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

For systems that perform repetitive tasks, a high performance feedforward signal can be derived using iterative learning control (ELC). The feedforward signal is updated through successive iterations. Disturbances present in the control scheme, such as load and measurement disturbances, are also present in the learning process and deteriorate the performance of ILC. This paper presents an expression for the tracking error of an arbitrary iteration which shows the influence of measurement and load disturbances of the present and previous iterations. The expression is validated by means of simulations and experiments on a motion system. The disturbances of the last two iterations prove to have the largest influence on the tracking error

The influence of disturbances in iterative learning control

Future large optical telescopes require adaptive optics (AO) systems whose deformable mirrors (DM) have ever more degrees of freedom. This paper describes advances that are made in a project aimed to design a new AO system that is extendible to meet tomorrow's specifications. Advances on the mechanical design are reported in a companion paper [6272-75], whereas this paper discusses the controller design aspects. The numerical complexity of controller designs often used for AO scales with the fourth power in the diameter of the telescope's primary mirror. For future large telescopes this will undoubtedly become a critical aspect. This paper demonstrates the feasibility of solving this issue with a distributed controller design. A distributed framework will be introduced in which each actuator has a separate processor that can communicate with a few direct neighbors. First, the DM will be modeled and shown to be compatible with the framework. Then, adaptive turbulence models that fit the framework will be shown to adequately capture the spatio-temporal behavior of the atmospheric disturbance, constituting a first step towards a distributed optimal control. Finally, the wavefront reconstruction step is fitted into the distributed framework such that the computational complexity for each processor increases only linearly with the telescope diameter.

/pdf/distributed-control-in-adaptive-optics-deformable-mirror-and-476rgvcgai.pdf

Distributed control in adaptive optics: Deformable mirror and turbulence modeling

Optimal higher-order encoder time-stamping

http://www.mate.tue.nl/mate/pdfs/4326.pdf

Analytical and experimental modelling for gain-scheduling of a double scara robot

The real-time control of the 3-DOF dynamics of an electrodynamic (EDS) maglev vehicle is presented. The design is based on a 5-DOF state-space model of the sled dynamics that uses a simple algebraic model to describe the interaction between the null-flux coils on the track and the permanent magnets on the sled. A first order sliding mode controller with integral error term is used to control heave, pitch and roll in real time from position-attitude information measured with sensors located on the sled. Experimental results show that control of the 3-DOF dynamics of the levitated vehicle in real-time can be successfully achieved by the proposed method.

M Maarten Steinbuch

Papers

The influence of disturbances in iterative learning control

Distributed control in adaptive optics: Deformable mirror and turbulence modeling

Optimal higher-order encoder time-stamping

Analytical and experimental modelling for gain-scheduling of a double scara robot

Real-time Control of the 3-DOF Sled Dynamics of a Null-Flux Maglev System with a Passive Sled