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 invention relates to a device for creating a local cold plasma at the location of an object, said device at least comprising a high-frequency power source, a plasma chamber, a plasma discharge electrode disposed in said plasma chamber, which is electrically connected to said high-frequency power source, as well as a supply line for a plasma gas, which opens into the plasma chamber at a location near the plasma discharge electrode. The object of the invention is to provide an improved device as referred to in the introduction, which enables an improved control of the created plasma in relation to the object. To that end, the device according to the invention is characterized in that the device comprises adjusting means arranged for automatically orienting the plasma discharge electrode relative to the object.

Device for creating a local plasma at the location of an object

This work discusses reference trajectory relevant model based feedforward design. For motion systems which contain at least one rigid body mode and which are subject to reference trajectories with mostly low frequency energy, the proposed feedforward controller improves tracking performance significantly. The feedforward controller may be of much lower order than the plant. The proposed feedforward controller is introduced using a model of an industrial XY-table as an application example.

/pdf/jerk-derivative-feedforward-control-for-motion-systems-33d3geph73.pdf

Jerk derivative feedforward control for motion systems

Piezo actuators are used in high-precision systems that require nanometer accuracy. In this paper, we consider a nano-motion stage driven by a walking piezo actuator, which contains four bimorph piezo legs. We propose a (model-based) optimization method to derive waveforms that result in optimal driving properties of the walking piezo motor. A model of the stage and motor is developed incorporating the switching behavior of the drive legs, the contact deformation, and stick-slip effects between the legs and the stage. The friction-based driving principle of the motor is modeled using a set-valued friction model, resulting in a model in terms of differential-algebraic inclusions. For this model, we developed a dedicated numerical time-stepping solver. Experiments show a good model accuracy in both the drive direction and the perpendicular direction. The validated model is used in an optimization, resulting in waveforms with optimal driving properties of the stage at constant velocity. Besides the model-based optimization, also a direct experimental data-based waveform optimization is performed. Experiments with the optimized waveforms show that compared to existing sinusoidal and asymmetric waveforms in literature the driving properties can be significantly improved by the model-based waveforms and even further by the data-based waveforms.

Modeling and Waveform Optimization of a Nano-motion Piezo Stage

Atomic Force Microscopes (AFMs) are widely used for the investigation of samples at the nanometer scale. The metrological AFM used in this work uses a 3 degrees-of-freedom (DOFs) stage, driven by piezo-stack actuators, for sample manipulation in combination with a fixed cantilever. The piezostack actuators suffer from hysteresis, which acts as a nonlinear disturbance on the system and/or can change the system dynamics. The contributions of this paper are the application of feedback control to all 3 DOFs of the metrological AFM and the design and application of a hysteresis feedforward for the asymmetric hysteresis present in the system. The amount of coupling between the DOFs is assessed by a non-parametric multiple-input-multipleoutput (MIMO) identification. Since the dynamics appear to be decoupled in the frequency range of interest, feedback controllers are designed for each DOF separately. For the modeling of the asymmetric hysteresis an extended Coleman-Hodgdon model is proposed. This model is used for feedforward compensation of the hysteresis. The combination of feedback control for all DOFs and the asymmetric hysteresis feedforward enables the AFM to track scanning profiles within the sensor bound of 5nm. Real-time imaging of the sample is possible with an accuracy of 2nm.

http://www.mate.tue.nl/mate/pdfs/9726.pdf

Identification, control and hysteresis compensation of a 3 dof metrological afm

Continuously variable transmissions (CVTs) are applied in an increasing number of vehicles. Large ratio coverage allows for reduced engine speeds, which adds to both highway driving comfort and reduced fuel consumption. It becomes increasingly important to further improve the performance in terms of efficiency, robustness and torque capacity of the CVT. This paper describes the possibilities of improving the CVT by minimising variator clamping forces. This is accomplished by using slip control technology. This technique allows for the best possible transmission efficiency, combined with improved robustness for slip damage. This paper first describes the relation between variator slip and functional transmission properties. The conditions for optimum performance regarding efficiency and robustness are identified. This leads to the development of a variator slip controller. The remaining sections describe experimental results on two test rigs and in a production vehicle. The paper concludes with an outlook into further developments.

http://www.mate.tue.nl/mate/pdfs/5359.pdf

M Maarten Steinbuch

Papers

Device for creating a local plasma at the location of an object

Jerk derivative feedforward control for motion systems

Modeling and Waveform Optimization of a Nano-motion Piezo Stage

Identification, control and hysteresis compensation of a 3 dof metrological afm

Performance optimisation of the push-belt CVT by variator slip control