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.

Wavelets for feature detection : theoretical background

Abstract: Wavelet theory is a relatively new tool for signal analysis. Although the rst wavelet was derived by Haar in 1909, the real breakthrough came in 1988 when Daubechies derived her famous wavelet design. Since then a lot of wavelets have been designed for many applications. A wavelet is a function that goes to zero at the bounds and between these bounds it behaves like a wave. The word wavelet originates from a combination of wave and the French word for small wave, ondelette. This small wave is convoluted with a signal. This expresses the amount of the overlap of the wavelet as it is shifted over the signal. In other words, where the signal resembles the wavelet, the resulting function will have high magnitude and where it has a totally dierent shape it will have low magnitude. How well the wavelet resembles the signal locally can be calculated by shifting the small wave over the entire signal. By not only comparing the signal with shifted wavelets but also comparing wavelets that are dierently dilated, something can be said about the scale (frequency) content of the signal. There are many dierent wavelets with a verity of shapes and properties. Therefore it is a much broader tool for signal analysis compared to the Fourier Transformation where the signal is only compared to sinusoids. However, Fourier analysis plays an important role in wavelet analysis and is still one of the most important tool for signal analysis. Therefore in Chapter 3 a short introduction is given about signal analysis in general and about the decomposition of signals. The Fourier Transformation and the Short Time Fourier Transformation are introduced as two possible analyzing methods. Thereafter, in Chapter 4, the Continuous Wavelet Transformation is introduced and two examples are presented. The Continuous Wavelet Transformation is in general considered redundant because it uses continuous signals and therefore needs to be made discrete before it can be used in an application. This makes the Continuous Wavelet Transform inecient. This can be overcome by using the Discrete Wavelet transform. It is very efficient if it is applied through a lter bank, which is an important part of the Discrete Wavelet Transform. The Discrete Wavelet Transform is discussed in Chapter 5. In Chapter 6 its most important properties are explained. In addition a number of issues related with the DWT are discussed. Finally the most important applications are explained in Chapter 7, whereafter in Chapter 8 some conclusions are presented.

Repetitive control applied to a walking piezo actuator

Abstract: The performance of systems that exhibit repetitive disturbances can be significantly improved using repetitive control (RC). If the period-time of the repetitive disturbance is exactly known and constant in time, perfect asymptotic disturbance rejection can be achieved. In this paper, we apply RC to a high-precision stage driven by a walking piezo actuator with four bimorph piezoelectric legs. The repetitive nature of the walking movement introduces repetitive disturbances in the system, which are periodic with respect to the angular orientation of the legs, but not with respect to time. Therefore, an adjusted RC scheme is introduced, which incorporates a time-varying delay that is dependent on the momentary orientation of the legs during the walking movement. Experiments show that the tracking error can be significantly reduced by the adjusted RC method compared to standard RC. Furthermore, the adjusted RC scheme can also suppress repetitive disturbances for varying setpoint velocities.

Haptic manipulation of virtual linkages with kinematic loops

Abstract: This paper proposes an approach to providing realistic force feedback to users manipulating virtual linkages with kinematic loops. In the proposed approach, users feel the effect of the effect of the virtual kinematic loops: (a) on the inertia of the virtual linkage at the user-selected operational point (OP); and (b) on their freedom of motion. The approach introduces a method for computing the inertia of linkages with kinematic loops at arbitrarily selected OPs. It uses this inertia to select the directions of motion resisted by the virtual joints. Users feel the apparent inertia via impedance control, and the motion restrictions via stiffness control. Controlled experiments within a planar haptic interaction system validate that the proposed approach successfully renders the kinematic loop closure constraints to users.

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.