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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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Journal ArticleDOI
TL;DR: This paper describes a new method to synthesize driving cycles, where not only the velocity is considered but the road slope information of the real-world measured driving cycle as well, and outperforms current methods in terms of accuracy and speed.
Abstract: This paper describes a new method to synthesize driving cycles, where not only the velocity is considered but the road slope information of the real-world measured driving cycle as well. Driven by strict emission regulations and tight fuel targets, hybrid or electric vehicle manufacturers aim to develop new and more energy- and cost-efficient powertrains. To enable and facilitate this development, short, yet realistic, driving cycles need to be synthesized. The developed driving cycle should give a good representation of measured driving cycles in terms of velocity, slope, acceleration, and so on. Current methods use only velocity and acceleration and assume a zero road slope. The heavier the vehicle is, the more important the road slope becomes in powertrain prototyping (as with component sizing or control design); hence, neglecting it leads to unrealistic or limited designs. To include the slope, we extend existing methods and propose an approach based on multidimensional Markov chains. The validation of the synthesized driving cycle is based on a statistical analysis (as the average acceleration or maximum velocity) and a frequency analysis. This new method demonstrates the ability of capturing the measured road slope information in the synthesized driving cycle. Furthermore, results show that the proposed method outperforms current methods in terms of accuracy and speed.

69 citations

Journal ArticleDOI
TL;DR: In this paper, the authors present a new controller design that explicitly separates the control laws for each objective by introducing clutch engagement phases, achieving fast and smooth clutches without a noticeable torque dip.

68 citations

BookDOI
TL;DR: In this paper, the authors present an on-ramp design suite for powertrain control and time to torque estimation for a wheel loader with an HIL application Carmaker.
Abstract: Part I: Optimization Methods.- Extremum Seeking,- Trajectory Planning.- Advances in Embedded MPC.- Network Optimization.- Approximate Optimal Solutions of HJB.- Part II: Inter- and Intra-Vehicle System Optimization.- Cooperative Optimal Control.- String Stability.- Trajectory Optimization.- Optimal Control for Vehicle Safety.- Fuel Economy by CACC.- Applications of Computational Optimal Control to Vehicle Dynamics.- Simulation and HIL Application Carmaker.- On Stochastic Optimal Control of Vehicle Speed for Fuel Efficient In-traffic.- Optimal Gearshift Control on Heavy Duty Applications.- Part III: Powertrain Optimization.- Powertrain Assessment.- Optimal Control of Wasteheat Recovery.- Topology Optimization.- Optimal Control of Hybrid Powertrains.- Control of Hybrid Powertrains by Approximately Linear Programming.- Optimal Control of Batteries.- Optimal Control of Fuel Cells.- Part IV: Engine Optimization.- Optimal Control of HCCI.- DOE and Automatic Mapping.- Optimal control of the Short Loading Cycle of a Wheel Loader.- On-ramp Design Suite for Powertrain Control.- Time to Torque Estimation.

68 citations

Journal ArticleDOI
01 May 1992
TL;DR: In this article, a general approach for modeling structured complex and real-valued parametric perturbations is presented, and the resulting robustness analysis problem is solved nonconservatively using real and complex-structured singular-value calculations.
Abstract: The investigation of closed-loop systems subject to model perturbations is an important issue to assure stability robustness of a control design. A large variety of model perturbations can be described by norm-bounded uncertainty models. A general approach for modelling structured complex and real-valued parametric perturbations is presented. The resulting robustness analysis problem is solved nonconservatively using real and complex-structured singular-value calculations. The uncertainty modelling and robustness analysis are shown for a high-accuracy 5D electromechanical positioning device to be used in optical (Compact Disc) recording.

66 citations

Journal ArticleDOI
20 Jun 2013
TL;DR: In this article, an optimal gear shift strategy for conventional passenger vehicles equipped with discrete ratio transmissions is proposed to study quantitatively an optimal trade-off between the fuel economy and the driveability.
Abstract: This paper aims at designing optimal gear shift strategies for conventional passenger vehicles equipped with discrete ratio transmissions. In order to study quantitatively an optimal trade-off between the fuel economy and the driveability, the vehicle driveability is addressed in a fuel-optimal gear shift algorithm based on dynamic programming by three methods: method 1, weighted inverse of power reserve; method 2, constant power reserve; method 3, variable power reserve. Furthermore, another method based on stochastic dynamic programming is proposed to derive an optimal gear shift strategy over a number of driving cycles in an average sense, hence taking into account the vehicle driveability. In contrast with the dynamic-programming-based strategy, the obtained gear shift strategy based on stochastic dynamic programming is real time implementable. A comparative analysis of all proposed gear shift methods is given in terms of the improvements in the fuel economy and the driveability. The variable-power-re...

63 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