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

Blind identification of multivariable disturbances

• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers.

/pdf/experimental-identification-and-validation-of-model-22i1iylnik.pdf

Experimental identification and validation of model parameters for a centrifugal compression system

Domestic robots are typically confronted with a complex, dynamically changing and unstructured environment. In such environments, robots must be able to perform a wide variety of tasks, e.g., safe navigation, object manipulation, including many different objects. An important prerequisite for successfully accomplishing these tasks is the availability of an accurate description of the environment the robot is operating in. In this work, such a description of the environment will be referred to as world model. A world model usually contains 3D-positions of uniquely labeled objects. In dynamic scenarios, dynamic object attributes such as velocities are included as well [1] and depending on the tasks any other object attribute can be added. The robot perceives the world using a perceptual system possibly containing multiple sensors such as cameras and laser scanners. The perceptual system generates measurements, i.e., time-stamped features, that can be anything from colored blobs in a camera image [2] to labeled object positions [1]. These measurements are the input for the algorithm that constructs the world model.

https://pure.tue.nl/ws/files/3714111/447595792270642.pdf

World modeling in robotics

In high tech motion systems, the finite stiffness of mechanical components usually limits the bandwidth of the control system. This can be counteracted by increasing the controller complexity. The resonance frequencies and the amount of damping of the non-rigid body dynamics significantly affect the achievable bandwidth. This paper describes a method to add damping to the flexible behavior of a motion stage, by using tuned mass-dampers with very high local damping. This results in a control bandwidth improvement of over 150%, which leads to a disturbance suppression of over 10 times for both force and displacement disturbances.

Bandwidth increase by shaping the plant in combination with the controller — The role of damping in positioning tables

Feedforward control is the key method to improve the servo performance of high-precision motion systems. In typical applications of motion control, a low-order feedforward controller is designed that approximates the inverse of the system. For example, the servo performance of systems with dominant rigid-body dynamics can be effectively enhanced by acceleration feedforward following the second law of Newton.

M Maarten Steinbuch

Papers

Blind identification of multivariable disturbances

Experimental identification and validation of model parameters for a centrifugal compression system

World modeling in robotics

Bandwidth increase by shaping the plant in combination with the controller — The role of damping in positioning tables

Advances in optimization-based feedforward tuning