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
收藏
页码：61 / 65
页数：5
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