UAV Target Tracking using Nonlinear Model Predictive Control

Publication Type:
Conference Proceeding
2022 International Conference on Electrical, Computer and Energy Technologies (ICECET), 2022, 00, pp. 1-7
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This paper presents a Nonlinear Model Predictive Control (NMPC) formulation for the attitude control of a fixed-wing Unmanned Aerial Vehicle (UAV) tracking a ground target. The vehicle is required to orbit around the target and as such, the tracking system can be modeled in two dimensions, namely the range and bearing angle. The system constraints are considered to account for real-world limitations. Subject to these constraints, the optimal input is obtained from solving a quadratic cost function. Extensive simulation was conducted for several case studies with various trajectories of the target, given position measurements of the UAV. The control development is then applied to track an estimated path taken from a mining truck during operation. The proposed control formulation is compared with a standard linear Model Predictive Control (MPC). The numerical results show that NMPC can cope with both constraints and nonlinearities, resulting in highly accurate tracking even when the UAV initial position is far away from the target, and overcoming poor tracking performance when using linear MPC. Using the current hardware standards, a quantitative analysis is also provided based on the required execution time for solving the constrained quadratic optimization problem at each sampling instant.
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