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

• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers.

A new instrument for deep brain stimulation surgery

In miniaturized systems (e.g. optical disk drives) much effort is made to design a structure in such a way that the demands on dimensions (height, width) and desired bandwidth are satisfied. Easier, and often cheaper designs can not be used, because they suffer from lower resonance frequencies, which limit the bandwidth. A method is presented for mechatronic structures that compensates unwanted bandwidth limiting resonance frequencies with an additional piezo electric actuator. Advantage of this method is that no extra sensor is used and that the contribution of the piezo is much easier to tune (when compared to a notch filter), since it is part of the structure. Disadvantage is that the piezo influences the dynamics of the original system, which complicates the choice of a suitable piezo actuator.

/pdf/compensation-of-mode-shapes-with-a-piezo-electric-actuator-3pwe4hjb3s.pdf

Compensation of mode shapes with a piezo electric actuator

model-based dynamic observer F. Felici1, M.R. De Baar1,2, M. Steinbuch1, E. Fable3, E. Fokina3, L. Giannone3, C. Rapson3, M. Reich3, W. Treutterer3 and the ASDEX-Upgrade team3 1 Eindhoven University of Technology, Faculty of Mechanical Engineering, Control Systems Technology Group. P.O. Box 513, 5600 MB Eindhoven, The Netherlands 2 FOM-Institute DIFFER, Association EURATOM-FOM, The Netherlands. 3 Max-Planck-Institut für Plasmaphysik, EURATOM Association, 85748 Garching, Germany.

Real-time plasma state reconstruction and fault detection using a model-based dynamic observer

A demonstration of real-time feedback control for autonomous tracking and stabilization of m/n = 2/1 tearing modes in a tokamak using Electron Cyclotron Resonance Heating and Current Drive (ECRH/ECCD) is reported. The prototype system on TEXTOR combines in the same sight-line an Electron Cyclotron Emission (ECE) diagnostic for tearing mode sensing and a steer-able ECRH/ECCD antenna. The mode location is retrieved from the ECE measurements and serves as input for a control loop, which aligns the ECRH/ECCD deposition with the tearing modes by steering of a launcher mirror. The alignment is achieved by matching the mode location in the sensor spectrum with the fixed ECRH/ECCD actuator frequency. The control response is dominated by the response of the mechanical launcher. Analysis of the launcher dynamics receives special emphasis in the control design. In addition, the ECRH/ECCD power is modulated in phase with the rotation frequency of the O-point of the tearing modes using a feedback loop, which extracts the modes frequency and phase from the ECE data. The 'line-of-sight' concept has advantages over conventional tearing mode suppression schemes as it provides information of the modes location and phase in the direct vicinity of the EC-deposition. It avoids the use of ray-tracing in the control loop and guarantees accurate alignment in the presence of disturbances. The experimental results demonstrate the capabilities of the control system to track and suppress tearing modes in real-time. A relatively simple control design suffices to meet the performance requirements demanded for effective tearing mode suppression. In addition, a control-oriented model for the tearing mode control problem is being developed. This model includes tearing mode and plasma dynamics, models for the actuators, diagnostics, data-processing and control algorithms. Simulation results for typical TEXTOR conditions will be discussed and validated with experimental results.

M Maarten Steinbuch

Papers

A new instrument for deep brain stimulation surgery

Compensation of mode shapes with a piezo electric actuator

Real-time plasma state reconstruction and fault detection using a model-based dynamic observer

Feedback control of tearing modes through ECRH with launcher mirror steering and power modulation using a line-of-sight ECE diagnostic

Setpoint relevant feedforward design for motion systems