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

Advanced motion systems like pick-and-place machines used in the semiconductor industry challenge the frontiers of mechatronic design, systems and control theory and practice. In the design phase, control oriented design of the electro-mechanics is necessary in order to achieve the tight performance specifications. Once realized, and since experimentation is fast, a machine in the loop procedure can be explored to close the design loop from experiment, experimental model building, model based control design, implementation and performance evaluation. Nevertheless, reliable numerical tools are required to meet the challenges posed with respect to dimensionality and model complexity. Extension of linear modeling techniques towards some classes of nonlinear systems is relevant for improved control of specific motion systems, such as with friction. Finally, medical robotics can greatly benefit from the experiences in high tech motion systems, and an eye surgical robot with haptics will be shown as an example. Other challenging applications in need for advanced design, modeling and control are fuel-efficient vehicles, including ultra-clean engines, vehicle electric and hybrid power trains, and control of plasma fusion.

Design and control of high tech systems

In this paper we present a general LMI-based analysis method to determine an upperbound on the L2 gain performance of a reset control system These computable sufficient conditions for L2 stability, based on piecewise quadratic Lyapunov functions, are suitable for all LTI plants and linear-based reset controllers, thereby generalizing the results available in literature Our results furthermore extend the existing literature by including tracking and measurement noise problems by using strictly proper input filters We illustrate the approach by a numerical example

/pdf/an-lmi-based-l-2-gain-performance-analysis-for-reset-control-3jmhwh5nq5.pdf

An LMI-based L 2 gain performance analysis for reset control systems

Current hybrid drivetrain simulation packages are based on discrete (existing) system components and predefined system structures. Optimization of the performance of the hybrid drivetrain is then based on finding the most efficient control strategy of the primary and secondary power source and finally comparing the performance of the different candidate drive-trains. In this paper, the secondary power source components, part of the energy storage system (S), are modeled continuously, i.e., scalable to power and/or energy capacity needs. In this way, the size of the components of S can be added as an optimization parameter to a hybrid drivetrain design procedure.

Optimal design of energy storage systems for hybrid vehicle drivetrains

We promote the idea of an experimentally supported control design as a successful way to achieve accurate tracking; of reference robot motions, under disturbance conditions and given the uncertainties arising from modeling errors. The H/sub /spl infin// robust control theory is used for design of motion controllers. Potential of the theory is additionally enhanced by incorporating a disturbance-based control design cycle. Within each iterative cycle we experimentally evaluate effects of designed H/sub /spl infin// controllers on a direct-drive robotic set-up. The controllers resulting from such iterative design are indeed specialized for this robot, but they significantly improve both performance and robustness against disturbances and modeling errors, as compared with conventional industrial motion controllers. Superior performance is experimentally demonstrated in both configuration (joint) and task (Cartesian) spaces of the robot.

/pdf/experimentally-supported-control-design-for-a-direct-drive-i6msyeof0o.pdf

Experimentally supported control design for a direct drive robot

This paper develops a new set of necessary and sufficient conditions for the stability of linear repetitive processes, based on a dissipative setting for analysis. These conditions reduce the problem of determining whether a linear repetitive process is stable or not to that of checking for the existence of a solution to a set of linear matrix inequalities (LMIs). Testing the resulting conditions only requires compu- tations with matrices whose entries are constant in comparison to alternatives where frequency response computations are required.

/pdf/dissipative-stability-theory-for-linear-repetitive-processes-3czr5eyviy.pdf

M Maarten Steinbuch

Papers

Design and control of high tech systems

An LMI-based L 2 gain performance analysis for reset control systems

Optimal design of energy storage systems for hybrid vehicle drivetrains

Experimentally supported control design for a direct drive robot

Dissipative stability theory for linear repetitive processes with application in iterative learning control