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

Tokamak plasmas in nuclear fusion are subject to various instabilities. A clear example is the sawtooth instability, which has both positive and negative effects on the plasma. To optimize between these effects control of the sawtooth period is necessary. This paper presents a simple control oriented model, from current drive (input) to sawtooth period (output), which can mimic the most relevant aspects of the sawtooth instability. It also shows some simulation results, including a static input-output map and a comparison with experimental data.

http://www.mate.tue.nl/mate/pdfs/10737.pdf

Control oriented modeling and simulation of the sawtooth instability in nuclear fusion tokamak plasmas

This paper considers the design, implementation and validation of FeedForwardControl for a Compact Disk mechanism. The design objective is to increase the ability of the CD mechanism to withstand external mechanical disturbances. For the research described an accelerometer has been used to measure the base-acceleration. This signal is filtered and added to the focus servo loop. The reduction of the servo error signal has been used to indicate the effectiveness of the feedforward.

Feed Forward Control to Enhance CD Skip Performance

A new measurement machine is being developed capable of universal, non-contact and fast measurement of freeform optics with a measurement uncertainty of 30 nm (2s). The detailed design is now almost complete and realization is progressing rapidly. The ambitious requirements are still expected to be met and completion is expected in the 3rd quarter of 2007.

Design and realization of a measurement machine for the universal non-contact measurement of large freeform optics with 30 nm uncertainty

A System Analysis for High- and Very High-Speed CD-ROM Drives

New deformable mirror technology is developed by the Technische Universiteit Eindhoven, Delft University of Technology and TNO Science and Industry. Several prototype adaptive deformable mirrors are realized mirrors, up to 427 actuators and ∅150mm diameter, with characteristics suitable for future AO systems. The prototypes consist of a 100µm thick, continuous facesheet on which low voltage, electromagnetic, push-pull actuators impose out-of-plane displacements. The variable reluctance actuators with ±10µm stroke and nanometer resolution are located in a standard actuator module. Each module with 61 actuators connects to a single PCB with dedicated, 16 bit, PWM based, drivers. A LVDS multi-drop cable connects up to 32 actuator modules. With the actuator module, accompanying PCB and multi-drop system the deformable mirror technology is made modular in its mechanics and electronics. An Ethernet-LVDS bridge enables any commercial PC to control the mirror using the UDP standard. Latest results of the deformable mirror technology development are presented.

/pdf/electromagnetic-dm-technology-meets-future-ao-demands-5fkjmun3r5.pdf

M Maarten Steinbuch

Papers

Control oriented modeling and simulation of the sawtooth instability in nuclear fusion tokamak plasmas

Feed Forward Control to Enhance CD Skip Performance

Design and realization of a measurement machine for the universal non-contact measurement of large freeform optics with 30 nm uncertainty

A System Analysis for High- and Very High-Speed CD-ROM Drives

Electromagnetic DM technology meets future AO demands