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 idea is to use a linear parameter varying (LPV) controller for an LTI plant to facilitate different performance properties in different parameter ranges. Consider for example the intentionally LPV closed-loop system CP 1+CP = 2 s2+( √ 8−2e)s+2 with error-dependent damping. The step response is depicted in black together with the responses of the base linear system with various damping ratios. i i

An exploration of nonlinear control of linear motion systems

In this report a traction controller is developed for an omni-directional robot, named Turtle. At first traction and slip are defined and the geometry of the Turtle is discussed. Then the explanation of the relevant software for the motion of the Turtles is spread over three chapters. The motion schemes of the Turtle in Simulink are simplified and discussed. The trajectory planner that creates the driving path for a Turtle is explained in detail and simulations are performed to show what kind of signals the trajectory planner creates and how these signals are alternated as inputs to the motors. After this some research has been done into control systems of other vehicles and some initial ideas are sketched for traction control in the Turtles. Finally the actual traction controller is developed and explained using flowcharts, the implementation (in C-code) of the traction controller and some simulations to give a quick look for how it works. Afterwards a general conclusion is drawn and some future work is suggested.

Traction control for omni-directional robots

http://www.nt.ntnu.no/users/skoge/prost/proceedings/ifac2014/media/files/2032.pdf

M Maarten Steinbuch

Papers

An exploration of nonlinear control of linear motion systems

Traction control for omni-directional robots

Frequency Domain Tuning Method for Unconstrained Linear Output Feedback Model Predictive Control

Design of a New Image-Guided Surgical Robot for Precision Bone Drilling in the Lateral Skull Base

Model-based real-time scenario monitoring for transient event prediction with RAPTOR