Journal ArticleDOI
Output Feedback Control of a Quadrotor UAV Using Neural Networks
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TLDR
It is shown using Lyapunov theory that the position, orientation, and velocity tracking errors, the virtual control and observer estimation errors, and the NN weight estimation errors for each NN are all semiglobally uniformly ultimately bounded (SGUUB) in the presence of bounded disturbances and NN functional reconstruction errors while simultaneously relaxing the separation principle.Abstract:
In this paper, a new nonlinear controller for a quadrotor unmanned aerial vehicle (UAV) is proposed using neural networks (NNs) and output feedback. The assumption on the availability of UAV dynamics is not always practical, especially in an outdoor environment. Therefore, in this work, an NN is introduced to learn the complete dynamics of the UAV online, including uncertain nonlinear terms like aerodynamic friction and blade flapping. Although a quadrotor UAV is underactuated, a novel NN virtual control input scheme is proposed which allows all six degrees of freedom (DOF) of the UAV to be controlled using only four control inputs. Furthermore, an NN observer is introduced to estimate the translational and angular velocities of the UAV, and an output feedback control law is developed in which only the position and the attitude of the UAV are considered measurable. It is shown using Lyapunov theory that the position, orientation, and velocity tracking errors, the virtual control and observer estimation errors, and the NN weight estimation errors for each NN are all semiglobally uniformly ultimately bounded (SGUUB) in the presence of bounded disturbances and NN functional reconstruction errors while simultaneously relaxing the separation principle. The effectiveness of proposed output feedback control scheme is then demonstrated in the presence of unknown nonlinear dynamics and disturbances, and simulation results are included to demonstrate the theoretical conjecture.read more
Citations
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Journal ArticleDOI
Survey of advances in guidance, navigation, and control of unmanned rotorcraft systems
TL;DR: The main objective of this paper is to present a comprehensive survey of RUAS research that captures all seminal works and milestones in each GNC area, with a particular focus on practical methods and technologies that have been demonstrated in flight tests.
Journal ArticleDOI
Energy-Efficient UAV Control for Effective and Fair Communication Coverage: A Deep Reinforcement Learning Approach
TL;DR: The proposed DRL-EC3 maximizes a novel energy efficiency function with joint consideration for communications coverage, fairness, energy consumption and connectivity, and makes decisions under the guidance of two powerful deep neural networks.
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Reinforcement Learning for UAV Attitude Control
TL;DR: This work developed an open source high-fidelity simulation environment to train a flight controller attitude control of a quadrotor through RL, and used this environment to compare their performance to that of a PID controller to identify if using RL is appropriate in high-precision, time-critical flight control.
Proceedings ArticleDOI
Autonomous landing of a VTOL UAV on a moving platform using image-based visual servoing
Daewon Lee,Tyler Ryan,H. Jin Kim +2 more
TL;DR: An image-based visual servoing algorithm to control a vertical-takeoff-and-landing unmanned aerial vehicle while tracking and landing on a moving platform and generates a velocity reference command used as the input to an adaptive sliding mode controller is described.
Journal ArticleDOI
Autonomy in materials research: a case study in carbon nanotube growth
Pavel Nikolaev,Daylond Hooper,Frederick Webber,Rahul Rao,Kevin Decker,Michael Krein,Jason Poleski,Rick Barto,Benji Maruyama +8 more
TL;DR: An Autonomous Research System (ARES), a robot guided by artificial intelligence in an iterative learning loop that is capable of designing, executing and analyzing its own experiments orders of magnitude faster than current research methods, is built.
References
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Quadrotor Helicopter Flight Dynamics and Control: Theory and Experiment
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Journal ArticleDOI
Stochastic choice of basis functions in adaptive function approximation and the functional-link net
B. Igelnik,Yoh-Han Pao +1 more
TL;DR: A theoretical justification for the random vector version of the functional-link (RVFL) net is presented, based on a general approach to adaptive function approximation, which results are that the RVFL is a universal approximator for continuous functions on bounded finite dimensional sets.