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 new universal non-contact measurement machine design for measuring free-form optics with 30 nm expanded uncertainty is presented. In the cylindrical machine concept, an optical probe with 5 mm range is positioned over the surface by a motion system. Due to a 2nd order error effect when measuring smoothly curved surfaces, only 6 position measurement errors are critical (nanometer level). A separate metrology system directly measures these critical errors of the probe and the product relative to a metrology frame, circumventing most stage errors. An uncertainty estimation has been performed for the presented design, including a calibration uncertainty estimation and a dynamic analysis. Machine dynamics certainly cause relative motion between probe and product, but due to the non-contact nature of the measurement and the short metrology loop, these motions do not cause significant measurement errors. The resulting shape measurement error for aspheres up to medium free-forms is between 24 and 37 nm, and 30 - 85 nm for medium to heavily free-form surfaces. The suitability of the proposed design is herewith confirmed. A detailed design and a prototype of the machine are currently being developed.

/pdf/design-of-a-machine-for-the-universal-non-contact-n5aoaruo7c.pdf

Design of a machine for the universal non-contact measurement of large free-form optics with 30 nm uncertainty

In this study, the energetic loss models involved with gear shift and engine start in a parallel passenger hybrid electric vehicle (HEV) will be introduced, and their effects on energy management strategies will be analysed. The simulation results disclose a superior fuel efficient property of the powershift-automated manual transmission (PS-AMT) HEV over the AMT one. However, by reducing the interruption time in the gear shift process of the AMT as much as possible, its fuel deficiency, when compared to the PSAMT, can be reduced noticeably. Furthermore, with an assumption of given preview route information, a model predictive control (MPC) algorithm is applied to investigate an achievable fuel saving with respect to the prediction horizon. It reveals a minimum prediction horizon of 5 s is required for the MPC-based controller to get the possible maximum fuel economy under the impact of the engine start loss.

Effect of gear shift and engine start losses on energy management strategies for hybrid electric vehicles

This paper suggests and employs a method for reducing vibrations at the tip of a robot. The vibrations, caused by structural flexibility, are detected using accelerometers. The robot control system consists of two complementary sub-systems: a nominal motion controller and a vibration compensator. The former one realizes joint motions in accordance with a prescribed tip trajectory. The latter one robustly attenuates oscillations at the tip that are due to structural flexibility. The benefits of this set-up are experimentally verified for a spatial direct-drive robot with 3 revolute degrees of freedom.

/pdf/robust-attenuation-of-direct-drive-robot-tip-vibrations-3zpoxnadbf.pdf

Robust attenuation of direct-drive robot-tip vibrations

Ellipsoidal unfalsified control is a data-driven, plant-model-free control design method. In this work, this framework is extended to cover full-block multivariable controllers. A new controller structure and a sequential update procedure are proposed. A simulation example shows the effectiveness of the method.

/pdf/data-driven-multivariable-controller-design-using-4n0w3ookow.pdf

Data-driven multivariable controller design using Ellipsoidal Unfalsified Control

Nearly every task a domestic robot could potentially solve requires a description of the robot's environment which we call a world model. One problem underexposed in the literature is the maintenance of world models. Rather than on creating a world model, this work focuses on finding a strategy that determines when to update which object in the world model. The decision whether or not to update an object is based on the expected information gain obtained by the update, the action cost of the update and the task the robot performs. The proposed strategy is validated during both simulations and real world experiments. The extended series of simulations is performed to show both the performance gain with respect to a benchmark strategy and the effect of the various parameters. The experiments show the proposed approach on different set-ups and in different environments.

/pdf/semi-task-dependent-and-uncertainty-driven-world-model-xhq2j5pboa.pdf

M Maarten Steinbuch

Papers

Design of a machine for the universal non-contact measurement of large free-form optics with 30 nm uncertainty

Effect of gear shift and engine start losses on energy management strategies for hybrid electric vehicles

Robust attenuation of direct-drive robot-tip vibrations

Data-driven multivariable controller design using Ellipsoidal Unfalsified Control

Semi-task-dependent and uncertainty-driven world model maintenance