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.

Under Actuated Air Path Control of Diesel Engines for Low Emissions and High Efficiency

Abstract: This paper presents a new method for feedback control using the Exhaust Gas Recirculation (EGR) valve and Variable Geometry Turbine (VGT) of a diesel engine. The controller effectively counteracts disturbances in NOx and PM emissions while maintaining the fuel efficiency. It is shown that by using a new combination of outputs, the controlled system has very good robustness properties. Using a mean-value engine model, which is extended with an emission model, the performance of the controlled system is examined in a simulation study with various applied disturbances; the feedback controller is shown to reduce the variation of emissions and pumping losses by 80–90 %. Compared to open loop control, the feedback controlled system has lower overall emissions by 14 % in NOx, 19 % in PM and a simultaneous 0.7 % improvement of the brake specific fuel consumption is achieved.

Robust control of a compact disc mechanism

Abstract: A compact disc (CD) player is an optical decoding device that reproduces high-quality audio from a digitally coded signal recorded as a spiral-shaped track on a reflective disc. Apart from the audio application, other optical data systems (CD-ROM, optical data drive) and combined audio/video applications (CD-interactive, CD-video) have emerged. An important research area for these applications is the possibility of increasing the rotational frequency of the disc to obtain faster data readout and shorter access time. For higher rotational speeds, however, a higher servo bandwidth is required that approaches the res onance frequencies of bending and torsional modes of the CD mechanism. Moreover, the system behavior varies from player to player because of manufacturing tolerances of CD players in mass production, which explains the need for robustness of the controller. Further, an increasing percentage of all CD-based applications is for portable use. Thus, additionally, power consumption and shock sensitivity play a decisive role in the performance assessment of controller design for CD systems. In this chapter we concentrate on the possible improvements of both the track-following and focusing behavior of a CD player, using robust control design techniques.

Parametric Modeling of Components for Selection and Specification of Hybrid Vehicle Drivetrains

Abstract: Drivetrain hybridization implies adding a secondary powe rs ource (electric machine/battery) to a primary power source (engine/fille df u e lt ank) in ord e rt o improv e: fuel economy, emissions, drivability (performance), comfort and safety. Designing a hybrid vehicle drivetrain fulfilling the require dv ehicle driving functions is therefore a comple xt ask. Many r esearchers have put effort formulating and developing overall hybrid drivetrain analysis, design and optimization models including top-leve lv ehicle control strategy for optimal fuel economy. This paper seeks to investigate the possibility of overall model simplification for the hybrid drivetrain system including the control strate gy. This is p erforme db y d escribing the component efficiencies and control rule sw ith only af ew characteristic parameters that capture the total systems fuel efficie ncy with suffici ent accuracy (~1%). Using these parameters the modeling and simulation process can be done very quickly. The method has bee nd emonstrate do n a serie s- , ap arall e l-a nd as eries-parallel hybrid drivetrain with specified component technologies, vehicle parameters and drive cycle .T he fuel economy and control strategy results are compared with Simulink/Advisor and Dynamic Programming.

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.