Nonlinear Model Predictive Control for Unmanned Aerial Vehicles†
Department of Mechanical & Aerospace Engineering, University of Texas at Arlington, Arlington, TX 76019,USA
*
Author to whom correspondence should be addressed.
†
This paper is an extended version of our paper published in the Proceedings of the AIAA Atmospheric Flight Mechanics Conference, AIAA Aviation Forum, Dallas, TX, USA, 22–25 June 2015; Kamesh Subbarao, Carlos Tule and Pengkai Ru, Nonlinear Model Predictive Control Applied to Trajectory Tracking for Unmanned Aerial Vehicles, No. AIAA 2015-2857.
‡
These authors contributed equally to this work.
Academic Editor: David Anderson
Aerospace 2017, 4(2), 31; https://doi.org/10.3390/aerospace4020031
Received: 26 April 2017 / Revised: 7 June 2017 / Accepted: 12 June 2017 / Published: 17 June 2017
(This article belongs to the Collection Unmanned Aerial Systems)
This paper discusses the derivation and implementation of a nonlinear model predictive control law for tracking reference trajectories and constrained control of a quadrotor platform. The approach uses the state-dependent coefficient form to capture the system nonlinearities into a pseudo-linear system matrix. The state-dependent coefficient form is derived following a rigorous analysis of aerial vehicle dynamics that systematically accounts for the peculiarities of such systems. The same state-dependent coefficient form is exploited for obtaining a nonlinear equivalent of the model predictive control. The nonlinear model predictive control law is derived by first transforming the continuous system into a sampled-data form and and then using a sequential quadratic programming solver while accounting for input, output and state constraints. The boundedness of the tracking errors using the sampled-data implementation is shown explicitly. The performance of the nonlinear controller is illustrated through representative simulations showing the tracking of several aggressive reference trajectories with and without disturbances.
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Keywords:
nonlinear; model predictive control; constraints; trajectory tracking; stability
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MDPI and ACS Style
Ru, P.; Subbarao, K. Nonlinear Model Predictive Control for Unmanned Aerial Vehicles. Aerospace 2017, 4, 31.
AMA Style
Ru P, Subbarao K. Nonlinear Model Predictive Control for Unmanned Aerial Vehicles. Aerospace. 2017; 4(2):31.
Chicago/Turabian StyleRu, Pengkai; Subbarao, Kamesh. 2017. "Nonlinear Model Predictive Control for Unmanned Aerial Vehicles" Aerospace 4, no. 2: 31.
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