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

Piezoelectric actuators are commonly used to drive high-precision stages. In this paper, a walking piezo actuator containing four piezoelectric legs is used to drive a high-precision nanomotion stage with constant velocity and point-to-point reference signals. The gain of the system is dependent on the momentary orientation of the piezo legs during the walking movement. The aim of this paper is to design a feedback controller that employs knowledge of the varying gain of the drive legs over one drive cycle. The gain variation is determined by a combination of local and global identification and modeling techniques. The excitation signal for the local frequency response function measurements at different positions of the legs in the drive cycle is designed to avoid stick-slip effects between the drive legs and the drive surface of the stage. Using the knowledge of the system variations, a linear parameter-varying model and a gain scheduling feedback controller are designed for the nanomotion stage. Experiments show that the designed gain scheduling controller reduces the tracking error and settling time compared to a robust H∞ controller with a comparable closed-loop bandwidth up to 53% and 80%, respectively.

Gain Scheduling Control of a Walking Piezo Actuator

This paper proposes a probabilistic object-object relation based approach for an active object search. An important role of mobile robots will be to perform object-related tasks and active object search strategies deal with the non-trivial task of finding an object in unstructured and dynamically changing environments. This work builds further upon an existing approach exploiting probabilistic object-room relations for selecting the room in which an object is expected to be. Learnt object-object relations allow to search for objects inside a room via a chain of intermediate objects. Simulations have been performed to investigate the effect of the camera quality on path length and failure rate. Furthermore, a comparison is made with a benchmark algorithm based the same prior knowledge but without using a chain of intermediate objects. An experiment shows the potential of the proposed approach on the AMIGO robot.

/pdf/active-object-search-exploiting-probabilistic-object-object-4lwgk9r1u3.pdf

Active Object Search Exploiting Probabilistic Object-Object Relations

The plasma needle is an atmospheric non-thermal plasma source. The plasma has bactericidal properties and can be used for tissue disinfection. In dentistry, it may provide a painless, non-contact and tissue saving method for treatment of dental cavities (caries). To this end it has been shown that it is possible to eliminate bacteria that are present in a dental environment Streptococcus mutans or S. mutans. An experiment on a simple model of a dental element has been executed, which shows the plasma is able to penetrate a cavity (1 mm wide) for 5.5-8.0 mm deep and kill the present bacteria.

Plasma treatment of S. mutans biofilms cultured in a simulated dental cavity model

This paper considers robust control design for singleloop compact disc mechanisms. The design problem is to achieve good track-following in the presence of disturbances and parametric plant uncertainty. This robust performance problem has been solved in the quantitative feedback theory (QFT) framework using measured frequency response data. The design has been implemented successfully in a digital signal processor (DSP) environment.

Design and implementation of a qft controller for a compact disc player

Suppression of tearing modes is essential for the operation of tokamaks. This paper describes the design and simulation of a tearing mode suppression feedback control system for the TEXTOR tokamak. The two main control tasks of this feedback control system are the radial alignment of electron cyclotron resonance heating and current drive (ECRH/ECCD) with a tearing mode and the stabilization of a mode at a specific width. In order to simulate these control tasks, the time evolution of a tearing mode subject to suppression by ECRH/ECCD and destabilization by a magnetic perturbation field is modelled using the generalized Rutherford equation. The model includes an equilibrium model and an ECRH/ECCD launcher model. The dynamics and static equilibria of this model are analysed. The model is linearized and based on the linearized model, linear feedback controllers are designed and simulated, demonstrating both alignment and width control of tearing modes in TEXTOR.

M Maarten Steinbuch

Papers

Gain Scheduling Control of a Walking Piezo Actuator

Active Object Search Exploiting Probabilistic Object-Object Relations

Plasma treatment of S. mutans biofilms cultured in a simulated dental cavity model

Design and implementation of a qft controller for a compact disc player

Systematic design and simulation of a tearing mode suppression feedback control system for the TEXTOR tokamak