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.

/pdf/essentials-of-robust-control-3kaks6yk3h.pdf

Essentials of Robust Control

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.

http://promexico.me/Rubenius/WhitePapers/sdarticle%20(1).pdf

Progress in electrical energy storage system: A critical review

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.

A survey of iterative learning control

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.

/pdf/social-lstm-human-trajectory-prediction-in-crowded-spaces-wwxok9svq7.pdf

Social LSTM: Human Trajectory Prediction in Crowded Spaces

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.

/pdf/reinforcement-learning-in-robotics-a-survey-4w4397qx9i.pdf

Reinforcement learning in robotics: A survey

In this paper a standard notch filter is modeled in the port-Hamiltonian framework. By having such a port-Hamiltonian description it is proven that the notch filter is a passive system. The notch filter can then be interconnected with another (nonlinear) port-Hamiltonian system, while preserving the overall passivity property. By doing so we can combine a frequency-based control method, the notch filter, with the nonlinear control methodology of passivity-based control.

/pdf/notch-filters-for-port-hamiltonian-systems-20d4mrkerz.pdf

Notch filters for port-Hamiltonian systems

The best way to confine such extremely hot plasmas is by magnetic confinement. A very promising configuration is the toroidal tokamak, see Figure 2. Strong poloidal magnets create a large toroidal magnetic field inside the plasma, while transformator coils generate a current in the plasma which results in a poloidal field. The resulting helical field confines the charged plasma particles very well. Poloidal field coils can further shape the plasma inside the tokamak.

http://www.mate.tue.nl/mate/pdfs/9037.pdf

Nuclear fusion: principles and challenges

Optimizing base poses for manipulation by mobile base inclusion in the inverse kinematics

http://www.mate.tue.nl/mate/pdfs/3861.pdf

QFT/H∞ Control of a Small Solar Thermal Transfer Vehicle (SOTV) with Inflatable Appendages

In motion systems, position measurements are often obtained using optical incremental encoders. The position accuracy is limited by the quantized position measurement of the encoder. Velocity and acceleration signals obtained by numerical differentiation are dominated by high-frequency content due to the quantization. In literature, several methods have been proposed to improve the position and velocity estimations using position measurements at irregular time instants [1, 2]. These methods often require a system model to be available or are not applicable in real-time experiments.

http://www.mate.tue.nl/mate/pdfs/7502.pdf

M Maarten Steinbuch

Papers

Notch filters for port-Hamiltonian systems

Nuclear fusion: principles and challenges

Optimizing base poses for manipulation by mobile base inclusion in the inverse kinematics

QFT/H∞ Control of a Small Solar Thermal Transfer Vehicle (SOTV) with Inflatable Appendages

Data-based velocity observer using encoder time stamping