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.

A compliance feedforward scheme for a class of LTV motion systems

Abstract: The implementation of lightweight high-performance motion systems in lithography applications imposes among others lower requirements on actuators, amplifiers, and cooling. However, the decreased stiffness of lightweight designs brings the effect of structural flexibilities to the fore especially when the so-called point of interest is not at a fixed location. This is for example the case when exposing a silicon wafer. To deal with structural flexibilities, a feedforward controller is proposed that combines two concepts: (a) continuous compliance compensation control and (b) snap feedforward control. Expanded to a subclass of LTV motion systems, the resulting controller compensates for the position-dependent and time-varying compliance of a flexible structure. The compliance function used will be derived using partial differential equations (PDE). The method is validated by simulation results.

Naar een Green Deal voor de kostendekkende uitrol van openbare laadpunten

Abstract: • A submitted manuscript is the author's version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers.

Control of automotive waste heat recovery systems with parallel evaporators

Abstract: In this paper, Model Predictive Control (MPC) is applied to control a Waste Heat Recovery system for a highly dynamic automotive application. As a benchmark, a commonly applied control strategy is used that consists of a feedforward based on engine conditions and of two PI controllers that individually control the post-EGR and post-exhaust evaporator temperature. By controlling the temperatures, this strategy deals with the constraints on working fluid temperature and aims to guarantee vapor state at the evaporator outlet. To design the local PI controllers, a frequency domain based loop shaping method is applied. Using a high-fidelity Waste Heat Recovery system model, the performance of the MPC and PI controller is compared in simulations over a World Harmonized Transient Cycle for both cold-start and hot-start conditions. Due to the availability of a model and knowledge of the engine disturbance, the MPC strategy is shown to outperform the reference PI controller, it gives higher energy recovery and guarantees operation within safety margins with respect to physical capabilities of the plant.

Data-based design of high-performance motion controllers

Abstract: This paper presents a data-based design of a linear feedback controller, which realizes desired closed-loop sensitivity and complementary sensitivity transfer functions. These transfer functions are specified via a single model-based performance cost. The data-based equivalent of this cost is derived, and its utility for the feedback design is demonstrated. A designer can prescribe the controller structure and complexity. Experimental results obtained in a direct-drive robot motion control problem show the quality of the design.

A systematic approach towards automated control design for heavy-duty EGR diesel engines

Abstract: This paper presents a model-based design method for a robust air path controller of adiesel engine with Exhaust Gas Recirculation (EGR). Following the ?? synthesis framework,a MIMO controller is developed, which guarantees robustness and optimal performance.This controller simultaneously controls EGR ow and air-fuel ratio. Due to the systematicdesign approach, this controller can be automatically generated, which dramatically reducescalibration time. Performance improvements are shown using simulations.

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.