Model-Free Control for Continuum Robots Based on an Adaptive Kalman Filter
Model-Free Control for Continuum Robots Based on an Adaptive Kalman Filter
复制标题
基于自适应卡尔曼滤波器的连续机器人无模型控制
DOI：
10.1109/tmech.2017.2775663
复制
发表时间：
2018-02-01
影响因子：
6.4
通讯作者：
Dai, Jian S.
中科院分区：
文献类型：
--
作者：
Li, Minhan;Kang, Rongjie;Dai, Jian S.
具有结构顺应性的连续体机器人在非结构化环境中具有很好的潜力。然而，由于机器人及其运动学模型中存在相当大的不确定性，这种结构顺应性给控制器的设计带来了挑战。通常，需要大量的传感器来向控制器提供机器人的状态变量，包括每个执行器的长度和机器人尖端的位置。本文提出了一种基于自适应卡尔曼滤波的无模型路径跟踪方法，实现了仅利用压力和末端位置的连续介质机器人的路径跟踪。由于卡尔曼滤波在每个控制区间只需两步代数运算，保证了低运算量和实时控制能力。通过在控制律中增加一个最优向量，也可以避免机器人的屈曲。通过仿真分析和实验验证，该控制方法对系统不确定性和外部扰动具有良好的鲁棒性，减少了传感器的数量。
Continuum robots with structural compliance have promising potential to operate in unstructured environments. However, this structural compliance brings challenges to the controller design due to the existence of considerable uncertainties in the robot and its kinematic model. Typically, a large number of sensors are required to provide the controller the state variables of the robot, including the length of each actuator and position of the robot tip. In this paper, a model-free method based on an adaptive Kalman filter is developed to accomplish path tracking for a continuum robot using only pressures and tip position. As the Kalman filter operates only with a two-step algebraic calculation in every control interval, the low computational load and real-time control capability are guaranteed. By adding an optimal vector to the control law, buckling of the robot can also be avoided. Through simulation analysis and experimental validation, this control method shows good robustness against the system uncertainty and external disturbance, and lowers the number of sensors.