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.

Control system and control means for controlling a quantity of such a control system

Abstract: In a control system a characteristic quantity of a process (1) exhibits a periodic disturbance. A measuring system (2) generates a measuring signal (Vm) which represents the characteristic quantity. A control device controls the process in response to the measuring signal (Vm). To reduce the periodic disturbance, the control device (3) comprises a delay circuit (4) which delays an input signal (Vin) related to the measuring signal (Vm) by a time interval (T) having the length of a period (Tp) of the periodic disturbance. The control device (3) comprises an analysis circuit (6) for deriving an analysis signal (Va) which is indicative of a deviation between the period of the periodic disturbance and the delay time. By means of a control circuit (13) and an adapter circuit (7), the delay time (T) is set in dependence on the analysis signal (Va) to a value for which the analysis signal (Va) indicates that the delay time (T) is equal to the period (Tp). A correlation system can be used for deriving the analysis signal (Va).

Modeling and fuel economy studies of a power steering pump and an air conditioning compressor for hybrid electric vehicles

Abstract: The hybridization and electrification of powertrains has greatly entered multiple transport sectors in the last decade. To find the optimal design of a vehicle that has more than one power convertor and one energy source is a complex optimization problem. As motivated in more detail in [1], the choice of the optimization algorithm and the definition of the problem will strongly influence the resulting power train. Beside the main components that are used for vehicles propulsion, also important energy consumptions are given by the auxiliaries present in the system. The goal of this research is to analyse the influence of the electrification of auxiliaries on fuel consumption and drive efficiency. The focus here will be on the modeling and fuel economy analysis of the power steering pump (PSP) and air conditioning compressor (ACC).

A multi-level optimization approach applied to hybrid vehicles design

Abstract: When the optimal topology for hybrid vehicles is investigated, one searches for that design which can ensure an energy efficient system rather than multiple energy efficient sub-systems (e.g. the combustion engine). This implies that the fuel consumption of the complete truck has to be minimized, instead of the fuel consumption of individual components. Furthermore, secondary aspects such as cost or performance have also to be considered. To address this problem for hybrid electric vehicles, we propose here a nested optimization approach and we apply it to the sizing and control design problem of two auxiliary units used in a heavy duty truck. A new topology is proposed, the optimization problem is introduced and a sensitivity analysis of the results is shown.

How To Obtain a 1 kHz Visual Servoing Setup

Abstract: The research described here focusses on visual servoing [1] for production processes that take place on repetitive structures. The long term goal of the project is to create a setup capable of sampling with more than 1 kHz with a total time delay of less than 2 samples. It should track velocities up to 1 m/s with an accuracy in the order of 1-10 mm. As a starting point a black dot on a sheet of white paper is placed onto a two dimensional stage, where the goal is to control the black dot within the field of view at 1 kHz.

The Smart Powertrain, Clean, Efficient and Safe Utilization of Existing Infrastructure

Abstract: In this paper a new project that is to be launched at the end of 2003 is described. The project is entitled "The Smart Powertrain" and is defined in the Netherlands where it is pending for governmental subsidy. The Smart Powertrain fuses Hybrid Powertrain Technology (HPT) with Advanced Driver Assist (ADA) Systems for improving both the efficiency of the powertrain and traffic. Break-through technologies on active and passive safety are an integral part of the project definition. The integration of HPT and ADA in one context will result in currently unforeseeable benefits on emissions and congestion, particularly since they are mutually enforcing mechanisms. The paper describes the Smart Powertrain definition, motivation for this definition and preliminary results on fuel economy benefits. Highly specialized R&D that is currently performed in The Netherlands and runs ahead on the SPT program is briefly addressed throughout the paper.

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.