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

Several control strategies are compared with respect to their performance for regulator tasks on mechanical systems that exhibit friction. For this purpose a classic PID-controller combined with mass and frictional feedforward is compared to (i) a PID-controller combined with a model-based friction compensation using the dynamic LuGre friction model and (ii) a gain-scheduled optimal PD-controller based on a polytopic linear model (PLM). The latter consists of a feedforward part and an optimal nonlinear feedback part. The controllers are compared to the classic PID-controller by means of experiments on a rotating arm subjected to friction. The performance for three third order point to point setpoints shows that the gain-scheduled optimal PD-controller outperforms the other controllers with respect to settling time and maximal error after setpoint. The tracking performance is comparable for the LuGre-based controller and the classic PID-controller where the tracking performance of the gain-scheduled PD-controller is limited.

High performance regulator control for mechanical systems subjected to friction

Optimal Shifting Strategy for a Parallel Hybrid Electric Vehicle

Iterative Learning Control (ILC) is a technique for improving the performance of systems or processes that operate repetitively over a x ed time interval. The basic idea of ILC is that it exploits every possibility to incorporate past repetitive control information, such as tracking errors and control input signals into the construction of the present control action. Past control information is stored and then used in the control action in order to ensure that the system meets the control specications such as convergence. The goal of the research presented in this paper is to liken two different ILC techniques applied to the wafer stage of a wafer scanner motion system. Namely, we consider briey the concepts of standard and lifted ILC and we evaluate the ILC performance in terms of tracking errors.

/pdf/standard-and-lifted-approaches-of-iterative-and-learning-39u983pp06.pdf

Standard and lifted approaches of iterative and learning control applied on a motion system

To minimize occupant injuries, passive in-vehicle safety systems like the safety belt and the airbag restrain the occupant during a crash. This paper presents a design approach for a feedback controller for the belt force to reduce the maximum chest acceleration as a measure for the risk of occupant injuries. Only frontal crashes are considered. The available, experimentally validated numerical crash model is too complex to be used as a controller design model. Therefore, approximate linear models for the transfer from belt force to chest acceleration are derived by analysing the effect of stepwise perturbations of the belt force on the chest acceleration. Using these linear models, loop shaping is applied to arrive at a controller that satisfies a set of a priori defined criteria. The controller is implemented in and evaluated with the complex crash model, showing that a reduction of approximately 60 per cent in the adopted injury measure can be achieved. Furthermore, it is shown that this approach can be applied in different situations.

Identification and control of a vehicle restraint system

This paper shows the implementation of a simulation model for new electromechanically actuated metal V-belt type Continuously Variable Transmission (CVT), referred to as the Empact CVT. An analysis of the dynamics of the actuation system and of the driveline shows that the eigenfrequencies of the system depend on both the CVT ratio and the slip in the variator. An accurate variator model is required to incorporate all characteristic dynamics. The implemented variator model is an explicit formulation of a model which gives an estimation of the tension forces and compression forces in the pushbelt. The simulation model also includes slip, shifting losses based on transient variator models and friction. Simulations are compared to measurements, showing good results.

/pdf/the-empact-cvt-modelling-simulation-and-experiments-1142v2lsv5.pdf

M Maarten Steinbuch

Papers

High performance regulator control for mechanical systems subjected to friction

Optimal Shifting Strategy for a Parallel Hybrid Electric Vehicle

Standard and lifted approaches of iterative and learning control applied on a motion system

Identification and control of a vehicle restraint system

The Empact CVT: modelling, simulation and experiments