Near-optimal Trajectory Tracking in Quadcopters using Reinforcement Learning

Authors

Randal Engelhardt
Alberto Velazquez, The University of Texas Rio Grande Valley
Tohid Sardarmehni, California State University, Northridge

Document Type

Article

Publication Date

2024

Abstract

The control of quadcopters poses significant challenges due to their complex dynamics characterized by highly nonlinear couplings, high system order, and under-actuation. This paper presents a novel control solution aimed at achieving near-optimal trajectory tracking for quadcopters. A near-optimal solution based on approximate dynamic programming is proposed to address the curse of dimensionality inherent in traditional dynamic programming, employing a single network adaptive critic. Extensive simulations validate the effectiveness and robustness of the proposed solution.

Comments

Student publication.

© 2024 The Authors. This is an open access article under the CC BY-NC-ND license

Recommended Citation

Engelhardt, Randal, Alberto Velazquez, and Tohid Sardarmehni. "Near-optimal Trajectory Tracking in Quadcopters using Reinforcement Learning." IFAC-PapersOnLine 58, no. 28 (2024): 61-65. https://doi.org/10.1016/j.ifacol.2024.12.011

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.

Publication Title

IFAC PapersOnLine

DOI

10.1016/j.ifacol.2024.12.011