Zobrazeno 1 - 10
of 123
pro vyhledávání: '"Helwa, Mohamed"'
In this paper, we propose an online learning approach that enables the inverse dynamics model learned for a source robot to be transferred to a target robot (e.g., from one quadrotor to another quadrotor with different mass or aerodynamic properties)
Externí odkaz:
http://arxiv.org/abs/1904.00249
In this paper, we study the control design of an automatic crosswind stabilization system for a novel, buoyantly-assisted aerial transportation vehicle. This vehicle has several advantages over other aircraft including the ability to take-off and lan
Externí odkaz:
http://arxiv.org/abs/1810.00046
Lagrangian systems represent a wide range of robotic systems, including manipulators, wheeled and legged robots, and quadrotors. Inverse dynamics control and feedforward linearization techniques are typically used to convert the complex nonlinear dyn
Externí odkaz:
http://arxiv.org/abs/1804.01031
This paper presents a learning-based approach for impromptu trajectory tracking for non-minimum phase systems, i.e., systems with unstable inverse dynamics. Inversion-based feedforward approaches are commonly used for improving tracking performance;
Externí odkaz:
http://arxiv.org/abs/1709.04407
Publikováno v:
Pereida, Karime, Mohamed K. Helwa, and Angela P. Schoellig. "Data-Efficient Multirobot, Multitask Transfer Learning for Trajectory Tracking." IEEE Robotics and Automation Letters 3.2 (2018): 1260-1267
Transfer learning has the potential to reduce the burden of data collection and to decrease the unavoidable risks of the training phase. In this letter, we introduce a multirobot, multitask transfer learning framework that allows a system to complete
Externí odkaz:
http://arxiv.org/abs/1709.04543
Multi-robot transfer learning allows a robot to use data generated by a second, similar robot to improve its own behavior. The potential advantages are reducing the time of training and the unavoidable risks that exist during the training phase. Tran
Externí odkaz:
http://arxiv.org/abs/1707.08689
This paper introduces deep neural networks (DNNs) as add-on blocks to baseline feedback control systems to enhance tracking performance of arbitrary desired trajectories. The DNNs are trained to adapt the reference signals to the feedback control loo
Externí odkaz:
http://arxiv.org/abs/1705.10932
Autor:
Helwa, Mohamed K., Caines, Peter E.
We consider affine systems defined on polytopes and study the cases where the systems are not in-block controllable with respect to the given polytopes. That are the cases in which we cannot fully control the affine systems within the interior of a g
Externí odkaz:
http://arxiv.org/abs/1610.09703
Autor:
Li, Qiyang, Qian, Jingxing, Zhu, Zining, Bao, Xuchan, Helwa, Mohamed K., Schoellig, Angela P.
Trajectory tracking control for quadrotors is important for applications ranging from surveying and inspection, to film making. However, designing and tuning classical controllers, such as proportional-integral-derivative (PID) controllers, to achiev
Externí odkaz:
http://arxiv.org/abs/1610.06283
This paper studies the problem of constructing in-block controllable (IBC) regions for affine systems. That is, we are concerned with constructing regions in the state space of affine systems such that all the states in the interior of the region are
Externí odkaz:
http://arxiv.org/abs/1610.01243