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.

Experimental modelling and LPV control of a motion system

Abstract: The objective of this paper is to show how experimentally based modelling can be used for designing Linear Parametrically Varying (LPV) controllers. As a test system we use an industrial pick and place unit with one linear X-drive and two independent linear Ydrives. The dynamics of the Y-axes depend on the Xposition. An LPV model is derived by using measured Frequency Response Functions at different positions, fitting a parametric model on each measurement and combining these models by linking parameters via a fit as a function of operating point. Rewriting the LPV model into a LFT structure and applying model reduction in the space of the scheduling variable finalizes the modelling phase. With this model an LPV controller is calculated and shows robust performance for the whole operating range, in contrast to local H∞ controllers.

Adaptive repetitive control of a compact disc mechanism

Abstract: Radial track following of a compact disc player servo mechanism is severely exposed to periodic disturbances, induced by the eccentric rotation of the disc. The period of this disturbance is not available for measurement and varies slowly in time. Periodic disturbances can be adequately attenuated using the concept of repetitive control, provided the period is known. To deal with time varying periodic disturbances, a repetitive controller is tuned based on a simple though inaccurate physical model of the time varying character of the period. The model is tuned based on an estimate of the period, obtained through recursive identification. Experimental results show that the proposed scheme enables a significant improvement of the tracking accuracy of the radial servo mechanism.

Adaptive Iterative Learning Control for High Precision Motion Systems

Abstract: Iterative learning control (ILC) is a very effective technique to reduce systematic errors that occur in systems that repetitively perform the same motion or operation. However, several characteristics have prevented standard ILC from being widely used for high precision motion systems. Most importantly, the learned feedforward signal depends on the motion profile (setpoint trajectory) and if this is altered, the learning process has to be repeated. Secondly, ILC amplifies non-repetitive disturbances and noise. Finally, its performance may be limited due to position-dependent dynamics. This paper presents the design and implementation of a time-frequency adaptive ILC that is applicable for motion systems which executes the same motion or operation. It employs the same control system block diagram as standard ILC, but instead of a fixed robustness filter it uses a time-varying filter. By using the results of the time-frequency adaptive ILC, i.e., the shape of the learned feedforward signal, a ldquopiecewise ILCrdquo is proposed that leads to the design of a single learned feedforward signal suitable for different setpoints. The results are experimentally shown to work for a high precision motion system.

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.