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

Design Of A Compact Robotic-assisted Ophthalmic Surgical System

Purpose:Developments in vitreo-retinal eye surgery are limited by human capabilities. To improve current vitreo-retinal surgical procedures and to enable new procedures, a robotic system has been developed, extending human capabilities. Methods:A compact, lightweight, easy to setup robotic master-slave system has been realized to perform vitreo-retinal eye surgery (Slave see Figure 1, Master see Figure 2). The system’s reach covers the major part of the vitreous cavity (up to the peripheral region). A combination of advanced mechanical and control design facilitates high accuracy (

Ex-vivo experiments with a microrobotic surgical system for vitreo-retinal surgery

The number of people with hearing loss and tumours increase, as the population is getting older and the world is getting louder. This poses increasing demands on healthcare, both in numbers and capabilities. For a number of specific applications new medical robots possibly can increase quality as well as reduce costs of healthcare. A new precision surgical robot design is presented which enables precise, safe, fast and less invasive bone removal, with a focus on procedures in the skull (base) and ear. The compact robot can operate autonomously using CT image data, but can also be driven manually by the surgeon. After determination of the robots' requirements, a deterministic process was used to select the best concept from multiple generated robotic concepts. A compact serial robotic arm with seven axes, built from modular units, gives the possibility to have a high rigidity, dexterity, large range of motion and low-cost design, while being able to achieve sufficient accuracy of an estimated 50 μm. Moreover, it is designed to withstand milling and drilling forces up to 50 N and usable in the medical operating room with minimal intervention. The weight of the seven- Axis robot with its base is estimated to be 9 kg. In future medical operating rooms, this precision robot may reduce the amount of bone removed up to a factor 30, could improve safety and could enable new surgical procedures. It might reduce bone-milling time from hours into minutes, being able to help more patients and reduce costs. Exact numbers will be generated after realisation and testing of one module and the full robot, which will be steps in future research.

/pdf/a-new-image-guided-surgical-robot-for-precision-bone-2z15j3u9c3.pdf

A new image guided surgical robot for precision bone sculpturing

http://www.dct.tue.nl/toomen/files/RijlaarsdamOomNuiSchSte2012b.pdf

New connections between frequency response functions for a class of nonlinear systems

A instrument manipulator to perform vitreo-retinal eye surgery is designed. The result is presented here.

M Maarten Steinbuch

Papers

Design Of A Compact Robotic-assisted Ophthalmic Surgical System

Ex-vivo experiments with a microrobotic surgical system for vitreo-retinal surgery

A new image guided surgical robot for precision bone sculpturing

New connections between frequency response functions for a class of nonlinear systems

A slave robot for vitreo-retinal ophthalmic surgery