Purpose

When the mobile manipulator is traveling on an unconstructed terrain, the external disturbance is generated. The load on the end of the mobile manipulator will be affected strictly by the disturbance. The purpose of this paper is to reject the disturbance and keep the end effector in a stable pose all the time, a control method is proposed for the onboard manipulator.

Design/methodology/approach

In this paper, the kinematics and dynamics models of the end pose stability control system for the tracked robot are built. Through the guidance of this model information, the control framework based on active disturbance rejection control (ADRC) is designed, which keeps the attitude of the end of the manipulator stable in the pitch, roll and yaw direction. Meanwhile, the control algorithm is operated with cloud computing because the research object, the rescue robot, aims to be lightweight and execute work with remote manipulation.

Findings

The challenging simulation experiments demonstrate that the methodology can achieve valid stability control performance in the challenging terrain road in terms of robustness and real-time.

Originality/value

This research facilitates the stable posture control of the end-effector of the mobile manipulator and maintains it in a suitable stable operating environment. The entire system can normally work even in dynamic disturbance scenarios and uncertain nonlinear modeling. Furthermore, an example is given to guide the parameter tuning of ADRC by using model information and estimate the unknown internal modeling uncertainty, which is difficult to be modeled or identified.

中文翻译：

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基于自抗扰控制的不平坦地形移动机械臂末端效应稳定性控制

目的

当移动机械手在未建成的地形上行驶时，会产生外部干扰。移动机械手末端的负载将受到扰动的严格影响。本文的目的是为了抑制干扰并使末端执行器始终处于稳定的姿态，提出了一种车载机械手的控制方法。

设计/方法/方法

本文建立了履带式机器人末端姿态稳定控制系统的运动学和动力学模型。通过该模型信息的指导，设计了基于自抗扰控制（ADRC）的控制框架，使机械手末端在俯仰、滚转和偏航方向上的姿态保持稳定。同时，由于研究对象救援机器人的目标是轻量级并通过远程操作执行工作，因此控制算法通过云计算进行操作。

发现

具有挑战性的仿真实验表明，该方法可以在具有挑战性的地形道路中在鲁棒性和实时性方面实现有效的稳定性控制性能。

原创性/价值

该研究有利于移动机械手末端执行器的稳定姿态控制，并使其保持在合适的稳定操作环境中。整个系统即使在动态扰动场景和不确定的非线性建模中也能正常工作。此外，给出了一个例子，利用模型信息指导ADRC的参数调整，估计难以建模或识别的未知内部建模不确定性。