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
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01 Jan 2008
TL;DR: In this article, the authors used optical incremental encoders to apply feedback control on motion systems, where the position is measured at a fixed sample frequency and the accuracy is limited by the quantized measurement of the encoder.
Abstract: Optical incremental encoders are widely used to apply feedback control on motion systems where the position is measured at a fixed sample frequency. The accuracy is limited by the quantized measurement of the encoder. Velocity and acceleration information from incremental encoders can be obtained using only the position information [1], thus disregarding the variable rate of occurrence of the encoder events, or using model based methods such as observers [2].
TL;DR: In this article, Loria et al. established uniform global asymptotic stability (UGAS) for the feedback loop consisting of a dynamical plant mod- eled using Euler-Lagrange equations of motion and of the adaptive controller of Slotine and Li.
01 Jan 2008
TL;DR: A reduced hybrid drive train model is introduced with which the effects of design parameter variation is studied very quickly and with an average error of less than 1.6%.
Abstract: Parametric modeling of CVT-based hybrid drive trains — In this paper the hybridization of a small passenger car equipped with a Continuously Variable Transmission (CVT) is investigated. In this work a parametric optimization procedure is presented in solving the design problem, where the main design objective is fuel consumption. The effects of parameter variation on the fuel consumption have been investigated. Furthermore, a reduced hybrid drive train model is introduced with which the effects of design parameter variation is studied very quickly and with an average error of less than 1.6%.
01 Jan 2011
TL;DR: In this paper, a decoupled system of non-linear equations in three directions: surge, heave and yaw was designed to detect foreign organisms residing on the sea chest of ships, which cause a risk for the domestic biodiversity.
Abstract: The AUV (autonomous underwater vehicle) of the University of Canterbury targets to discover any foreign organisms residing on the sea chests of ships, which cause a risk for the domestic biodiversity, and removes them. With the design of the AUV finished, the primary goal of this paper is to design control software that stabilizes the vehicle and minimizes the error in the desired trajectory. The dynamical model with implemented assumptions ultimately leads to a decoupled system of non-linear equations in three directions: surge, heave and yaw. For this system, experiments are designed (but not yet successfully accomplished) to identify the system parameters. With respect to control, a feedback linearization is firstly applied to a 1D case, which results in a satisfactory PIDcontroller, taking into account parameter perturbation and noise contamination. Finally, the under actuated problem in the 2D situation is evaluated, for which a path planning method and a state feedback control method is derived.
TL;DR: The method uses the singular value decomposition (SVD) technique to analyse the correlations among the design objectives and investigate their sensitivity to an optimal control algorithm and will reduce the complexity of the control design.
Abstract: In the optimal control design for vehicular propulsion systems, the objective function describing the design objectives (fuel economy, driveability, comfort, emissions, battery operation, battery aging, etc.) plays an important role in defining the optimal solution. However, the definition of the objective function is not often analysed explicitly and thoroughly. In this study, a method of objectively analysing and evaluating the objective function is introduced. The method uses the singular value decomposition (SVD) technique to analyse the correlations among the design objectives and investigate their sensitivity to an optimal control algorithm. Accordingly, the dependent design objective(s) will be omitted such that the objective function can be simplified. This will reduce the complexity of the control design.
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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