-
Dr Guillermo Ortega (ESA)24/11/2017, 09:00Introduction to the 1st International Round TableIntroduction of the workshop, objectives, composition, structure, speakers. Keynote speech about the current ESA understanding of the state of the art and the needs for a more intelligent control in the upcoming complex space missions.Go to contribution page
-
Dr Johann Bals (DLR)24/11/2017, 09:25Intelligent Control for TransportationModern model-based control methods such as model predictive control (MPC), inverse model feedforward control, and nonlinear dynamic inversion (NDI) encapsulate mathematical model representations of the system to be controlled into the feedforward or feedback control algorithm implementation. Depending on the accuracy and the validity range of the embedded model, these methods can achieve good...Go to contribution page
-
Dr Nicola Policella (Solenix Deutschland GmbH), Dr Simone Fratini (Solenix Deutschland GmbH)24/11/2017, 09:50Intelligent Control for SpaceArtificial Intelligence has improved the cost-effectiveness and flexibility of mission planning tools design and development. This talk provides an overview of significant planning and scheduling experiences in deploying Intelligent tools to innovate the ESA mission planning practice. Specifically, the focus is in some key points that an approach based on Artificial Intelligence brings to...Go to contribution page
-
Prof. María Victoria Lapuerta González (Technical University of Madrid)24/11/2017, 10:15Fuzzy Logic ControlIn this talk we show that the use of intelligent control systems based on fuzzy logic is a great advantage over conventional control systems currently being used in satellite attitude control, and open new possibilities of application of intelligent controllers in the field of space technologies. In order to develop and introduce intelligent systems in the space field, we have designed an...Go to contribution page
-
Keisuke Sugawara (Japan Aerospace Exploration Agency)24/11/2017, 10:40This talk is dedicated to the efforts of JAXA in the field of artificial intelligence. And in particular about Intelligent Control using Deep learning as well as Reinforcement Learning and Machine Learning. The talk contains also the work done by JAXA on the areas of Onboard identification of target using Deep Learning and Machine Learning as well as in the field of Safety of Intelligent...Go to contribution page
-
Prof. Joerg Fliege (University of Southampton)24/11/2017, 11:20Optimisation is a key technology for intelligent control, as exemplified by evolutionary and genetic algorithms, machine learning techniques, and training of neural networks. Deterministic global optimisation is concerned with rigorous methods to find globally optimal solutions for such applications as well as mathematically precise error bounds for such solutions. Corresponding solver...Go to contribution page
-
Georg Nührenberg (fortiss GmbH)24/11/2017, 11:45Neural Networks Control SystemsThe deployment of Artificial Neural Networks (ANNs) in critical missions poses a number of new verification challenges. In particular, for ANN-enabled perception and control it is important to establish properties about the resilience of ANNs to noisy or even maliciously manipulated sensory input. Furthermore, given interpretability of the inputs and outputs of an ANN, certain safety...Go to contribution page
-
Dr Juan Félix San-Juan (Scientific Computing Group (GRUCACI), University of La Rioja)24/11/2017, 12:10Genetic and Evolutionary ComputationArtificial Intelligence in general, and machine learning in particular, are the cornerstones of the so-called Industry 4.0. Being aware of the new opportunities provided by current technology and increased computational power, public administrations are fostering this upcoming fourth industrial revolution, in which both the private sector and academia are also taking an active part. The space...Go to contribution page
-
Prof. Daniel Hennes (University of Stuttgart)24/11/2017, 12:35Intelligent Control for TransportationDeep learning techniques allow us to scale reinforcement learning to problems that were previously intractable, i.e. to domains with high-dimensional state (or observation) spaces and continuous action spaces. We will give an overview of state-of-the-art deep reinforcement learning methods, including deep Q-learning, deep deterministic policy gradients, and asynchronous...Go to contribution page
-
Dr Annalisa Riccardi (Strathclyde University)24/11/2017, 14:00Intelligent Control for ManufacturingThe Intelligent Computational Engineering Laboratory (ICE Lab) in the Department of Mechanical and Aerospace Engineering at the University of Strathclyde acts as the bridge between novel computational intelligence techniques and real-world engineering applications. The aim is two-fold: to learn from real-life problems and develop new solutions as well as to apply existing numerical techniques...Go to contribution page
-
Dr Guido de Croon (TU Delft), Dr Javier Alonso-Mora (Delft University of Technology)24/11/2017, 14:25Personal Assistants using Artificial IntelligenceBoth in space and on earth there is a strong drive to make smaller autonomous robots. Smaller implies lower production costs and the possibility to scale up to large multi-robot systems. Specifically for space exploration, this might lead to a reduction in launching costs. A challenge is to make such robots still perform on a level that is comparable to larger robots, under strict restrictions...Go to contribution page
-
Mr Vesa Klumpp (Knowtion UG)24/11/2017, 14:50Intelligent Control for SpaceCurrently, intelligent algorithms for optimization and control are employed in many so called Industrie 4.0/Internet of Things projects. These include on one hand the optimization of production processes by implementing intelligent control algorithms into production control or Manufacturing Execution Systems, and on the other hand the improvement of products by equipping them with intelligent...Go to contribution page
-
Dr Steve Chien (Jet Propulsion Laboratory, California Institute of Technology)24/11/2017, 15:35NASA JPL has been employing Artificial Intelligence in support of space exploration. These efforts have been ongoing for many years: NASA’s Earth Observing One mission was controlled by AI software – the Autonomous Sciencecraft and Earth Observing Sensorweb – for more than a dozen years, linked with other satellites and scores of ground assets to track volcanism, floods, wildfires and the...Go to contribution page
Choose timezone
Your profile timezone: