Learning Dynamics for Improving Control of Overactuated Flying Systems
Open Access
- 1 October 2020
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Robotics and Automation Letters
- Vol. 5 (4) , 5283-5290
Abstract
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Funding Information
- NCCR Digital Fabrications, NCCR Robotics and Armasuisse
This publication has 26 references indexed in Scilit:
- Gaussian Process Model Predictive Control of an Unmanned QuadrotorJournal of Intelligent & Robotic Systems, 2017
- Towards robust online inverse dynamics learning2016
- RotorS—A Modular Gazebo MAV Simulator Framework2016
- A nonlinear force observer for quadrotors and application to physical interactive tasks2014
- Bayesian Nonparametric Adaptive Control Using Gaussian ProcessesIEEE Transactions on Neural Networks and Learning Systems, 2014
- Learning-based nonlinear model predictive control to improve vision-based mobile robot path-tracking in challenging outdoor environments2014
- Modeling and control of a quadrotor UAV with tilting propellers2012
- Model learning for robot control: a surveyCognitive Processing, 2011
- Adaptive Optimal Feedback Control with Learned Internal Dynamics Models2010
- Gaussian Processes and Reinforcement Learning for Identification and Control of an Autonomous BlimpProceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (cat. No.01ch37164), 2007