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.

Two level world modeling for cooperating robots using a multiple hypotheses filter

Abstract: Robots increasingly operate in dynamic environments and in order to operate safely, reliable world models are indispensable. A world model is the robot's view of the world and contains information about obstacle locations and velocities. A two level algorithm is proposed. It is of particular use for teams of cooperating robots and the algorithm is based on a multiple hypotheses filter. Each robot features a low level world model with a fast update rate which can be used for obstacle avoidance. The local world models are combined to one global view of the world that is shared between all robots and can be used for the implementation of team strategies. Labeling and tracking is added to the multiple hypotheses filter in order to reduce the sensitivity to track loss in case of temporary occlusions of objects or false measurements. The algorithm was extensively tested during the 2010 RoboCup Middle Size League world championships in Singapore, the results of which are presented.

Frequency response based multivariable control design for motion systems

Abstract: In this paper, we discuss the design of multivariable motion controllers exploiting crosscouplings in the controller for open loop decoupling, disturbance rejection and feedforward decoupling. Using specific properties of motion systems, we illustrate that frequency response design methods can be extended to handle several multivariable control problems. Application to high performance motion systems shows significant improvement

Control of a Waste Heat Recovery system with decoupled expander for improved diesel engine efficiency

Abstract: In this paper, a switching Model Predictive Control strategy is proposed for a Waste Heat Recovery system in heavy-duty automotive application. The objective is to maximize the WHR system output power while satisfying the output constraints under highly dynamic engine variations. For control design, a WHR system architecture with the expander and pumps decoupled from the engine is considered. Compared to a WHR system with the expander coupled to the engine, up to 29% more output power is obtained for the considered design. This holds for both steady state and highly dynamic engine conditions. The simulation results are obtained using a validated high-fidelity WHR system model with realistic disturbances from a Euro VI heavy-duty diesel engine.

Identification and control for future restraint systems

Abstract: In this paper, an approach to design an ideal restraint system is discussed. The problem, normally solved by optimization approaches, is translated to a tracking problem, where a given reference trajectory has to be tracked. Before a controller can be designed, identification of the local dynamic input-output behavior is performed in several operating points using stepwise perturbations added to the input. Using the obtained model, a stabilizing controller is designed with loop shaping for performance. Results are shown for the design of the belt force, such that the maximum chest deceleration of the driver in a frontal crash test with 56 km/h crash velocity, is minimized. A reduction of 60% of the maximum chest acceleration is achieved.

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.