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Pengkai Ru
These authors contributed equally to this work

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Journal article
Published: 17 June 2017 in Aerospace
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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.

ACS Style

Pengkai Ru; Kamesh Subbarao. Nonlinear Model Predictive Control for Unmanned Aerial Vehicles. Aerospace 2017, 4, 31 .

AMA Style

Pengkai Ru, Kamesh Subbarao. Nonlinear Model Predictive Control for Unmanned Aerial Vehicles. Aerospace. 2017; 4 (2):31.

Chicago/Turabian Style

Pengkai Ru; Kamesh Subbarao. 2017. "Nonlinear Model Predictive Control for Unmanned Aerial Vehicles." Aerospace 4, no. 2: 31.