Author
M Maarten Steinbuch
Other affiliations: Nanyang Technological University, Delft University of Technology, Bosch ...read more
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 published on a yearly basis
Papers
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29 Jun 1994
TL;DR: In this paper, robust control design for single-loop compact disc mechanisms is considered and the design problem is to achieve good track-following in the presence of disturbances and parametric plant uncertainty.
Abstract: This paper considers robust control design for single-loop compact disc mechanisms. The design problem is to achieve good track-following in the presence of disturbances and parametric plant uncertainty. This robust performance problem has been solved in the QFT-framework using measured frequency response data. The design has been implemented successfully in a digital signal processor (DSP) environment.
21 citations
TL;DR: In this article, the authors presented a modelling and simulation approach for determining the optimal degree-of-hybridisation for the drive train (engine, electric machine size) and the energy storage system (battery, ultra capacitor) for a medium-duty truck.
Abstract: This paper presents a modelling and simulation approach for determining the optimal degree-of-hybridisation for the drive train (engine, electric machine size) and the energy storage system (battery, ultra capacitor) for a medium-duty truck. The results show that the degree-of-hybridisation of known medium-duty hybrid electric trucks is close to the optimal degree-of-hybridisation using the methods as described in this paper. Furthermore, it is found that the Li-ion battery is from an energy and power density as well as cost point of view the most preferable energy storage system.
21 citations
TL;DR: In this article, the authors presented manageable, mathematical models of a vehicle occupant and a belt restraint system, which are relevant in the development of controlled restraint systems, which aim at lowering injury criteria by real-time control of occupant motion.
Abstract: This paper presents manageable, mathematical models of a vehicle occupant and a belt restraint system. These low-order models are relevant in the development of controlled restraint systems, which aim at lowering injury criteria by real-time control of the occupant motion. The models can be employed for control design and real-time injury prediction, the main components of controlled restraint systems. Several low-order models are constructed with first principles of physics and through knowledge obtained from a sensitivity analysis of validated, complex occupant models. The biomechanical responses of the low-order models, related to neck and thoracic injury criteria are validated with the results of the complex models. They are found to be valid for 5th, 50th and 95th percentile Hybrid III dummies in a range of frontal impact scenarios. The conclusion of this study is that thoracic and neck injury criteria in frontal impact can be accurately assessed with relatively simple occupant models, which are required for real-time control of injury-related biomechanical responses.
21 citations
TL;DR: In this paper, a rule-based energy management strategy (RB EMS) is proposed, where the maximum power level of the electric machine during pure electric driving is the control design variable.
21 citations
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05 Oct 1997TL;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
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
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
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
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