为移动机械臂设计新型积分微分控制器,提升运动精度与抗扰能力。
Homogeneous Proportional-Integral-Derivative Controller in Mobile Robotic Manipulators
- 基于齐次控制理论,将传统PID增益扩展为状态相关非线性函数
- 实现全局渐近稳定与有限时间收敛,实验验证响应更快、误差更小
- 适合需要高精度协同控制的移动机器人系统,尤其在复杂环境中
移动机器人操作臂(MRMs)融合移动与操作功能,因其非线性动力学、欠驱动特性和基座与机械臂子系统间的耦合,控制难度大。本文提出一种专用于MRMs的新型齐次比例-积分-微分(hPID)控制策略,以实现鲁棒且协调的运动控制。与经典PID不同,hPID控制器利用齐次控制理论的数学框架,系统提升闭环系统的稳定性与收敛性,即使存在动态不确定性与外部干扰,也能以齐次方式处理。设计了齐次PID结构,通过分级齐次性方法改善跟踪误差的收敛性,将传统线性PID增益推广为非线性、状态依赖函数。采用基于李雅普诺夫的方法进行稳定性分析,证明在弱假设下hPID控制器可保证全局渐近稳定与有限时间收敛。在代表性MRM模型上的实验结果验证了其在基座与机械臂轨迹跟踪中实现高精度控制的有效性,相比传统线性PID,在响应时间、稳态误差和对模型不确定性的鲁棒性方面表现更优。该研究为下一代移动操作系统的自主性与可靠性提供了可扩展、分析严谨的控制框架,适用于结构化与非结构化环境。
原文摘要 · Abstract (English)
Mobile robotic manipulators (MRMs), which integrate mobility and manipulation capabilities, present significant control challenges due to their nonlinear dynamics, underactuation, and coupling between the base and manipulator subsystems. This paper proposes a novel homogeneous Proportional-Integral-Derivative (hPID) control strategy tailored for MRMs to achieve robust and coordinated motion control. Unlike classical PID controllers, the hPID controller leverages the mathematical framework of homogeneous control theory to systematically enhance the stability and convergence properties of the closed-loop system, even in the presence of dynamic uncertainties and external disturbances involved into a system in a homogeneous way. A homogeneous PID structure is designed, ensuring improved convergence of tracking errors through a graded homogeneity approach that generalizes traditional PID gains to nonlinear, state-dependent functions. Stability analysis is conducted using Lyapunov-based methods, demonstrating that the hPID controller guarantees global asymptotic stability and finite-time convergence under mild assumptions. Experimental results on a representative MRM model validate the effectiveness of the hPID controller in achieving high-precision trajectory tracking for both the mobile base and manipulator arm, outperforming conventional linear PID controllers in terms of response time, steady-state error, and robustness to model uncertainties. This research contributes a scalable and analytically grounded control framework for enhancing the autonomy and reliability of next-generation mobile manipulation systems in structured and unstructured environments.
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