Trajectory Tracking for UAVs: An Interpolating Control Approach

TL;DR

This paper evaluates an interpolating control method for UAV trajectory tracking, demonstrating it offers comparable control quality to MPC but with lower computational demands, suitable for resource-limited platforms.

Contribution

It introduces a modified extended interpolating control (eIC) method and compares its real-time performance to MPC through simulations and lab tests.

Findings

eIC achieves similar control accuracy as MPC

eIC significantly reduces computational load

eIC is effective for resource-constrained UAVs

Abstract

Building on our previous work, this paper investigates the effectiveness of interpolating control (IC) for real-time trajectory tracking. Unlike prior studies that focused on trajectory tracking itself or UAV stabilization control in simulation, we evaluate the performance of a modified extended IC (eIC) controller compared to Model Predictive Control (MPC) through both simulated and laboratory experiments with a remotely controlled UAV. The evaluation focuses on the computational efficiency and control quality of real-time UAV trajectory tracking compared to previous IC applications. The results demonstrate that the eIC controller achieves competitive performance compared to MPC while significantly reducing computational complexity, making it a promising alternative for resource-constrained platforms.