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 paper deals with the theoretical and experimental evaluation of the performance of a continuously variable transmission chain drive in steady-state conditions. We propose an enhanced version of the Carbone–Mangialardi–Mantriota (CMM) model, to accurately predict the slip behavior and the traction performance of the variator. To achieve this objective, it is necessary to accurately estimate the elastic displacements of the pulley. The determination of the actual pulley deformation allows to obtain a better estimation of the sliding velocities between the pins and the pulley surfaces and, therefore, to calculate the amount of total slip between the primary and secondary pulleys with a higher degree of accuracy. In contrast to multibody models, the approach presented here has a very simple formulation and results in an easy implementation, which allows a complete and fast evaluation of the variator working points. The theoretical results are discussed and critically compared with experimental data. The comparison confirms the validity of the CMM approach in a large range of clamping forces, speed ratios and torque loads. DOI: 10.1115/1.4000833

http://www.mate.tue.nl/mate/pdfs/11170.pdf

An Enhanced CMM Model for the Accurate Prediction of Steady-State Performance of CVT Chain Drives

A comparative overview of frequency domain methods for nonlinear systems

In this paper, a design methodology is developed to condtruct the gear shift map for the automotive transmissions used in conventional and hybrid electric vehicles. The methodology utilizes an optimal gear shift strategy to derive the optimal gear shift patterns over a wide range of driving profiles. Then, statistical theory is applied to analyse the obtained gear shift patterns for a gear shift map. The designed gear shift map improves the fuel economy and driveability. It is consistent, robust to shift busyness and real-time implementable. The design process is flexible and time efficient such that applicability to various powertrain systems configured with discrete-ratio transmissions is possible. Validation on a test vehicle proves the effectiveness of the design methodology.

Gear shift map design methodology for automotive transmissions

This paper presents the synthesis, the implementation and the performance-based tuning of an adaptive cruise control (ACC) stop-&-go (S&G) design. A model predictive control (MPC) framework is adopted to design the controller. Performance of the controller is evaluated, distinguishing between comfort of the resulting longitudinal vehicle behavior and the behavior due to traffic requirements. Corresponding metrics are incorporated in the controller design. Performance functions, relating the metrics explicitly to comfort and traffic requirements enable tuning of the resulting controller. As a result, the performance of the controller can be changed by a single performance-based weight. Simulation results for varying controller performance in various traffic situations are shown.

M Maarten Steinbuch

Papers

An Enhanced CMM Model for the Accurate Prediction of Steady-State Performance of CVT Chain Drives

A comparative overview of frequency domain methods for nonlinear systems

Gear shift map design methodology for automotive transmissions

Explicit MPC design and performance-based tuning of an Adaptive Cruise Control Stop-&-Go

Measuring the higher order sinusoidal input describing functions of a non-linear plant operating in feedback