基于重构容错的智能水下机器人定深运动控制

AUV's Depth Control Based on Reconstructive Fault-tolerant Control

为保证智能水下机器人(AUV)在部分运动执行器出现故障的情况下,仍可在一定深度下顺利完成相应任务,提出一种定深容错运动控制策略。该控制策略针对某型智能水下机器人垂向推进器的故障,从实用角度出发,基于重构容错控制思想,同时结合自抗扰控制(ADRC)方法进行具体的控制器设计和实现。该控制策略中包括两种定深控制器设计,分别为垂推正常工况下和垂推故障情况下的定深控制,试图依靠相关故障信息,通过重构替换实现容错控制。在仿真实验中,该控制策略于不同环境干扰下进行了相应测试,并与结合PID方法的定深控制器进行了比较。结果表明,基于重构容错控制思想,并结合自抗扰控制方法的定深容错控制策略不仅有效,同时具有更好的抑制干扰作用,从而可以为机器人提供更优的控制效果。

A depth fault-tolerant control strategy is proposedto make sure an autonomous underwater vehicle （AUV） can finish the expected tasks successfully when its one or more motion executers are in fault. The control strategy is based on reconstructable fault-tolerant control theory, and the active disturbance rejection control （ADRC） is used to achieve the design and implementation of controllers. The strategy includes two control methods, in which one is used when the motion executers work in normal and the other is used when one or more motion executers are in fault. These two methods can change from one to another according to the fault diagnosis of the executers. In simulation experiment, the control strategy is tested in different environmental disturbances and compared with the strategy with PID. The results show that the depth fault-tolerant control strategy based on reconstructable fault-tolerant control theory and AD- RC is not only effective but also has stronger disturbance resistance.