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.

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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

Sjoerd van den Dries Jos Elfring René van de Molengraft Maarten Steinbuch I. INTRODUCTION Domestic robots are typically confronted with a complex, dynamically changing and unstructured environment. In order to adequately perform tasks such as safe navigation and object manipulation, robots need an accurate description of the world. Such an environmental description, here referred to as world model, is constructed based on perceived object features and must store and keep track of the objects in the world and their attributes. Requirements of such a world modeling algorithm are: (1) semantic richness: object descriptions must be semantically annotated, i.e., stored with human-readable attributes such as position, color and class such that human-defined tasks can be mapped to the world description, and the algorithm must allow multiple attributes to be stored and maintained simultaneously; (2) probabilistic nature: due to sensor limitations and prediction uncertainty, uncertainty must be explicitly represented and dealt with in the system by using probabilistic representations and calculations; (3) data association and attribute maintenance: measured object features must be linked to and update object descriptions, while dealing with possible ambiguities; (4) exploitation of prior knowledge: existing knowledge about the behavior of objects, e.g., movement, expected locations, should be used as much as possible; (5) computationally feasible and scalable: the system must run real-time on a robot, even in cluttered scenarios with many objects. We present Probabilistic Multiple Hypothesis Anchoring (PMHA), a world modeling algorithm that fulfills these requirements by uniquely extending perceptual anchoring ([2]) with an efficient Bayesian multiple hypothesis framework ([3], [1]) and a generalized multiple-model object tracking approach ([4]).

World modeling in robotics : probabilistic multiple hypothesis anchoring

The investigation of closed-loop systems subject to model perturbations is an important issue in order to assume stability robustness of a control design. A large variety of model perturbations can be described by norm-bounded uncertainty models. In this paper a general approach for modelling structured complex and real-valued parametric perturbations is presented. The resulting robustness analysis problem is solved nonconservatively using real and complex structured singular value calculations. The uncertainty modelling and the robustness analysis are shown for a high-accuracy 5D electromechanical positioning device to be used in optical recording. >

Parametric uncertainty modelling and real and complex structured singular value computation applied to an electro-mechanical system

MIMO fitting tool for motion systems with flexible structures

This paper deals with the critical problem of actuator limitations for the successful implementation of active surge control. We specify the capacity, bandwidth and allowable time delay for a control valve that can be used to actively suppress surge in a specific full-scale centrifugal compression system. The actuator requirements are obtained from closed-loop simulations with a nonlinear simulation model of the compressor test rig. In order to meet the requirements, a new high-speed valve actuator had to be developed. We present the resulting actuator design and provide test results to illustrate that the control valve meets our design specifications.

/pdf/a-high-speed-valve-for-surge-control-in-a-centrifugal-2ovy2js6c3.pdf

M Maarten Steinbuch

Papers

World modeling in robotics : probabilistic multiple hypothesis anchoring

Parametric uncertainty modelling and real and complex structured singular value computation applied to an electro-mechanical system

MIMO fitting tool for motion systems with flexible structures

A high-speed valve for surge control in a centrifugal compression system

Friction in motion systems : performance deterioration for regular tasks