针对船舶航行中横摇运动控制问题,提出一种基于反步法与径向基函数神经网络(Radial Basis Function Neural Network,RBFNN)的船舶鳍减摇控制律,以实现船舶自适应减摇控制。首先,基于反步法设计获得PD型的控制器,并使用闭环增益成形算法...针对船舶航行中横摇运动控制问题,提出一种基于反步法与径向基函数神经网络(Radial Basis Function Neural Network,RBFNN)的船舶鳍减摇控制律,以实现船舶自适应减摇控制。首先,基于反步法设计获得PD型的控制器,并使用闭环增益成形算法确定其初始参数值;其次,引入RBFNN对PD控制器的初始参数进行优化,以经归一化处理的船舶横摇角度和横摇角速度作为网络输入,动态调节控制参数,从而实现自适应控制;最后,通过在随机波环境下的数值仿真试验,验证了所提控制器的减摇性能。仿真结果表明,该控制器能有效抑制船舶横摇运动,减摇率超过94%,并展现出优异的自适应能力。因此,本研究提出的控制策略可为船舶横摇运动控制提供一种高效且实用的解决方案。展开更多
Fault diagnosis occupies a pivotal position within the domain of machine and equipment management.Existing methods,however,often exhibit limitations in their scope of application,typically focusing on specific types o...Fault diagnosis occupies a pivotal position within the domain of machine and equipment management.Existing methods,however,often exhibit limitations in their scope of application,typically focusing on specific types of signals or faults in individual mechanical components while being constrained by data types and inherent characteristics.To address the limitations of existing methods,we propose a fault diagnosis method based on graph neural networks(GNNs)embedded with multirelationships of intrinsic mode functions(MIMF).The approach introduces a novel graph topological structure constructed from the features of intrinsic mode functions(IMFs)of monitored signals and their multirelationships.Additionally,a graph-level based fault diagnosis network model is designed to enhance feature learning capabilities for graph samples and enable flexible application across diverse signal sources and devices.Experimental validation with datasets including independent vibration signals for gear fault detection,mixed vibration signals for concurrent gear and bearing faults,and pressure signals for hydraulic cylinder leakage characterization demonstrates the model's adaptability and superior diagnostic accuracy across various types of signals and mechanical systems.展开更多
文摘针对船舶航行中横摇运动控制问题,提出一种基于反步法与径向基函数神经网络(Radial Basis Function Neural Network,RBFNN)的船舶鳍减摇控制律,以实现船舶自适应减摇控制。首先,基于反步法设计获得PD型的控制器,并使用闭环增益成形算法确定其初始参数值;其次,引入RBFNN对PD控制器的初始参数进行优化,以经归一化处理的船舶横摇角度和横摇角速度作为网络输入,动态调节控制参数,从而实现自适应控制;最后,通过在随机波环境下的数值仿真试验,验证了所提控制器的减摇性能。仿真结果表明,该控制器能有效抑制船舶横摇运动,减摇率超过94%,并展现出优异的自适应能力。因此,本研究提出的控制策略可为船舶横摇运动控制提供一种高效且实用的解决方案。
文摘Fault diagnosis occupies a pivotal position within the domain of machine and equipment management.Existing methods,however,often exhibit limitations in their scope of application,typically focusing on specific types of signals or faults in individual mechanical components while being constrained by data types and inherent characteristics.To address the limitations of existing methods,we propose a fault diagnosis method based on graph neural networks(GNNs)embedded with multirelationships of intrinsic mode functions(MIMF).The approach introduces a novel graph topological structure constructed from the features of intrinsic mode functions(IMFs)of monitored signals and their multirelationships.Additionally,a graph-level based fault diagnosis network model is designed to enhance feature learning capabilities for graph samples and enable flexible application across diverse signal sources and devices.Experimental validation with datasets including independent vibration signals for gear fault detection,mixed vibration signals for concurrent gear and bearing faults,and pressure signals for hydraulic cylinder leakage characterization demonstrates the model's adaptability and superior diagnostic accuracy across various types of signals and mechanical systems.
