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

The inherent transparency-stability tradeoff in bilateral teleoperation poses a challenge to design controllers that find a proper balance between both requirements. Furthermore, when the environment of the teleoperation system varies within a large range, a single controller might not be sufficient to achieve both stability and high performance. In this paper, we propose the synthesis of a switching robust controller that allows to design multiple robust controllers suitable for different ranges of environment stiffness, so that an overall better performance is achieved. The controllers are scheduled using an estimate of the environment stiffness. During the design, we account for smooth switching among controllers and for the fact that environment estimators will have limited accuracy and uncertainty. We present synthesis, simulation, and experimental results of the proposed approach, thus showing the effectiveness of the method to improve the robust performance of the teleoperated system.

Switching Robust Control for Bilateral Teleoperation

Continuously variable transmissions (CVT) can be used to operate a combustion engine in a more optimal working point. Unfortunately, due to the relatively low efficiency of modern production CVT's the total efficiency of the driveline is not increased significantly. This low efficiency is mainly caused by losses in the hydraulic actuation system and the variator. Decreasing the clamping forces in the variator greatly improves the efficiency of the CVT. However, lower clamping forces increase the risk of excessive belt slip, which can damage the system. In this paper a method is presented to measure and control slip in a CVT in order to minimize the clamping forces while preventing destructive belt slip. To ensure robustness of the system against torque peaks, a controller is designed with optimal load disturbance response. A synthesis method for robust PI(D)-controller design is used to maximize the integral gain while making sure that the closed loop system remains stable. Experimental results prove the validity of the approach.

/pdf/slip-controller-design-and-implementation-in-a-continuously-2p73ro9vf2.pdf

Slip controller design and implementation in a continuously variable transmission

In printing systems, the positioning accuracy of the medium with respect to the print heads directly impacts print quality. In a regular document inkjet printer, the main task of the media positioning drive is to shift the medium after the printhead has finished a pass. Most media have the tendency to deform while it is being printed due to variations in temperature and moisture content. In order to improve print quality, we propose to move the medium during printing to counteract the deformation. These small scale trajectories are performed in an operating regime of which the dynamics considerably differ from the regular transportation step. Using iterative learning control with basis functions for both positioning tasks, the positioning accuracy of the drive is improved substantially; while keeping numerical cost low.

/pdf/iterative-learning-control-with-basis-functions-for-media-56njrybasq.pdf

Iterative learning control with basis functions for media positioning in scanning inkjet printers

This paper presents a method to perform model-free fixed structure controller synthesis. Based on frequency response data of the plant, the parameters of a predefined controller structure are optimized directly with respect to closed-loop performance specifications. As a result, no parametric plant model is required such that time consuming iterative identification-synthesis procedures can be omitted. A framework is presented to both assure stability and optimize closed-loop performance based on frequency response data. Furthermore, based upon these results, a cost-function is formulated that can be exploited to converge from a destabilizing- to the stabilizing controller parameter region. Both the stability guarantee and performance optimization procedures are combined in one optimization algorithm that is illustrated by means of an example.

/pdf/model-free-norm-based-fixed-structure-controller-synthesis-3u0hklx3i8.pdf

M Maarten Steinbuch

Papers

Switching Robust Control for Bilateral Teleoperation

Slip controller design and implementation in a continuously variable transmission

Iterative learning control with basis functions for media positioning in scanning inkjet printers

Model-free norm-based fixed structure controller synthesis

Inversion-free design algorithms for multivariable quantitative feedback theory: an application to robust control of a CD-ROM