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.

Optimal energy management for a flywheel-based hybrid vehicle

Abstract: This paper presents the modeling and design of an optimal Energy Management Strategy (EMS) for a flywheel-based hybrid vehicle, that does not use any electrical motor/generator, or a battery, for its hybrid functionalities. The hybrid drive train consists of only low-cost components, such as a flywheel module and a continuously variable transmission. This hybrid drive train is characterized by a relatively small energy capacity (flywheel) and discrete shifts between operation modes, due to the use of clutches. The main design criterion of the optimized EMS is the minimization of the overall fuel consumption, over a pre-defined driving cycle. In addition, comfort criteria are formulated as constraints, e.g., to avoid high-frequent shifting between driving modes. The criteria are used to find the optimal sequence of driving modes and the generated engine torque. Simulations show a fuel saving potential of 20% to 39%, dependent on the chosen driving cycle.

Time-delay pre-filter design for vibration free tracking of periodic reference signals

Abstract: The aim of this work is the development of time-delay filters that enable tracking of periodic signals with zero phase error Time-Delay control or input-shaping is a proven technique to reduce motion induced vibrations for systems with lightly damped modes subject to point-to-point movements An extension is proposed for systems subject to periodic reference signals, for which a closed-form solution is provided for second-order systems, followed by a generalization for higher order systems and multi-harmonic reference signals It is shown that the theory enables a significant reduction of tracking errors for non-differentiable reference signals The paper concludes with the presentation of an approach to desensitize the pre-filter to uncertainties in the forcing frequencies

Robust adaptive control of the sawtooth instability in nuclear fusion

Abstract: The sawtooth instability is a repetitive phenomenon occurring in plasmas of tokamak nuclear fusion reactors. Experimental studies of these instabilities and the effect they have on the plasma (notably the drive of secondary instabilities and consequent performance reduction) for a wide variety of plasma conditions is an important line of study in nuclear fusion research. Variations in the plasma conditions have a significant influence on the dynamical behavior of the sawtooth instability. Therefore, this paper presents the design of a sawtooth period controller which is robust against such variations. The controller is from a class of adaptive controllers better known as extremum seekers. In this technique, a cost function in terms of the desired sawtooth period is optimized on-line. The Extremum Seeking Controller (ESC) is model-free and is therefore inherently robust against model uncertainty. Simulations show that the controller is robust against variations in plasma parameters, delay in the sawtooth crash detection, and noise on the in- and outputs of the process. Because of its robustness, ESC is a promising candidate strategy for a wide range of fusion-related control problems with high model uncertainty.

Experimental investigation of macro-slip in a pushbelt CVT variator

Abstract: One way to further reduce the fuel consumption of passenger cars that are equipped with a pushbelt continuously variable transmission (CVT) is to improve the CVT efficiency by lowering the clamping forces in the variator. This approach requires the application of active control of the slip between the belt and the conical sheaves, which demands for a reliable and accurate dynamic variator model that describes the variator characteristics in the applicable slip range. In this paper, the dynamic variator model is investigated in detail, with emphasis on the torque transmission. As a first step, the torque transmission in stationary situations is investigated, in which it is generally assumed that macro-slip occurs on the pair of conical sheaves with the smallest wrapped angle. In this paper, it is experimentally shown that macro-slip only occurs on the pair of conical sheaves with the smallest wrapped angle for the extreme ratios Low and OverDrive in stationary situations.

Control oriented modeling and simulation of the sawtooth instability in nuclear fusion tokamak plasmas

Abstract: Tokamak plasmas in nuclear fusion are subject to various instabilities. A clear example is the sawtooth instability, which has both positive and negative effects on the plasma. To optimize between these effects control of the sawtooth period is necessary. This paper presents a simple control oriented model, from current drive (input) to sawtooth period (output), which can mimic the most relevant aspects of the sawtooth instability. It also shows some simulation results, including a static input-output map and a comparison with experimental data.

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.