Nonlinear Model Predictive Control for Unmanned Aerial Vehicles†
AbstractThis 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. View Full-Text
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Ru, P.; Subbarao, K. Nonlinear Model Predictive Control for Unmanned Aerial Vehicles. Aerospace 2017, 4, 31.
Ru P, Subbarao K. Nonlinear Model Predictive Control for Unmanned Aerial Vehicles. Aerospace. 2017; 4(2):31.Chicago/Turabian Style
Ru, Pengkai; Subbarao, Kamesh. 2017. "Nonlinear Model Predictive Control for Unmanned Aerial Vehicles." Aerospace 4, no. 2: 31.
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