Abstract
In this paper, we present an integrated dynamic path planning and trajectory tracking control strategy for electric vertical take-off and landing (eVTOL) unmanned aerial vehicles (UAVs) by using a receding horizon optimization (RHO) approach. Considering that the effective sensing range of onboard sensors is practically short, we formulate the path planning into minimum curvature-based RHO problems with a polynomial path template. By adding the contraction constraint derived from the backstepping controller, the proposed nonlinear model predictive control (NMPC), as an online optimization scheme, guarantees the closed-loop stability. It also achieves the optimal control performance by minimizing the tracking error and power consumption. The experimental results of the dynamic path planning and trajectory tracking demonstrate the effectiveness of the proposed control algorithm.
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