scispace - formally typeset
Search or ask a question
Author

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.


Papers
More filters
01 Jan 2003
TL;DR: In this paper, a model that describes the 3DOF dynamics of a passively levitated electro-dynamic maglev system is presented, based on the flux-current-force interactions and the geometric relationships between the levitation coils and the permanent magnets on the sled.
Abstract: A model that describes the 3-DOF dynamics of a passively levitated electro-dynamic maglevsystem is presented. The model is based on the flux-current-force interactions and the geometricrelationships between the levitation coils and the permanent magnets on the sled. The model ispresented in a parametric state-space formulation, suitable to extract model parameters frominput-output measurements in a minimum mean square error sense, and model predictions arecompared with measured trajectories in height, pitch and roll. The proposed structure is very wellsuited to later develop robust feedback control of the sled dynamics.

5 citations

Journal ArticleDOI
TL;DR: In this article, an active printhead alignment mechanism for multi-printhead inkjet printers is presented, where the position of each printhead can be independently controlled without tightening the manufacturing tolerances.

5 citations

Proceedings Article
01 Jan 2004
TL;DR: In this article, the problems of robot modelling and identification for high-performance model-based motion control were discussed, and a derivation of robot kinematic and dynamic models was explained using a writing task to establish correctness of the models.
Abstract: This chapter deals with the problems of robot modelling and identification for high-performance model-based motion control A derivation of robot kinematic and dynamic models was explained Modelling of friction effects was also discussed Use of a writing task to establish correctness of the models was suggested Guidelines for design of an exciting identification trajectory were given A Kalman filtering technique for on-line reconstruction of joint motions, speeds, and accelerations was explained A straightforward but efficient estimation of parameters of the rigid-body dynamic model with friction effects was described The effectiveness of the procedure was experimentally demonstrated on a direct-drive robot with three revolute joints For this robot, models of kinematics and rigid-body dynamics were derived in closed-form, and presented in full detail The correctness of the models was established in simulation Results of experimental estimation of the dynamic model parameters were presented The appropriateness of the dynamic model for model-based control purposes was verified However, it was also indicated that this model is still not sufficient for a perfect match to the real robot dynamics, as these dynamics may contain more effects than covered by the rigid-body model A procedure to identify the dynamics not covered by the rigid-body model was proposed With these additional dynamics available, more advanced feedback control designs become possible

5 citations

Proceedings ArticleDOI
01 Jan 2000
TL;DR: In this article, the authors present the results of a single-input/dual-output (SIDO) control design for a high performance optical drive, which consists of a dual input/single-output actuator: both a sledge actuator and a rotating mirror contribute to a radial movement of the spot on the disk.
Abstract: This paper presents the results of a single-input/dual-output (SIDO) control design for a high performance optical drive. The optical drive consists of a dual-input/single-output (DISO) actuator: both a sledge actuator and a rotating mirror contribute to a radial movement of the spot on the disk. The used controller design method is a modified version of the PQ design method. The design method reduces the problem to two SISO design problems for which frequency response design techniques can be used. The first step is focused on designing a parallel combination of the two subsystems such that the parallel plant allows optimal design freedom. The second design issue is the design of the controller for the created parallel system. Here, the focus is on stability and achieving performance specifications. The design method leads to good insights in the system characteristics.

5 citations


Cited by
More filters
Book
05 Oct 1997
TL;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

Journal ArticleDOI
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

Journal ArticleDOI
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

Proceedings ArticleDOI
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

Journal ArticleDOI
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