain accuracy. Misalignment of the ECRH power deposition w.r.t. the magnetic island reduces the effectiveness of its suppression. An island track-and-kill s ystem must therefore deal both with the identification of the magnetic island and its position in the plasma, as well as with the steering of the ECRH beam in order to deposit the power at the magnetic island. Note that such system has a typical feedback control structure where the necessary detection and control actions must be performed in real-time within tenth of milliseconds, while the tracking of the ECRH wave beam w.r.t. the island is subject to disturbances.
1 citations
Cited by
More filters
Book•
05 Oct 1997TL;DR: In this article, the authors introduce linear algebraic Riccati Equations and linear systems with Ha spaces and balance model reduction, and Ha Loop Shaping, and Controller Reduction.
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.
3,471 citations
TL;DR: In this paper, a review of electrical energy storage technologies for stationary applications is presented, with particular attention paid to pumped hydroelectric storage, compressed air energy storage, battery, flow battery, fuel cell, solar fuel, superconducting magnetic energy storage and thermal energy storage.
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.
3,031 citations
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.
2,645 citations
27 Jun 2016
TL;DR: This work proposes an LSTM model which can learn general human movement and predict their future trajectories and outperforms state-of-the-art methods on some of these datasets.
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.
2,587 citations
TL;DR: This article attempts to strengthen the links between the two research communities by providing a survey of work in reinforcement learning for behavior generation in robots by highlighting both key challenges in robot reinforcement learning as well as notable successes.
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.
2,391 citations