基于自抗扰控制的机器人定位策略

机器人定位即需根据传感器测量对自身位置进行估计. 由于机器人系统模型的复杂非线性, 工况环境中的不确定干扰, 定位结果不可避免地会受到系统内外扰动的影响. 现有的定位算法往往仅能依赖模型或传感配置以及算法自身的鲁棒性被动抗扰, 这使得定位系统的抗扰能力有限、应用场景受限. 本文基于自抗扰控制思想提出一种 能够主动补偿系统内外扰动的机器人定位策略. 该策略将系统中所有能够影响最终定位结果的不确定因素统一视为总扰动, 并设计扩张状态观测器实现对总扰动的观测, 在此基础上构建控制器补偿总扰动影响, 以使定位结果更加准确. 与传统的定位抗扰策略相比, 本文所提出的抗扰定位策略并不依赖于模型或特定的传感配置, 能够处理任意有界的扰动类型, 理论上能够成为定位抗干扰的终极解决路径. 最后, 基于李雅普诺夫理论证明了系统的稳定性. 仿真和实车实验验证了本文提出的基于自抗扰控制的机器人定位策略能够有效地观测系统总扰动, 并补偿扰动影响, 提高定位结果的准确度.

Robot localization based on active disturbance rejection controller

Robot localization refers to the estimation of its own position based on sensor measurements. Due to the complexity of nonlinear robot systems and the disturbances in the working environment, the localization results are inevitably affected by the internal and external disturbances. Existing localization algorithms adopt a passive disturbance-rejection strategy, relying on the robustness of the algorithm itself to cope with uncertainty, and often depend on models or specific sensing configurations, which leads to limited disturbance-rejection capability and restricted application scenarios. In this paper, we propose a novel robot localization framework that can actively cope with both internal and external uncertainties of the systems. All uncertainties that affect the localization results are uniformly considered as a total disturbance. The extended state observer is designed to estimate the total disturbance, and the controller is applied to compensate the total disturbance and to improve the accuracy of localization. Compared with the traditional localization frameworks, the proposed approach is independent of specific system configurations, and can also handle arbitrary bounded disturbances, resulting in more universal, robust and effective disturbance-rejection localization strategies. Finally, the stability of the system is proved based on the Lyapunov theory. The results of simulations and a real platform-based experiment verify the effectiveness and satisfactory performance of the proposed localization framework.