Near-optimal Trajectory Tracking in Quadcopters using Reinforcement Learning

被引：0

作者：

Engelhardt, Randal ^{[1
]}

Velazquez, Alberto ^{[2
]}

Sardarmehni, Tohid ^{[1
]}

机构：

[1] Calif State Univ Northridge, Mech Engn, Northridge, CA 91330 USA

[2] Univ Texas Rio Grande Valley, Mech Engn, Edinburg, TX 78539 USA

来源：

IFAC PAPERSONLINE | 2024年 / 58卷 / 28期

关键词：

Optimal Control; Reinforcement Learning; Quadcopter; MODEL-PREDICTIVE CONTROL; QUADROTOR;

D O I：

10.1016/j.ifacol.2024.12.011

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：61 / 65

页数：5

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