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Informationen zum Autor John Valasek is Professor of aerospace engineering and Director of the Vehicle Systems & Control Laboratory at Texas A&M University. He has industrial experience with the Northrop Corporation! Aircraft Division and research experience with the Air Force Research Laboratory and NASA. He is the author or editor of numerous publications including Progress in Astronautics and Aeronautics Volume 240! Morphing Aerospace Vehicles and Structures (ISBN 978-1-60086-903-7). He holds a PhD and MS in aerospace engineering from the University of Kansas! and a BS in aerospace engineering from California State Polytechnic University! Pomona. He is an Associate Editor of the Journal of Guidance! Control! and Dynamics and is an AIAA Associate Fellow. Klappentext Research advances in embedded computational intelligence, communication, control, and new mechanisms for sensing, actuation, and adaptation hold the promise to transform aerospace. The result will be air and space vehicles, propulsion systems, exploration systems, and vehicle management systems that respond more quickly, provide large-scale distributed coordination, work in dangerous or inaccessible environments, and augment human capabilities. Advances in Intelligent and Autonomous Aerospace Systems seeks to provide both the aerospace researcher and the practicing aerospace engineer with an exposition on the latest innovative methods and approaches that focus on intelligent and autonomous aerospace systems. The chapters are written by leading researchers in this field, and include ideas, directions, and recent results on intelligent aerospace research issues with a focus on dynamics and control, systems engineering, and aerospace design. The content on uncertainties, modeling of large and highly non-linear complex systems, robustness, and adaptivity is intended to be useful in both the sub-system and the overall system level design and analysis of various aerospace vehicles.A broad spectrum of methods and approaches are presented, including: * Bio-Inspiration * Fuzzy Logic * Genetic Algorithms * Q-Learning * Markov Decision Processes * Approximate Dynamic Programming * Artificial Neural Networks * Probabilistic Maps * Multi-Agent Systems * Kalman, particle, and confidence filtering Inhaltsverzeichnis INTELLIGENT FLIGHT CONTROL Towards Bio-inspired Robotic Aircraft: CPG-based Control of Flapping and Gliding Flight Neural Network-based Optimal Control of an Unmanned Helicopter Intelligent Constrained Optimal Control of Aerospace Vehicles with Model Uncertainties Modified Reference Model MRAC (M-MRAC): An Application to a Generic Transport Aircraft L1 Adaptive Control in Flight INTELLIGENT PROPULSION AND HEALTH MANAGEMENT Integrated Systems Health Management for Intelligent Systems Intelligent Propulsion Control and Health Management Genetic Fuzzy Controller for a Gas Turbine Fuel System INTELLIGENT PLANNING AND MULTI-AGENT SYSTEMS Multi-Resolution State-Space Discretization Method for Q-Learning Motion Planning Under Uncertainty Protocol Utilization in Intelligent Systems to Facilitate Exploration Missions A Hybrid Framework for User-Guided Prioritized Search and Adaptive Target Tracking via Cooperative UAVs ...
