An Efficient Egocentric Regulator for Continuous Targeting Problems of the Underactuated Quadrotor
arxiv(2021)
摘要
Flying robots such as the quadrotor could provide an efficient approach for
medical treatment or sensor placing of wild animals. In these applications,
continuously targeting the moving animal is a crucial requirement. Due to the
underactuated characteristics of the quadrotor and the coupled kinematics with
the animal, nonlinear optimal tracking approaches, other than smooth feedback
control, are required. However, with severe nonlinearities, it would be
time-consuming to evaluate control inputs, and real-time tracking may not be
achieved with generic optimizers onboard. To tackle this problem, a novel
efficient egocentric regulation approach with high computational efficiency is
proposed in this paper. Specifically, it directly formulates the optimal
tracking problem in an egocentric manner regarding the quadrotor's body
coordinates. Meanwhile, the nonlinearities of the system are peeled off through
a mapping of the feedback states as well as control inputs, between the
inertial and body coordinates. In this way, the proposed efficient egocentric
regulator only requires solving a quadratic performance objective with linear
constraints and then generate control inputs analytically. Comparative
simulations and mimic biological experiment are carried out to verify the
effectiveness and computational efficiency. Results demonstrate that the
proposed control approach presents the highest and stablest computational
efficiency than generic optimizers on different platforms. Particularly, on a
commonly utilized onboard computer, our method can compute the control action
in approximately 0.3 ms, which is on the order of 350 times faster than that of
generic nonlinear optimizers, establishing a control frequency around 3000 Hz.
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