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.

Suppressing non-periodically repeating disturbances in mechanical servo systems

Abstract: Non-periodically repeating (NPR) disturbances are fixed-shape disturbances that occur randomly in time. We can provide a control system with the capability to suppress this type of disturbance by adding in parallel to the input of the nominal feedback controller a learning look-up-table based feedforward controller that is activated using an NPR-disturbance detector.

Control Relevant Blind Identification of Disturbances With Application to a Multivariable Active Vibration Isolation Platform

Abstract: This paper deals with the identification of root causes of disturbances in multivariable systems. It is shown that this boils down to a blind identification problem that can be solved, under additional assumptions, within certain indeterminacies. Using the results from identification, it is illustrated how sources can be physically interpreted and the location of the sources can be recovered. Also, a design tool is developed to select multivariable feedback controller candidates. The practical feasibility is demonstrated on an industrial multivariable controlled active vibration isolation platform.

Iterative learning control for variable setpoints, applied to a motion system

Abstract: Iterative Learning Control (ILC) is a known technique for improving the performance of systems or processes that operate repetitively over a fixed time interval. ILC generates a feedforward signal effective for providing good tracking control. However, there still exist a number of problems which hinder extensions of ILC schemes. The major obstacle is perhaps the requirement that the trajectory (or repetitive disturbance) must be strictly repeatable over operations. ILC has also liability to deal with stochastic effects. This paper presents the design and the implementation of a time-frequency adaptive ILC that is applicable for motion systems which execute the same kind of repetitive tasks. For the motion system, we show that the adaptive algorithm we propose leads to design one (learned) feed-forward signal suitable for different setpoints. We demonstrate that, when implementing time-frequency adaptive ILC, very good time performance (tracking errors) is obtained. The proposed algorithm converges faster than standard ILC. With time-frequency adaptive ILC noise amplification is reduced.

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.