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
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TL;DR: In this article, an adaptive extremum seeking disturbance feedforward is designed that approximates the unknown, static mapping between the disturbances and the optimal inputs, which enables the extremum seek to be conducted in the proximity of the generator's extremum thus yielding improvements both in terms of accuracy and increased convergence speed compared to the traditional scheme.
11 citations
TL;DR: In this paper, a dead-zone non-linear control strategy is developed and implemented in an optical drive (CD-RW) to improve anti-shock performance during playing and recording.
11 citations
TL;DR: In this paper, the authors present a model independent of the actuator type used for actuation of continuous facesheet deformable mirrors, to study the design parameters such as: actuator spacing & coupling, influence function, peak-valley stroke, dynamical behavior: global & local, etc.
Abstract: Adaptive Optics is established as essential technology in current and future ground based (extremely) large telescopes to compensate for atmospheric turbulence. Deformable mirrors for astronomic purposes have a high number of actuators (> 10k), a relatively large stroke (> 10µm) on a small spacing ( 100Hz). The availability of piezoelectric ceramics as an actuator principle has driven the development of many adaptive deformable mirrors towards inappropriately stiff displacement actuation. This, while the use of force actuation supersedes piezos in performance and longevity while being less costly per channel by a factor of 10-20. This paper presents a model which is independent of the actuator type used for actuation of continuous facesheet deformable mirrors, to study the design parameters such as: actuator spacing & coupling, influence function, peak-valley stroke, dynamical behavior: global & local, etc. The model is validated using finite element simulations and its parameters are used to derive design fundamentals for optimization. © 2009 SPIE.
11 citations
04 Jun 2014
TL;DR: Recent developments in inferential control are utilized to arrive at control structures suited for inferential ILC, and proposed frameworks extend earlier results and encompass various controller structures.
Abstract: Since performance variables cannot be measured directly, Iterative Learning Control (ILC) is usually applied to measured variables. In this paper, it is shown that this can deteriorate performance. New batch-wise sensors that measure the performance variables directly are well-suited for use in ILC and can potentially improve performance. In this paper, recent developments in inferential control are utilized to arrive at control structures suited for inferential ILC. The proposed frameworks extend earlier results and encompass various controller structures. The results are supported with a simulation example.
11 citations
TL;DR: Recurring tasks like ‘rough/fine approach’ and ‘retreat’ covered more than 50% of the total task completion time and were identified as most promising for further improvement.
11 citations
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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