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

The development of an optimal energy management system for vehicle electric power nets : a feasibility study from a control perspective

Light traveling from a star suffers from optical distortion caused by the earth’s atmosphere. Adaptive optics (AO) is used in ground-based telescopes to correct these distortions (figure 1). In an AO system a real-time control system drives a deformable mirror based on information from the wavefront sensor.

Test results of an adaptive deformable mirror for future large telescopes

This paper presents a control-oriented model of the magnetic flux in the International Tokamak Experimental Reactor (ITER) actuated with Electron Cyclotron Current Drive (ECCD) at different locations. The main objective of the control-oriented modeling is to derive an input/output representation of the magnetic profile written as a state-space model. The state-space model allows determination of the most suitable actuation strategies using the concept of controllability analysis. To illustrate the controllability analysis of the magnetic flux actuated at different locations, we present numerical results based on space dependent parameters obtained from the integrated modeling code CRONOS.

/pdf/control-oriented-modeling-of-the-electron-cyclotron-current-433xngx14o.pdf

Control-oriented modeling of the Electron Cyclotron Current Drive actuated hybrid mode in ITER

In this paper, we address the problem of haptic simulation of a gear selector lever using a two degrees–of–freedom mechatronic lever. To reproduce the behavior of a customary gearshift, we propose suitable virtual artificial potential fields that generate the desired force profiles. The artificial potential functions are based on generalized sigmoid functions and contain parameters that have intuitive physical meaning and that can be easily adjusted to change the force sensations fed to the user. To validate our approach experiments have been carried out.

Haptic Simulation of a Gear Selector Lever Using Artificial Potential Fields

A new traceable metrological AFM with a measuring volume of 1x1x1 mm is being developed for the Dutch metrology institute (NMi - Van Swinden Laboratory) The instrument consists of a low-hysteresis elastic translation stage and a custom designed, kinematically mounted AFM measurement head Here a short overview of the translation stage design will be given and some preliminary results presented

M Maarten Steinbuch

Papers

The development of an optimal energy management system for vehicle electric power nets : a feasibility study from a control perspective

Test results of an adaptive deformable mirror for future large telescopes

Control-oriented modeling of the Electron Cyclotron Current Drive actuated hybrid mode in ITER

Haptic Simulation of a Gear Selector Lever Using Artificial Potential Fields

Design and realization of a long-stroke translation stage for SPM