An Efficient Egocentric Regulator for Continuous Targeting Problems of the Underactuated Quadrotor

Currently, for the continuous targeting problem with the underactuated quadrotor, a nonlinear optimal control method with high computational efficiency is still missing. To tackle this problem, a novel efficient egocentric regulation approach with high computational efficiency is proposed in this article. Specifically, it directly formulates the optimal control problem in an egocentric manner regarding the quadrotor's body coordinates. Meanwhile, the nonlinearities of the system are decoupled through a mapping of the feedback states and control inputs, between the inertial and body coordinates. In this way, it only requires solving a quadratic performance objective with linear constraints and then generates control inputs analytically. Simulations and mimic biological experiments are carried out to verify the effectiveness and computational efficiency. Results demonstrate that the proposed control approach presents the highest and most stable computational efficiency compared with 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 optimizers, establishing a control frequency around 3000 Hz.