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Trajectory Tracking for UAVs: An Interpolating Control Approach

Zdeněk Bouček, Miroslav Flídr, Ondřej Straka

TL;DR

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.

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.

Trajectory Tracking for UAVs: An Interpolating Control Approach

TL;DR

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.

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.
Paper Structure (20 sections, 14 equations, 6 figures, 4 tables)

This paper contains 20 sections, 14 equations, 6 figures, 4 tables.

Table of Contents

  1. INTRODUCTION
  2. TRAJECTORY TRACKING FOR UAVS
  3. Model Predictive Control
  4. Interpolating Control Based Trajectory Tracking
  5. Invariant Sets
  6. State Decomposition
  7. Interpolating Control
  8. Extended Interpolating Control
  9. PLANAR UAV MODEL AND CONTROL DESIGN
  10. Planar UAV Model
  11. Model Parameters
  12. Controllers Design
  13. EXPERIMENTAL SETUP
  14. Simulations
  15. Laboratory Experiment with Crazyflie UAV
  16. ...and 5 more sections

Figures (6)

  • Figure 1: Planar UAV in the local frame
  • Figure 2: Laboratory Experiment with Crazyflie UAV
  • Figure 3: Path from tracking the high-frequency trajectory with the planar model
  • Figure 4: Path from tracking the high-frequency trajectory with Crazyflie UAV in laboratory
  • Figure 5: Path from 3D tracking the high-frequency trajectory with Crazyflie UAV in laboratory
  • ...and 1 more figures