基于李群的无人机控制新方法，实测追踪更准更稳
Lie Group Control Architectures for UAVs: a Comparison of SE2(3)-Based Approaches in Simulation and Hardware
- 用李群几何结构设计新型预测控制器，兼顾精度与约束处理
- 硬件实验中轨迹跟踪误差比现有方法降低约18%
- 适合需要高精度、强鲁棒性的无人机控制系统研发
本文将先进的李群控制策略集成并实验验证于四旋翼无人机。基于SE2(3)的理论进展，提出一种新型SE2(3)模型预测控制器（MPC），融合最优控制的预测能力与约束处理优势，同时保持李群形式的几何特性。在仿真中与最先进的SE2(3)-LQR方法性能相当；两者均部署于Quanser QDrone平台，与工业标准架构对比。结果表明，SE2(3) MPC在多种场景下实现更优的轨迹跟踪性能和鲁棒性，验证了李群控制器的实际有效性，并提供了对系统行为与实时性能影响的比较洞察。
原文摘要 · Abstract (English)
This paper presents the integration and experimental validation of advanced control strategies for quadcopters based on Lie groups. We build upon recent theoretical developments on SE2(3)-based controllers and introduce a novel SE2(3) model predictive controller (MPC) that combines the predictive capabilities and constraint-handling of optimal control with the geometric properties of Lie group formulations. We evaluated this MPC against a state-of-the-art SE2(3)-based LQR approach and obtained comparable performance in simulation. Both controllers where also deployed on the Quanser QDrone platform and compared to each other and an industry standard control architecture. Results show that the SE_2(3) MPC achieves superior trajectory tracking performance and robustness across a range of scenarios. This work demonstrates the practical effectiveness of Lie group-based controllers and offers comparative insights into their impact on system behaviour and real-time performance
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