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

http://www.mate.tue.nl/mate/pdfs/4380.pdf

Modeling and Simulation of an Electro-Mechanically Actuated Pushbelt Type Continuously Variable Transmission

Atomic force microscopes (AFMs) are used for sample imaging and characterization at nanometer scale. In this work, we consider a metrological AFM, which is used for the calibration of transfer standards for commercial AFMs. The metrological AFM uses a three-degree-of-freedom (DOF) stage to move the sample with respect to the probe of the AFM. The repetitive sample topography introduces repetitive disturbances in the system. To suppress these disturbances, repetitive control (RC) is applied to the imaging axis. A rotated sample orientation with respect to the actuation axes introduces a nonrepetitiveness in the originally fully repetitive errors and yields a deteriorated performance of RC. Directional repetitive control (DRC) is introduced to align the axes of the scanning movement with the sample orientation under the microscope. Experiments show that the proposed directional repetitive controller significantly reduces the tracking error as compared to standard repetitive control.

http://www.mate.tue.nl/mate/pdfs/11808.pdf

Directional Repetitive Control of a Metrological AFM

A nonlinear detailed model has been constructed for synchronous generators loaded with a rectifier bridge. This dynamic model is based on the description of the commutation and noncommutation stages. The switching instants depend on the state variables of the system. In order to be able to apply linear system theory for control design, an approximating linear model has been derived, based on application of the Floquet theory. Consequently, asymptotic stability analysis is possible with the linear model. Comparison with the full nonlinear model shows good resemblance. >

Dynamic modeling of a generator/rectifier system

High performance continuously variable transmission (CVT) operation requires a reliable control design for its actuation system. The aim of the present paper is to design a high performance robust controller for a range of operating conditions. High performance robust control is achieved by identifying a robust-control-relevant model set that represents the relevant dynamics of the actuation system for a range of operating conditions. Specifically, a new coordinate frame for representing model uncertainty is adopted that transparently connects the size of the model uncertainty and the control criterion, consequently a nonconservative control design can be obtained. Subsequent robust control synthesis reveals that robust performance has been significantly improved over the entire operating range, with respect to both the criterion value and relevant simulated and measured closed-loop step responses.

http://www.dct.tue.nl/toomen/files/OomenMeuBosSteElf2010.pdf

A robust-control-relevant model validation approach for continuously variable transmission control

This paper presents a novel control strategy for real-time controlled restraint systems. Todaypsilas restraint systems typically include a number of airbags, and a three-point seat belt with load limiter and pretensioner. In the class of realtime controlled restraint systems, the restraint actuator settings are continuously manipulated during the crash. The control strategy developed here is based on reference management, in which a nonlinear device - a reference governor - is added to a primal closed loop controlled system. This governor determines an optimal setpoint in terms of injury reduction and constraint satisfaction by solving a constrained optimization problem. Prediction of the vehicle motion, required to predict future constraint violation, is included in the design and is based on linear regression of past crash data. Simulation results with a MADYMO model show that a significant injury reduction is possible, without prior knowledge of the crash. Furthermore, it is shown that the algorithms are sufficiently fast to be implemented on-line.

/pdf/reference-governors-for-controlled-belt-restraint-systems-4nsg0ez18h.pdf

M Maarten Steinbuch

Papers

Modeling and Simulation of an Electro-Mechanically Actuated Pushbelt Type Continuously Variable Transmission

Directional Repetitive Control of a Metrological AFM

Dynamic modeling of a generator/rectifier system

A robust-control-relevant model validation approach for continuously variable transmission control

Reference governors for controlled belt restraint systems