NDLI: An approximate Dynamic Programming based controller for an underactuated 6DoF quadrotor

Content Provider	IEEE Xplore Digital Library
Author	Stingu, E. Lewis, F.L.
Copyright Year	2011
Description	Author affiliation: Automation & Robotics Research Institute, University of Texas at Arlington, USA (Stingu, E.; Lewis, F.L.)
Abstract	This paper discusses how the principles of Adaptive Dynamic Programming (ADP) can be applied to the control of a quadrotor helicopter platform flying in an uncontrolled environment and subjected to various disturbances and model uncertainties. ADP is based on reinforcement learning using an actor-critic structure. Due to the complexity of the quadrotor system, the learning process has to use as much information as possible about the system and the environment. Various methods to improve the learning speed and efficiency are presented. Neural networks with local activation functions are used as function approximators because the state-space can not be explored efficiently due to its size and the limited time available. The complex dynamics is controlled by a single critic and by multiple actors thus avoiding the curse of dimensionality. After a number of iterations, the overall actor-critic structure stores information (knowledge) about the system dynamics and the optimal controller that can accomplish the explicit or implicit goal specified in the cost function.
Starting Page	271
Ending Page	278
File Size	367735
Page Count	8
File Format	PDF
ISBN	9781424498871
e-ISBN	9781424498888
DOI	10.1109/ADPRL.2011.5967394
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-04-11
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Heuristic algorithms Neurons Rotors Artificial neural networks optimal control training system model Approximation methods neural network Equations reinforcement learning feedback actor critic Optimal control
Content Type	Text
Resource Type	Article

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