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

Repetitive control is useful if periodic disturbances act on a control system. Perfect (asymptotic) disturbance rejection is achieved if the period-time is exactly known. For those cases where the period-time changes and can not be measured directly by an auxiliary signal, a robust repetitive controller structure is proposed. It uses multiple memory-loops in a certain feedback configuration, such that small changes in period-time do not diminish the disturbance rejection properties. For larger changes of the period-time a new self-tuning method is proposed, based on correlation analysis. Both the robust and the adaptive repetitive controller show good implementation results for a tracking control problem of a Compact Disc player.

Robust and adaptive repetitive control

In this paper, a method is outlined, that can be used as an expedient for the design of safety restraint systems or as tool to design online controllers for smart safety restraint systems. For complexity reasons, the airbag is left out of consideration. The focus is to develop the method to minimize the maximum value of the chest acceleration by manipulation of the force applied to the belt by the beltforce limiter. The method roughly exists of identification and controller design. The system identification part results in a model, describing the transfer from a perturbation in the beltforce to the corresponding disturbance in the chest acceleration. The controller design part results in a PID-like controller, based on the identified transfer. To evaluate the outlined method, results of the method for manipulation of the beltforce in case of a standard crash test with a BMW 3-series are presented.

http://www.mate.tue.nl/mate/pdfs/5365.pdf

Control design of a safety restraint system

Computation of transfer function data from frequency response data with application to data-based root-locus

The large parameter variations in heavy-duty trucks make it difficult to find appropriate settings for fixed structure speed controllers. Satisfying the design specifications with experience based trial-and error control design methods is very time-consuming. This paper discusses the modelling and model verification of a vehicular driveline and the model based, systematic design and tuning of speed controllers, using frequency domain techniques. Experiments with a real truck show that the performance and robustness of the closed loop system is within the specifications. The focus is on the cruise control, but the approach is also applicable to other speed controllers.

http://www.mate.tue.nl/mate/pdfs/7487.pdf

M Maarten Steinbuch

Papers

Robust and adaptive repetitive control

Control design of a safety restraint system

Computation of transfer function data from frequency response data with application to data-based root-locus

Frequency domain approach for the design of heavy-duty vehicle speed controllers

Approximate realization of valve dynamics with time delay