Author
M Maarten Steinbuch
Other affiliations: Nanyang Technological University, Delft University of Technology, Bosch ...read more
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.
Papers published on a yearly basis
Papers
More filters
TL;DR: Experimental results show that control of the 3-DOF dynamics of the levitated vehicle in real-time can be successfully achieved by the proposed method.
Abstract: The real-time control of the three degrees of freedom (DOF) dynamics of an electrodynamic (EDS) Maglev vehicle is presented. The design is based on a 5-DOF state-space model of the sled dynamics that uses a simple algebraic model to describe the interaction between the null-flux coils on the track and the permanent magnets on the sled. A first-order sliding mode controller with integral error term is used to control heave, pitch, and roll in real time from position-attitude information measured with sensors located on the sled. Experimental results show that control of the 3-DOF dynamics of the levitated vehicle in real time can be successfully achieved by the proposed method.
18 citations
01 Jan 1989
TL;DR: In this paper, the authors investigated the control system design for a wind energy conversion system, which consists of a three-bladed rotor and an electrical conversion system with a synchronous generator and rectifier/inverter link.
Abstract: The control system design for a wind energy conversion system is investigated. The system consists of a three–bladed rotor and an electrical conversion system with a synchronous generator and rectifier/inverter link. The system has three inputs and two measurable outputs. Because there is severe interaction, especially between the electrical and mechanical part, the approach of multivariable control is chosen. In order to handle the conflicting control requirements, the method of linear quadratic optimal output feedback (or parametric optimal control) has been used. The controller structure has been used as part of the design process, to obtain robustness. The results for the wind turbine are shown, using a nonlinear dynamic model, and these are compared to results obtained with a classical PID design.
18 citations
01 Jan 2004
TL;DR: Simulation results show that this method can provide an estimate for the stability parameter of a centrifugal compression system and a good value for this parameter is not easily obtained from surge data.
Abstract: This paper presents the application of a lumped parameter model to describe the dynamic behavior of a centrifugal compression system including surge. The response of the model is compared with experimental surge measurements from an industrial single stage compressor test rig. A parametric analysis of the model reveals the large influence of the stability parameter on the transient response. However, a good value for this parameter is not easily obtained from surge data. Therefore, an identification method is proposed to uniquely determine the stability parameter that is based on an approximate realization algorithm, making use of the fact that the step response of the system has the characteristics of a first order system. Simulation results show that this method can provide an estimate for the stability parameter of a centrifugal compression system.
18 citations
29 Jun 1994
TL;DR: The design and implementation of robust multivariable controllers for a compact disc mechanism using the DK-iteration scheme to achieve good track-following and focusing performance in the presence of disturbances and norm-bounded structured plant uncertainty is considered.
Abstract: This paper considers the design and implementation of robust multivariable controllers for a compact disc mechanism. The design problem is to achieve good track-following and focusing performance in the presence of disturbances and norm-bounded structured plant uncertainty. This robust performance problem has been solved in the /spl mu/-framework using the DK-iteration scheme. Limits of implementation necessitate the use of sophisticated model reduction techniques and implementation schemes.
18 citations
TL;DR: This paper presents an algorithm for optimized (low latency, robust and high fidelity) real-time sensing of the crashes, based on time-scale wavelet theory and edge-detection, which is robust and accurate.
18 citations
Cited by
More filters
Book•
05 Oct 1997TL;DR: In this article, the authors introduce linear algebraic Riccati Equations and linear systems with Ha spaces and balance model reduction, and Ha Loop Shaping, and Controller Reduction.
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.
3,471 citations
TL;DR: In this paper, a review of electrical energy storage technologies for stationary applications is presented, with particular attention paid to pumped hydroelectric storage, compressed air energy storage, battery, flow battery, fuel cell, solar fuel, superconducting magnetic energy storage and thermal energy storage.
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.
3,031 citations
TL;DR: Though beginning its third decade of active research, the field of ILC shows no sign of slowing down and includes many results and learning algorithms beyond the scope of this survey.
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.
2,645 citations
27 Jun 2016
TL;DR: This work proposes an LSTM model which can learn general human movement and predict their future trajectories and outperforms state-of-the-art methods on some of these datasets.
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.
2,587 citations
TL;DR: This article attempts to strengthen the links between the two research communities by providing a survey of work in reinforcement learning for behavior generation in robots by highlighting both key challenges in robot reinforcement learning as well as notable successes.
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.
2,391 citations