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.

Model-based approach to minimize system calibrations for 3D reconstruction quality of a cone-beam CT system

Abstract: Obtaining high quality medical 3D reconstructions is of increasing importance for the medical community. The quality of a 3D reconstruction depends heavily on accurate knowledge of the position and orientation of the detector and X-ray source during a reconstruction scan. New developments in the considered class of medical imaging systems tend to lightweight design and an increasing number of scan positions. In this work, an alternative model-based approach is introduced for the currently used geometric system calibrations.

Sufficient Conditions for Data-driven Stability of Ellipsoidal Unfalsified Control

Abstract: Unfalsified Control is a direct data-driven, plant-model-free controller design method, which recursively falsifies controllers that fail to meet the required performance specification, making them ineligible to actually control the plant. In this paper it is shown that sufficient conditions for stability can be derived for Unfalsified Control with an ellipsoidal Unfalsified set, Ellipsoidal Unfalsified Control (EUC). These conditions are: feasibility of the adaptive control problem, discarding of demonstrable destabilizing controllers and a finite number of controller switches. The latter is guaranteed by imposing a maximum volume ratio between two consecutive ellipsoidal Unfalsified sets and a minimum stepsize on the controller adjustments.

Performance simulations of a low-cost hybrid powertrain with large fuel savings

Abstract: This paper presents a new design of a low-cost mechanicalhybrid powertrain with large fuel savings. The hybrid powertrain contains only low-cost mechanical components, such as a compact flywheel module and a Continuously Variable Transmission (CVT). No electrical motor/generator or battery is used. On the basis of the characteristics of typical driving cycles, the energy storage capacity of the flywheel module is derived accordingly. The fuel-saving potential of the new powertrain is simulated for a compact passenger vehicle, which represents the aimed vehicle segment in emerging markets. Simulations show that the fuel-saving potential, with respect to the same vehicle without flywheel module, ranges in between 15% and 29%, depending on the considered driving cycle.

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.