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Robust control has been a topic of active research in the last three decades culminating in H_2/H_infty and mu design methods followed by research on parametric robustness, initially motivated by Kharitonov's theorem, the extension to non-linear time delay systems, and other more recent methods. The two volumes of Recent Advances in Robust Control give a selective overview of recent theoretical developments and present selected application examples. The volumes comprise 39 contributions covering various theoretical aspects as well as different application areas. The first volume covers selected problems in the theory of robust control and its application to robotic and electromechanical systems. The second volume is dedicated to special topics in robust control and problem specific solutions. Recent Advances in Robust Control will be a valuable reference for those interested in the recent theoretical advances and for researchers working in the broad field of robotics and mechatronics.

Robust control. --- Robustness (Control systems) --- Automatic control --- Automatic control engineering

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The main objective of this book is to present important challenges and paradigms in the field of applied robust control design and implementation. Book contains a broad range of well worked out, recent application studies which include but are not limited to H-infinity, sliding mode, robust PID and fault tolerant based control systems. The contributions enrich the current state of the art, and encourage new applications of robust control techniques in various engineering and non-engineering systems.

Robust control. --- Robustness (Control systems) --- Automatic control --- Automatic control engineering

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Embodied and evolving systems — biological or robotic — are interacting networks of structure, function, information, and behavior. Understanding these complex systems is the goal of the research presented in this book. We address different questions and hypotheses about four essential topics in complex systems: evolvability, environments, embodiment, and emergence. Using a variety of approaches, we provide different perspectives on an overarching, unifying question: How can embodied and evolutionary robotics illuminate (1) principles underlying biological evolving systems and (2) general analytical frameworks for studying embodied evolving systems? The answer — model biological processes to operate, develop, and evolve situated, embodied robots.

Cognitive Robotics --- environments --- emergence --- robustness --- embodiment --- Evolvability --- Evolutionary Robotics

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A multi-modal neural network exploits information from different channels and in different terms (e.g., images, text, sounds, sensor measures) in the hope that the information carried by each mode is complementary, in order to improve the predictions the neural network. Nevertheless, in realistic situations, varying levels of perturbations can occur on the data of the modes, which may decrease the quality of the inference process. An additional difficulty is that these perturbations vary between the modes and on a per-sample basis. This work presents a solution to this problem. The three main contributions are described below. 
First, a novel attention module is designed, analysed and implemented. This attention module is constructed to help multi-modal networks handle modes with perturbations.

Secondly, two new regularizers are developed to improve the generalization of the robustness gain on more intensive failing modes (relative to the training set).

Lastly, a unified multi-modal attention module is presented, combining the main types of attention mechanisms in the deep learning literature with our module. We suggest that this unified module could be coupled with a prediction model to enable the latter face unexpected situations, and improve the extraction of the relevant information in the data.

Multimodal, Deep Learning, Attention, Robustness --- Ingénierie, informatique & technologie > Sciences informatiques

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Embodied and evolving systems — biological or robotic — are interacting networks of structure, function, information, and behavior. Understanding these complex systems is the goal of the research presented in this book. We address different questions and hypotheses about four essential topics in complex systems: evolvability, environments, embodiment, and emergence. Using a variety of approaches, we provide different perspectives on an overarching, unifying question: How can embodied and evolutionary robotics illuminate (1) principles underlying biological evolving systems and (2) general analytical frameworks for studying embodied evolving systems? The answer — model biological processes to operate, develop, and evolve situated, embodied robots.

Cognitive Robotics --- environments --- emergence --- robustness --- embodiment --- Evolvability --- Evolutionary Robotics --- Cognitive Robotics --- environments --- emergence --- robustness --- embodiment --- Evolvability --- Evolutionary Robotics