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.

Time-frequency analysis of a motion system with learning control

Abstract: Iterative learning control (ILC) is a known technique for improving the performance of systems or processes that operate repetitively over a fixed time interval. ILC generates a feed-forward signal effective for providing good tracking control. Experience with ILC algorithm applied to the wafer stage of a wafer scanner motion system has shown that ILC has liability to deal with limited performance in the face of position dependent dynamics, with the fact that ILC does not account for setpoint trajectory changes and with stochastic effects. The goal of the research presented in this paper is to integrate ILC applied to the wafer stage motion system with time-frequency analysis. This provides insight into the above mentioned ILC shortcomings when the learning control technique is applied on the considered motion system. We examine the suitability of a time-frequency adaptive filtering design for the learned feed-forward when applied to the wafer stage setup.

Control relevant identification of a compact disc pick-up mechanism

Abstract: This paper discusses the control relevant parametric identification of a servo system present in a compact disc player. In this application an approximate closed loop identification problem is solved in order to come up with a linear multivariable discrete time model, suitable for control design. This identification problem is handled by a recently introduced two stage method. It yields an explicit and tunable expression for the bias distribution of the model being estimated, clearly showing the dynamics or the closed loop system in the (asymptotic) approximation criterion. This result is exploited to identify the model in a control relevant way by additional data filtering. The recently introduced method by Vries-Van den Hof (1993) for model uncertainty quantification is used to construct an upper bound for the corresponding model error. >

Model predictive control of a waste heat recovery system for automotive diesel engines

Abstract: In this paper, a switching Model Predictive Control strategy is designed for an automotive Waste Heat Recovery system with two parallel evaporators. The objective is to maximize Waste Heat Recovery system output power, while satisfying safe operation under highly dynamic disturbances from the engine. Safe system operation is associated with vapor state after the evaporators. The closed-loop performance of the Model Predictive Control strategy is demonstrated on a high-fidelity validated Waste Heat Recovery system model subject to realistic disturbances from an Euro VI heavy-duty diesel engine. The simulation results, based on a World Harmonized Transient Cycle, demonstrate that the proposed control strategy outperforms a classical PI control strategy in terms of safety and relative average power, up to 15% and 3%, respectively. cop. 2014 IEEE.

Deformable mirrors with thermo-mechanical actuators for extreme ultraviolet lithography: Design, realization and validation

Abstract: In lithographic illumination systems, a nonuniform light distribution causes local deformations on the mirrors used. Active mirrors are a solution to correct these deformations by reshaping the surface. This paper presents the deformation of a mirror with thermo-mechanical actuators placed perpendicular to the surface. Two deformable mirrors are modeled, realized and validated: one with seven and one with 19 actuators. By placing the actuators on a thin back plate, the force loop is localized and therefore a lower actuator coupling is achieved. The thermo-mechanical actuators are free from mechanical hysteresis and therefore have a high position resolution with high reproducibility. Extensive Finite Element Analysis is done, to maximize actuator stroke and minimize input power. The mirrors are tested and validated with interferometer surface measurements and thermocouple temperature measurements. A mirror deflection of 0.68 nm/K is realized and no hysteresis is observed. Thermal step responses are fitted and both heating and cooling characteristic time constants are 2.5 s. The thermal actuator coupling from an energized actuator to its direct neighbor is 6.0%. The total actuator coupling is approximated around 10%, based on the good agreement between simulated and measured inter-actuator stroke.

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.