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.

Modelling slip- and creepmode shift speed characteristics of a pushbelt type continuously variable transmission

Abstract: A new model for transient behavior of a pushbelt type CVT is proposed in order to be able to simulate variator behavior under slip conditions. This model is compared to existing models. Compared to those models better agreement with measurements was found.

Modeling, identification and control of a metrological Atomic Force Microscope with a 3DOF stage

Abstract: Atomic Force Microscopes (AFMs) are widely used for the investigation of samples at nanometer scale. In this paper, we present the modeling, the identification and the control of a metrological AFM. The metrological AFM is used for the calibration of transfer standards for commercial AFMs. Therefore, the focus of the presented work is on scanning accuracy rather than on scanning speed. The contribution of this paper is the combination of 3 degree-of-freedom (DOF) control, including position feedforward, with an AFM with fixed cantilever and a piezo-stack driven stage. The amount of coupling between all DOFs is assessed by a non-parametric MIMO identification of the AFM. Since the dynamics appear to be decoupled in the frequency range of interest, feedback controllers are designed using loopshaping techniques for each DOF separately. Position feedforward is added to the stage in x and y direction, which improves the tracking performance by a factor two. The controlled stage is able to track scanning profiles within the sensor bound of 5 nm. With the proposed control method, the metrological AFM can produce images of the transfer standards with a sensor bound of 2 nm. Furthermore, real-time imaging of the sample is possible without the need for a-posteriori image correction. Finally, it is shown that the proposed control method almost completely compensates the hysteresis in the system.

A Port-Hamiltonian Approach to Visual Servo Control of a Pick and Place System

Abstract: In this paper, we take a port-Hamiltonian approach to address the problem of image-based visual servo control of a pick and place system. Through a coordinate transformation and a passive interconnection between mechanical system and camera dynamics we realize a closed-loop system that is port-Hamiltonian. The resulting control strategy depends only on the camera states and it can be proven that the closed-loop system is also asymptotically stable. Furthermore, a region of attraction is defined for which asymptotic stability holds.

Large adaptive deformable membrane mirror with high actuator density: design and first prototypes

Abstract: A large adaptive deformable mirror with high actuator density is presented The DM consists of a thin continuous membrane which acts as the correcting element A grid of low voltage electro-magnetical push-pull actuators, - located in an actuator plate -, impose out-of-plane displacements in the mirror's membrane To provide a stable and stiff reference plane for the actuators, a mechanically stable and thermally decoupled honeycomb support structure is added The design is suited for mirrors up to several hundred mm with an actuator pitch of a few mm One of the key elements in the design is the actuator grid Each actuator consists of a closed magnetic circuit in which a strong permanent magnet (PM) attracts a ferromagnetic core Movement of this core is provided by a low stiffness elastic guiding A coil surrounds the PM Both the coil and the PM are connected to the fixed world By applying a current through the coil, the magnetic force acting on the core can be influenced This force variation will lead to translation of the ferromagnetic core This movement is transferred to the reflective mirror surface in a piston-free manner The design allows for a long total stroke and a large inter actuator stroke The actuators are produced in arrays which make the design modular and easily extendable The first actuators and an actuator grid are produced and tested in a dedicated test set-up This paper describes how relevant actuator properties, such as stiffness and efficiency, can be influenced by the design The power dissipation in the actuator grid is optimized to a few milliwatts per actuator, thereby avoiding active cooling

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.