Meta-Learning-Based Robust Adaptive Flight Control Under Uncertain Wind Conditions
California Institute of Technology（加州理工学院）
Michael O'Connell, Guanya Shi, Xichen Shi, Soon-Jo Chung
备注：
7 pages, 7 figures; this article is an early draft and presents preliminary results; the full method and improved results were published in Science Robotics on May 4th, 2022: doi.org/10.1126/scirobotics.abm6597; arXiv: doi.org/10.48550/arXiv.2205.06908
Realtime model learning proves challenging for complex dynamical systems, such as drones flying in variable wind conditions. Machine learning technique such as deep neural networks have high representation power but is often too slow to update onboard. On the other hand, adaptive control relies on simple linear parameter models can update as fast as the feedback control loop. We propose an online composite adaptation method that treats outputs from a deep neural network as a set of basis functions capable of representing different wind conditions. To help with training, meta-learning techniques are used to optimize the network output useful for adaptation. We validate our approach by flying a drone in an open air wind tunnel under varying wind conditions and along challenging trajectories. We compare the result with other adaptive controller with different basis function sets and show improvement over tracking and prediction errors.
Differentiable Physics-Informed Adaptive Koopman Control for Stable Flight under Unknown Disturbances
Research Center for Intelligent Robotics, School of Astronautics, Northwestern Polytechnical University（西北工业大学航天学院智能机器人研究中心）；Department of Automation, Tsinghua University（清华大学自动化系）
Design and Aerodynamic Modeling of MetaMorpher: A Hybrid Rotary andFixed-Wing Morphing UAV
University of Zagreb, Faculty of Electrical Engineering and Computing, LARICS Laboratory for Robotics and Intelligent Control Systems（萨格勒布大学电气工程与计算学院LARICS机器人智能控制系统实验室）
College of Automation, Harbin Engineering University（哈尔滨工程大学自动化学院）；Department of Mechanical Engineering, University of Hong Kong（香港大学机械工程系）；College of Mechatronics and Control Engineering, Shenzhen University（深圳大学机电与控制工程学院）
Motion-Specific Battery Health Assessment for Quadrotors Using High-Fidelity Battery Models
Department of Convergence IT Engineering, Pohang University of Science and Technology（convergence IT工程系，釜山科学技术大学）；Department of Electrical Engineering, Pohang University of Science and Technology（电气工程系，釜山科学技术大学）
Learning Hybrid Policies for MPC with Application to Drone Flight in Unknown Dynamic Environments
National Key Lab of Autonomous Intelligent Unmanned Systems, Beijing Institute of Technology（北京理工大学自主智能无人系统全国重点实验室）；Beijing Institute of Technology Chongqing Innovation Center（北京理工大学重庆创新中心）；Department of Control Science and Engineering, Tongji University（同济大学控制科学与工程系）
Aerobatic maneuvers in insect-scale flapping-wing aerial robots via deep-learned robust tube model predictive control
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology（电气工程与计算机科学系，麻省理工学院）；Department of Aeronautics and Astronautics, Massachusetts Institute of Technology（航空宇航系，麻省理工学院）
DikpolaSat Mission: Improvement of Space Flight Performance and Optimal Control Using Trained Deep Neural Network -- Trajectory Controller for Space Objects Collision Avoidance
Tod’Aérs - Transparent Organization for Aeronautics and Space Research（Tod'Aérs - 航空航天研究透明组织）；Space Generation Advisory Council（太空世代咨询委员会）；Moscow Aviation Institute（莫斯科航空学院）；Univerité Sultan Moulay Slimane（苏丹穆莱·斯利曼大学）
