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

The tracking performance of systems that perform repetitive tasks can be significantly improved using iterative learning control (ILC). During successive iterations, ILC learns a high performance feedforward signal from the measured tracking error. In practice, the tracking error consists of both a repetitive part which is equal every iteration and a non-repetitive part which varies every iteration. ILC can only compensate for the repetitive part, the non-repetitive part limits the achievable performance of ILC. In this paper, a wavelet based filtering method is presented which identifies and removes the non-repetitive part of the tracking error by a comparison of two error realizations for each iteration of ILC. The filtered error signal is used as input for the learning scheme of ILC. Simulations and experiments show that the wavelet filtering method improves the performance of ILC, resulting in a smaller tracking error and in a learned feedforward signal that contains significantly less non-repetitive disturbances.

Removing non-repetitive disturbances in iterative learning control by wavelet filtering

This paper presents a practical extension to the classical gradient-based extremum seeking control for the case when the disturbances responsible for the changes in the extremum of a related performance function can be measured. The additional information is used to improve accuracy, convergence speed and robustness of the underlying ESC scheme. Based on the disturbance measurements a map between them and the optimal inputs is iteratively constructed and used as an extremum seeking feedforward. A supervising state-machine is designed to regulate feedforward and search processes ensuring the latter is conducted in the close vicinity of an extremum. The search is based on the sinusoidal input perturbation introduced each time the disturbance is detected and removed once the optimal set-point is identified. Simulation results for the cases of photovoltaic and turbine driven electrical generator systems demonstrate the benefits of the presented design.

Extremum seeking control with data-based disturbance feedforward

One way to predict and control the output torque of a geared neutral CVT is to use the relationship between slip in the variator and transmitted belt torque. This relationship is often referred to as the traction curve. For small slip values, the output torque increases almost linearly with slip. However, at approximately 2% slip, the maximum output torque is reached. For larger slip values, the output torque shows even a decline. Hence, measuring the traction curve in open loop is not possible. Stability analysis shows that by modifying the plant with a nonlinear feedback control torque depending on the slip, a stable closed loop plant with two new defined inputs is obtained. An open loop input torque at this plant now uniquely defines the slip in the system. The primary shaft angular velocity is controlled using a robust stabilizing controller. This controller is tuned using measured frequency response functions at different slip values.

http://www.mate.tue.nl/mate/pdfs/4367.pdf

Nonlinear stabilization of slip in a continuously variable transmission

μ-Analysis and synthesis toolbox μ-tools) : G. J. Balas, J. C. Doyle, K. Glover, A. Packard and R. Smith : software review

Iterative Learning Control (ILC) enables performance improvement by learning from previous tasks. The aim of this paper is to develop an ILC approach for Linear Parameter Varying (LPV) systems to enable improved performance and increased convergence speed compared to the linear time-invariant approach. This is achieved through dedicated analysis and norm-optimal synthesis of LPV learning filters. Application to a position-dependent motion system shows a significant improvement in accuracy and convergence rate, thereby confirming the potential of the proposed approach.

/pdf/iterative-learning-control-and-feedforward-for-lpv-systems-374ylpfyeh.pdf

M Maarten Steinbuch

Papers

Removing non-repetitive disturbances in iterative learning control by wavelet filtering

Extremum seeking control with data-based disturbance feedforward

Nonlinear stabilization of slip in a continuously variable transmission

μ-Analysis and synthesis toolbox μ-tools) : G. J. Balas, J. C. Doyle, K. Glover, A. Packard and R. Smith : software review

Iterative Learning Control and feedforward for LPV systems: Applied to a position-dependent motion system