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 method for performance assessment of a variable-gain control design for optical storage drives is proposed based on a quantitative performance measure. The variable-gain strategy is used to overcome well-known control design tradeoffs (for linear systems) between low-frequency tracking properties and high-frequency noise sensitivity. A performance analysis is conducted to support the design of the variable-gain controller which subsequently is shown to outperform linear controllers.

Performance of variable-gain controlled optical storage drives

This paper presents a new design of a low-cost hybrid powertrain with large fuel savings. The hybrid powertrain contains only low-cost mechanical components, such as a flywheel module and a continuously variable transmission (CVT). No electrical motor/generator or battery is used. Based on characteristics of typical driving cycles, a hybrid topology is introduced and the energy storage capacity of the flywheel module is derived accordingly. The fuel saving potential of the new powertrain is simulated for a compact passenger vehicle, which represents the aimed vehicle segment of the emerging market in China. Simulations show that the fuel saving potential, with respect to the same vehicle without flywheel module, ranges in between 16 and 29%, dependent on the considered driving cycle. © EVS-25 Shenzhen, China.

http://www.mate.tue.nl/mate/pdfs/12524.pdf

Design of a low-cost hybrid powertrain with large fuel savings

This paper presents a method for higher-order controller design with improved low-frequent disturbance rejection properties. Given a default PID-based controller, the circle criterion is used to construct an extra controller part that deals with resonances resulting from closing the loop. The advantage is twofold. First, a closed-loop shaping method is obtained that is straightforward, graphically-supported, and intuitively clear. Second, it applies to existing control designs and therefore exploits (or even further extends) the design freedom and low-frequency performances therein. The effectiveness of the method is demonstrated using examples from the wafer scanning industry.

Circle criterion in linear control design

Accurate modelling is of key importance for the model-based design of controlled systems. The overall system complexity can be limited by using simple component models that represent only the main ...

Semi-empirical power dissipation modelling of mechanical hybrid powertrain components

The authors consider the design of static and fixed order controllers based on a new formulation of a mixed H/sub 2//H/sub infinity / optimal control problem. An explicit parameterization is given of H/sub infinity / norm bounded uncertainties. Necessary conditions are derived for the minimum of an H/sub 2/ norm control problem, under a worst-case H/sub infinity / norm-bounded uncertainty. >

M Maarten Steinbuch

Papers

Performance of variable-gain controlled optical storage drives

Design of a low-cost hybrid powertrain with large fuel savings

Circle criterion in linear control design

Semi-empirical power dissipation modelling of mechanical hybrid powertrain components

Robust performance in H/sub 2//H/sub infinity / optimal control