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.

Exploratory study on MIMO control for motion systems

Abstract: This presentation gives an overview of the results of an exploratory study on multivariable feedback and feedforward control for electromechanical motion systems. Industrial cases are presented where multivariable control may be beneficial. Multivariable control problems due to plant interaction and coupling in external disturbances and performance parameters are studied. Tools are presented to quantify multivariable properties in practical multivariable systems. Also, it is demonstrated how scalar manual loopshaping techniques may be extended to handle multivariable control problems.

Development of a modular service robot

Abstract: Due to ageing of the population, providing care to acceptable will standards become increasingly difficult. Thanks to recent advances in robotics and mechatronics, it is now starting to become possible to develop robots that can provide assistance in daily living, e.g., fetching objects, opening and closing of doors and drawers and operating switches. However, these technologies have not been brought together into an economically viable service robot. The goal of this project is the development of design methods for autonomous robot systems, using standardized architectures, which can safely work in a care situation. As a first step, the AMIGO robot has been designed, which will be used as a research platform to investigate standards in mechanical and electric interfaces and software architectures. Furthermore, it will be used in related activities such as the RoboEarth project and the RoboCup@Home competition.

An exploration of nonlinear control of linear motion systems

Abstract: The idea is to use a linear parameter varying (LPV) controller for an LTI plant to facilitate different performance properties in different parameter ranges. Consider for example the intentionally LPV closed-loop system CP 1+CP = 2 s2+( √ 8−2e)s+2 with error-dependent damping. The step response is depicted in black together with the responses of the base linear system with various damping ratios. i i

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.