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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
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Book ChapterDOI
25 Nov 2020
TL;DR: Maarten Steinbuch Eindhoven University of Technology 1.1 million euros ($1.3m; £1.1m) research grant to establish a new method for measuring the impact of natural disasters on education.
Abstract: Motion systems are mechanical systems with actuators with the primary function to position a load. The actuator can be either hydraulic, pneumatic, or electric. The feedback controller is typically designed using frequency domain techniques, in particular via manual loop-shaping. In addition to the feedback controller, a feedforward controller is often implemented with acceleration, velocity, and friction feedforward for the reference signal. This chapter provides an overview of a systematic control design procedure for motion systems that has proven its use in industrial motion control practise. A step-by-step procedure is presented that gradually extends single-input single-output (SISO) loop-shaping to the multi-input multi-output (MIMO) situation. This step-by-step procedure consists of interaction analysis, decoupling, independent SISO design, sequential SISO design, and finally, norm-based MIMO design. Extreme ultraviolet is a key technology for next-generation lithography.

16 citations

Proceedings ArticleDOI
19 Sep 2005
TL;DR: A method is presented to measure and control slip in a CVT in order to minimize the clamping forces while preventing destructive belt slip and a synthesis method for robust PI(D)-controller design is used to maximize the integral gain while making sure that the closed loop system remains stable.
Abstract: Continuously variable transmissions (CVT) can be used to operate a combustion engine in a more optimal working point. Unfortunately, due to the relatively low efficiency of modern production CVTs the total efficiency of the driveline is not increased significantly. This low efficiency is mainly caused by losses in the hydraulic actuation system and the variator. Decreasing the clamping forces in the variator greatly improves the efficiency of the CVT. However, lower clamping forces increase the risk of excessive belt slip, which can damage the system. In this paper a method is presented to measure and control slip in a CVT in order to minimize the clamping forces while preventing destructive belt slip. To ensure robustness of the system against torque peaks, a controller is designed with optimal load disturbance response. A synthesis method for robust PI(D)-controller design is used to maximize the integral gain while making sure that the closed loop system remains stable. Implementation in a test vehicle is achieved

16 citations

Proceedings ArticleDOI
14 Jun 2006
TL;DR: In this paper, an approach towards sheet control in a printer paper path is presented, where the complex overall control question is formulated in a hierarchical control set-up with a low level motor control part and a high level sheet control part.
Abstract: In this paper an approach towards sheet control in a printer paper path is presented. To make the control problem feasible, the complex overall control question is formulated in a hierarchical control set-up with a low level motor control part and a high level sheet control part. To understand the essence of the sheet control problem we consider a basic paper path in which industrial constraints and requirements are relaxed. Furthermore, the motor control part is assumed to be ideal and the sheet dynamics are captured in the piecewise linear modeling formalism. Based on the model of the sheet dynamics, the controller synthesis is carried out. Both state and output feedback control designs are presented and stability and tracking performance are analyzed. The effectiveness of the control design approaches is demonstrated via simulations.

16 citations

Journal ArticleDOI
TL;DR: From the experience with actual implementation on a compact disc system, frequency weighting reflecting the disturbance spectrum has to be applied to obtain good experimental results.
Abstract: In fixed-point implementation of digital controllers, the effects of quantization can be reduced by proper scaling of the controller parameters. Commonly, controllers are scaled using l/sub 1/ norm based calculations, i.e., determining the worst case impulse response of the controller. This is only possible, however, if the controller is asymptotically stable. If this is not the case, a solution is to calculate the scaling transformation on the basis of the closed-loop impulse responses. From the experience with actual implementation on a compact disc system, frequency weighting reflecting the disturbance spectrum has to be applied as well to obtain good experimental results. >

16 citations

Journal ArticleDOI
TL;DR: This article presents a method to select appropriate algorithm classes that solve both the steps of motion planning and to select a suitable approach to combine those algorithm classes.
Abstract: A motion planner for mobile robots is commonly built out of a number of algorithms that solve the two steps of motion planning: 1) representing the robot and its environment and 2) searching a path through the represented environment. However, the available literature on motion planning lacks a generic methodology to arrive at a combination of representations and search algorithm classes for a practical application. This article presents a method to select appropriate algorithm classes that solve both the steps of motion planning and to select a suitable approach to combine those algorithm classes. The method is verified by comparing its outcome with three different motion planners that have been successfully applied on robots in practice.

16 citations


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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