水下机器人传感器及推进器状态监测系统

为了保障水下机器人作业安全,提高其智能程度,提出了基于RBF(径向基函数)网络和FNN(模糊神经网络)的水下机器人传感器及推进器状态监测系统。根据对预处理后的传感器信号进行分析,传感器监测模型检测传感器的故障,并对出现故障的传感器信号进行恢复,将其作为水下机器人的实际运行状态参数提供给推进器监测模型;推进器监测模型输出与传感器实际输出共同作用,通过评价模型即得到了相关推进器的状态信息,并实现了故障定位。某型水下机器人的真实试验数据的计算机仿真结果验证了提出的监测系统的有效性和可靠性。

CONDITION MONITORING SYSTEM FOR SENSORS AND THRUSTERS OF AUV

A condition monitoring system based on the RBF networks and FNN for the sensors and thrusters is proposed for guaranteeing the safety in task and improving the intelligence degree of underwater vehicles. According to the analysis of the pretreatment signals, the sensors monitoring model detects the fault of sensors, and restores the signals of fault ones, which are provided as the actual running condition parameters of AUV for the thruster monitoring model. For the corporate action of the actual outputs of the thruster and sensors, the condition information of the thrusters is gained by the evaluation model, and it also realizes the faults location. The results of the computer simulated by actual experiment data of a certain AUV prove that the condition monitoring system proposed is effective and feasible.