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

Current state-of-the-art optical lithography scanners using 193nm wavelength lasers and numerical apertures of 1.35 have reached fundamental printing limits. Yet, consumer demands and device trends continue to drive smaller feature sizes, and most IC manufacturers have already navigated beyond the lithographic printing limits by turning to double patterning techniques. 1 Requiring an extra lithography step for these techniques, it is essential to keep costs down by e.g. increasing wafer throughput. Currently, leading edge immersion scanners consistently produce over 190 wafers per hour (wph). However, to keep decreasing the cost per transistor, higher throughputs of 250 wph are key targets for the year 2013 2 . Amongst others, higher throughput can be acquired by increasing acceleration of the positioning stages. One of the constraining technologies is the current mask or reticle clamping concept due to its friction based acceleration. While current reticle accelerations amount to 150 m/s 2 , some research 3 has already been performed to reticle stage accelerations of 400 m/s 2 . In this paper, a novel reticle clamping concept is presented. The concept is shown to be suitable for accelerations larger than 400 m/s 2 entirely eliminating reticle slip, whilst meeting specifications for clamping induced error with a pattern deformation of < 0.12 nm on wafer stage level (WS) and comprising high clamp stiffness.

Mask side wall clamping

Micrometer-precision penetration motion in robot-assisted vitreoretinal surgery

This paper presents an Iterative Learning Control algorithm for direct-drive robots. The learning algorithm assumes linear dynamics, which is created using a nonlinear model-based compensator. The convergence criterion of the learning controller is derived in the frequency domain. Rules for designing the filters, used in the update law, are explained. The effectiveness of the algorithm is demonstrated in experiments on a spatial direct-drive robot. The root-mean-square values of the tracking errors in a demanding writing task are over 10 times smaller after just eight iterations of the learning algorithm, compared with the errors before learning.

Frequency domain iterative learning control for direct-drive robots

In fixed-point implementation of digital controllers, the effects of quantisation can be reduced by proper scaling of the controller parameters. Commonly, controllers are scaled using l/sub 1/ norm based calculations, i.e. determining the worst case impulse response of the controller. However, this is only possible if the controller is asymptotically stable. If this is not the case, a solution is to calculate the scaling transformation on the basis of the closed-loop impulse responses. From the experience with actual implementation on a compact disc system, frequency weighting reflecting the disturbance spectrum has to be applied as well in order to obtain good experimental results.

Closed loop l/sub 1/ scaling for fixed-point digital control implementation

Current trends towards lightweight positioning systems from the lithography industry demand the usage of advanced servo control design methods that can actively control the unavoidable flexibilities. One solution is to use overactuation, [1], and over-sensing and to control the rigid and flexible modes of the system in a decentralized manner. Compared with the traditional decentralized control approach, in this work a more effective and a less conservative way towards a decentralized modal control is investigated for a 6 degrees of freedom motion stage. This paper describes a method to compute decoupling matrices Fu and Fy (Figure 1) that decouple the system in its rigid body(RB) modes and a number of non-rigid body(NRB) modes. Alignment and scaling of the rigid body modes are ensured. The decoupling enables the design of multi-loop SISO controllers, K, for the decoupled MIMO system, Gd.

/pdf/modal-decoupling-of-a-lightweight-motion-stage-using-5d441rt7a8.pdf

M Maarten Steinbuch

Papers

Mask side wall clamping

Micrometer-precision penetration motion in robot-assisted vitreoretinal surgery

Frequency domain iterative learning control for direct-drive robots

Closed loop l/sub 1/ scaling for fixed-point digital control implementation

Modal decoupling of a lightweight motion stage using algebraic constraints on the decoupling matrices