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 AUV (autonomous underwater vehicle) of the University of Canterbury targets to discover any foreign organisms residing on the sea chests of ships, which cause a risk for the domestic biodiversity, and removes them. With the design of the AUV finished, the primary goal of this paper is to design control software that stabilizes the vehicle and minimizes the error in the desired trajectory. The dynamical model with implemented assumptions ultimately leads to a decoupled system of non-linear equations in three directions: surge, heave and yaw. For this system, experiments are designed (but not yet successfully accomplished) to identify the system parameters. With respect to control, a feedback linearization is firstly applied to a 1D case, which results in a satisfactory PIDcontroller, taking into account parameter perturbation and noise contamination. Finally, the under actuated problem in the 2D situation is evaluated, for which a path planning method and a state feedback control method is derived.

Modeling and simulated control of an under actuated autonomous underwater vehicle

http://www.mate.tue.nl/mate/pdfs/5565.pdf

Ellipsoidal Unfalsified Control applied to a motion system (ID 04265)

Thereal-time control ofthe3-DOFdynamics ofan electrodynamic (EDS)maglevvehicle ispresented. Thedesign is basedon a 5-DOFstate-space modelofthesleddynamics that usesa simple algebraic modeltodescribe theinteraction between thenull-flux coils on thetrackandthepermanentmagnetson thesled. A first ordersliding modecontroller withintegral error termisusedtocontrol heave, pitch androll inrealtimefrom position-attitude information measured withsensorslocated on thesled. Experimental results showthatcontrol ofthe3-DOF dynamics ofthelevitated vehicle inreal-time can besuccessfully achieved bytheproposed method. IndexTerms-Electrodynamic magnetic suspension, magnetic levitation, null-flux levitation, sliding modecontrol, magnetic servolevitation.

http://ieeexplore.ieee.org/iel5/4035454/4078480/04078648.pdf

Real-time Control ofthe3-DOESledDynamics of aNull-Flux MaglevSystemwithaPassive Sled

https://pure.tue.nl/ws/files/1748134/672918634105093.pdf

M Maarten Steinbuch

Papers

Modeling and simulated control of an under actuated autonomous underwater vehicle

Ellipsoidal Unfalsified Control applied to a motion system (ID 04265)

Real-time Control ofthe3-DOESledDynamics of aNull-Flux MaglevSystemwithaPassive Sled

Experimental frequency-domain method for control design in heavy-duty vehicles

Non-parametric H∞ control synthesis with suboptimal controller sets.