Control of Aerial Robots Using Convex QP LMPC and Learning-Based Explicit-MPC

Abstract

This article presents a novel method for quadrotor trajectory control utilizing cascade control and model predictive control (MPC). The proposed approach divides the control problem into a linear position controller, employing linear MPC with convex quadratic programming, and a nonlinear attitude controller, utilizing deep neural network-based MPC. Addressing the computational load challenges associated with online control, the hardware-in-the-loop (HIL) controller is tested to demonstrate its effectiveness in ensuring fast processing and suitability for online control. The stability of the proposed control strategies is analyzed, and simulation results using the HIL system validate the accurate tracking of desired trajectories. The findings highlight the functionality, reliability, and potential of the proposed approach for real-time applications in quadrotor control.