水下机器人运动控制与故障诊断技术研究
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摘要
二十一世纪是人类开发和利用海洋的世纪,海洋是人类的发源地和资源宝库,对全球的经济和社会发展起到重要的推动作用。“谁控制了海洋和掌握海洋资源开发的技术,谁就掌握了未来世界的资源宝库”,这是对海洋重要性的高度评价。作为人类认识和利用海洋资源的重要工具,水下机器人在海洋资源开发和军事应用方面展示出良好的应用前景。运动控制和故障诊断技术作为水下机器人的核心技术,对其开展深入研究具有重要的理论意义和工程应用价值。论文的主要目的是提出更好的运动控制算法并实现控制系统的故障自主诊断,从而提高水下机器人在恶劣海洋环境下的运动控制性能和生存能力。
作者以“水下机器人运动控制与故障诊断技术研究”为题展开博士论文研究工作。论文首先简要介绍研究对象的基本情况,阐述水下机器人的硬件体系和软件体系结构,导出水下机器人运动仿真模型,为随后的运动控制与故障诊断技术研究奠定理论基础；为了提高水下机器人组合导航系统的精度和可靠性,开展了联邦kalman滤波技术研究。基于信息守恒原则,推导出联邦kalman滤波算法并完成最优性证明。将联邦kalman滤波器应用于水下机器人组合导航,通过试验结果分析验证了联邦kalman滤波器应用于水下机器人的可行性；考虑到普通S面控制器在系统稳定时存在一定的稳态误差,本文在S面控制器中引入智能积分以减小稳定时的稳态误差。将专家系统引入到S面控制器中,构建专家S面控制器,借助领域专家的经验构造控制策略,从而提高控制系统的控制性能。基于普通S面控制器和极板模型控制器的对比分析,提出了广义S面控制器,通过选取不同的S形非线性函数来代替普通S面控制器中的Sigmoid函数以得到不同的S面控制器,相应的结论可以指导控制器的设计过程；针对基于解析模型等故障诊断方法鲁棒性差的缺点,引入滑动模态的思想以提高故障诊断的鲁棒性。由于阈值判别法分析残差时容易受到客观因素和主观因素的影响引起误判,本文引入模糊推理的方式来分析残差信息以克服阈值判别法判断不连续和误报率高的缺点,大量的仿真试验结果表明基于模糊推理的残差分析方法能够区分水下机器人当前是处于状态调整阶段还是状态异常阶段,从而大大提高了故障诊断的准确性,有利于故障诊断鲁棒性的提高；针对神经网络用于故障诊断存在的缺点,将模糊逻辑与神经网络结合构造模糊神经网络以弥补神经网络的不足,阐述了一种模糊神经网络结构,推导出基于最小调整的网络动态学习率以确保当前学习样本的调整结果对历史数据改变最小；对水下机器人传感器信息做小波变换,利用小波变换极值点来检测信号的突变故障。为了消除环境噪声的影响,引入了阈值法,通过对小波变换的高频系数设置阈值来消除噪声干扰。针对定位声纳输出数据的振荡情况,采用了线性平滑方法。设计了三次曲线拟合及kalman滤波并与线性平滑进行了对比试验,试验结果表明：对水下机器人这样一个特定的对象,线性平滑方法不仅处理简单而且直接有效。
本文完成了大量的仿真试验和实际试验,试验结果验证了文中所提方法的可行性和有效性。运动控制算法能够提高控制性能,故障诊断算法可以提高故障诊断的准确性和鲁棒性。
21 century is the century which human develop and utilize ocean. Ocean is the seedbed and resource treasury of human being and it will play great roll in the development of economics and society. "The people who control ocean and master the development technology of ocean resource will control the future resource treasury", these words show the important value of ocean. As the important instrument of knowing and utilizing the ocean resource, underwater vehicle will display its potential usage in ocean development and maritime application. Motion control and fault detection and diagnosis (FDD) are the core technologies of underwater vehicle, and it has crucial theory and practical engineering worth. The main purpose of this dissertation is to propose better motion control algorithms and realize the FDD of control system so as to enhance the motion control performance and life-force of underwater vehicle.
The author undertakes the dissertation research with the title of "Research on Motion Control and Fault Diagnosis of Underwater Vehicle". We introduced the basic configuration of the research plant and hardware and software architectures, and the motion model in six-degree freedom was built up. All those will establish the theory foundation for the following passages. The federated kalman filtering technology was studied in order to improve the precision and reliability of combined navigation of underwater vehicle. Based on the information conservation principle, the federated kalman filtering algorithm was deduced and its optimization was demonstrated. The federated kalman filter was adapted for the combined navigation of underwater vehicle, and the feasibility of the federated kalman filter was verified by the experiment results. The typical S-plane controller has certain steady state error when the system is stable; therefore, the intelligent integral was brought in to decrease the steady state error. The expert system was brought in S-plane controller to construct the expert S-plane controller. We use the expert experience for reference, and the control effect was improved. Comparing between the typical S-plane controller and the capacitor-plate model controller, the generalized S-plane controller was presented. Different S-plane controller can be obtained by substitute different S type nonlinear function for Sigmoid function in typical S-plane controller. The analyzing conclusion can direct controller designing. Aiming at the bad robustness of fault detection and diagnosis (FDD) based on analytic model; the sliding mode idea was introduced to enhance the robustness of FDD. The threshold method can cause erroneous judgement because of its own defects; therefore, here we use fuzzy reasoning method to analyze the residual data so as to overcome the shortcoming of the threshold method. Plenty of simulation results told us that the residual analyzing method based on fuzzy reasoning can distinguish between the state adjustment and the state abnormity of underwater vehicle; accordingly, the precision and robustness of FDD were increased. Aiming at the disadvantage of neural network, we combined the fuzzy logic and neural network. A fuzzy neural network structure was introduced, and the neural network dynamic learning rate based on minimum adjustment was deduced to make sure the present sample will has small impact to the history data. The wavelet transform is undertaken for the sensor information of the underwater vehicle, and the extreme points of the wavelet transform are used to detect the jumping faults of the signal. In order to decrease the noise's influence, the threshold method is brought in. The disturbance of the noise can be bucked by setting the threshold for the high frequency parameters of the wavelet transform. As to the oscillation of the outputs of the positioning sonar, the linear smoothing method is adopted. The cubical curve fitting and kalman filter are designed and comparison experiments are conducted among them, and the experiment results say:underwater vehicle as the specific research plant, linear smoothing method is not only very simple but also direct and effective.
This dissertation undertakes plenty of simulation experiments and practical trials, and the experiment results verify the feasibility and validity of the proposed methods. The motion control algorithms can improve the control performance, and the fault detection and diagnosis algorithms can enhance the precision and robustness of FDD.
作者以“水下机器人运动控制与故障诊断技术研究”为题展开博士论文研究工作。论文首先简要介绍研究对象的基本情况,阐述水下机器人的硬件体系和软件体系结构,导出水下机器人运动仿真模型,为随后的运动控制与故障诊断技术研究奠定理论基础；为了提高水下机器人组合导航系统的精度和可靠性,开展了联邦kalman滤波技术研究。基于信息守恒原则,推导出联邦kalman滤波算法并完成最优性证明。将联邦kalman滤波器应用于水下机器人组合导航,通过试验结果分析验证了联邦kalman滤波器应用于水下机器人的可行性；考虑到普通S面控制器在系统稳定时存在一定的稳态误差,本文在S面控制器中引入智能积分以减小稳定时的稳态误差。将专家系统引入到S面控制器中,构建专家S面控制器,借助领域专家的经验构造控制策略,从而提高控制系统的控制性能。基于普通S面控制器和极板模型控制器的对比分析,提出了广义S面控制器,通过选取不同的S形非线性函数来代替普通S面控制器中的Sigmoid函数以得到不同的S面控制器,相应的结论可以指导控制器的设计过程；针对基于解析模型等故障诊断方法鲁棒性差的缺点,引入滑动模态的思想以提高故障诊断的鲁棒性。由于阈值判别法分析残差时容易受到客观因素和主观因素的影响引起误判,本文引入模糊推理的方式来分析残差信息以克服阈值判别法判断不连续和误报率高的缺点,大量的仿真试验结果表明基于模糊推理的残差分析方法能够区分水下机器人当前是处于状态调整阶段还是状态异常阶段,从而大大提高了故障诊断的准确性,有利于故障诊断鲁棒性的提高；针对神经网络用于故障诊断存在的缺点,将模糊逻辑与神经网络结合构造模糊神经网络以弥补神经网络的不足,阐述了一种模糊神经网络结构,推导出基于最小调整的网络动态学习率以确保当前学习样本的调整结果对历史数据改变最小；对水下机器人传感器信息做小波变换,利用小波变换极值点来检测信号的突变故障。为了消除环境噪声的影响,引入了阈值法,通过对小波变换的高频系数设置阈值来消除噪声干扰。针对定位声纳输出数据的振荡情况,采用了线性平滑方法。设计了三次曲线拟合及kalman滤波并与线性平滑进行了对比试验,试验结果表明：对水下机器人这样一个特定的对象,线性平滑方法不仅处理简单而且直接有效。
本文完成了大量的仿真试验和实际试验,试验结果验证了文中所提方法的可行性和有效性。运动控制算法能够提高控制性能,故障诊断算法可以提高故障诊断的准确性和鲁棒性。
21 century is the century which human develop and utilize ocean. Ocean is the seedbed and resource treasury of human being and it will play great roll in the development of economics and society. "The people who control ocean and master the development technology of ocean resource will control the future resource treasury", these words show the important value of ocean. As the important instrument of knowing and utilizing the ocean resource, underwater vehicle will display its potential usage in ocean development and maritime application. Motion control and fault detection and diagnosis (FDD) are the core technologies of underwater vehicle, and it has crucial theory and practical engineering worth. The main purpose of this dissertation is to propose better motion control algorithms and realize the FDD of control system so as to enhance the motion control performance and life-force of underwater vehicle.
The author undertakes the dissertation research with the title of "Research on Motion Control and Fault Diagnosis of Underwater Vehicle". We introduced the basic configuration of the research plant and hardware and software architectures, and the motion model in six-degree freedom was built up. All those will establish the theory foundation for the following passages. The federated kalman filtering technology was studied in order to improve the precision and reliability of combined navigation of underwater vehicle. Based on the information conservation principle, the federated kalman filtering algorithm was deduced and its optimization was demonstrated. The federated kalman filter was adapted for the combined navigation of underwater vehicle, and the feasibility of the federated kalman filter was verified by the experiment results. The typical S-plane controller has certain steady state error when the system is stable; therefore, the intelligent integral was brought in to decrease the steady state error. The expert system was brought in S-plane controller to construct the expert S-plane controller. We use the expert experience for reference, and the control effect was improved. Comparing between the typical S-plane controller and the capacitor-plate model controller, the generalized S-plane controller was presented. Different S-plane controller can be obtained by substitute different S type nonlinear function for Sigmoid function in typical S-plane controller. The analyzing conclusion can direct controller designing. Aiming at the bad robustness of fault detection and diagnosis (FDD) based on analytic model; the sliding mode idea was introduced to enhance the robustness of FDD. The threshold method can cause erroneous judgement because of its own defects; therefore, here we use fuzzy reasoning method to analyze the residual data so as to overcome the shortcoming of the threshold method. Plenty of simulation results told us that the residual analyzing method based on fuzzy reasoning can distinguish between the state adjustment and the state abnormity of underwater vehicle; accordingly, the precision and robustness of FDD were increased. Aiming at the disadvantage of neural network, we combined the fuzzy logic and neural network. A fuzzy neural network structure was introduced, and the neural network dynamic learning rate based on minimum adjustment was deduced to make sure the present sample will has small impact to the history data. The wavelet transform is undertaken for the sensor information of the underwater vehicle, and the extreme points of the wavelet transform are used to detect the jumping faults of the signal. In order to decrease the noise's influence, the threshold method is brought in. The disturbance of the noise can be bucked by setting the threshold for the high frequency parameters of the wavelet transform. As to the oscillation of the outputs of the positioning sonar, the linear smoothing method is adopted. The cubical curve fitting and kalman filter are designed and comparison experiments are conducted among them, and the experiment results say:underwater vehicle as the specific research plant, linear smoothing method is not only very simple but also direct and effective.
This dissertation undertakes plenty of simulation experiments and practical trials, and the experiment results verify the feasibility and validity of the proposed methods. The motion control algorithms can improve the control performance, and the fault detection and diagnosis algorithms can enhance the precision and robustness of FDD.
引文
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