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

This paper describes a modeling and design approach in finding the optimal controller for a low-cost hybrid drive train on a pre-defined driving cycle. The hybrid drive train is characterized by a small energy capacity (flywheel) and discrete shifts between operation modes, due to the use of clutches. The main design criterion, is the minimization of the overall fuel consumption, whereas constraints are defined to describe the systems dynamics, kinematics and constraints. In addition, comfort criteria are defined as constraints, to avoid uncomfortable driving conditions. These criteria are used to find the optimal sequence of driving modes and generated engine torque over a defined driving cycle. Simulations show a fuel saving potential of 22% to 39%, dependent on the chosen driving cycle.

http://www.mate.tue.nl/mate/pdfs/12525.pdf

Control optimization for a low-cost flywheel module

s presented at the Medical Physics and Engineering 2003 - VIII EFOMP Congress - 30th NVKF Annual Scientific Meeting (May 20-23, 2003, Eindhoven, The Netherlands)

Robust controller design for a teleoperated forceps

Control design for bilateral teleoperation is still an open problem, given that it is desirable to meet a proper balance in the inherent trade-off between transparency and stability. We propose the use of switching robust control, in which smooth switching among controllers is achieved by the existence of multiple Lyapunov functions with a special structure, linked by conditions of maximum average dwell time switching among controllers. We show the advantage of the proposed method by means of a control design synthesis for an 1-DoF teleoperation system, and by means of simulations of the corresponding closed loop system.

Switching Robust Control Synthesis for Teleoperation via Dwell Time Conditions

Occupant safety can be significantly improved by continuous restraint control systems. These restraint systems adjust their configuration during the impact according to the actual operating conditions, such as occupant size, weight, occupant position, belt usage and crash severity. In this study, the potential of a controlled restraint system is demonstrated. First, an overview is given of the problems concerning the sensors, actuators and control strategy of such a system, and solutions are given. Next, a numerical demonstrator is developed, which includes a dummy and vehicle model, and a realistic implementation of the components of the controlled restraint system. The demonstrator is subjected to different loading conditions, and the results are compared to a reference model. This reference model contains a conventional restraint system with optimized settings, and it has been validated against sled test experiments. Simulation results with the demonstrator indicate that significant injury reduction can be achieved with continuous restraint control systems.

http://www.mate.tue.nl/mate/pdfs/9884.pdf

M Maarten Steinbuch

Papers

Control optimization for a low-cost flywheel module

Robust controller design for a teleoperated forceps

Switching Robust Control Synthesis for Teleoperation via Dwell Time Conditions

Continuous restraint control systems: safety improvement for various occupants

Heat transport modeling in tokamaks : for acquiring a simulation model by using different spatial discretization methods