基于自主航行系统的无人船海上航迹多目标控制技术研究

航迹控制存在累积跟踪控制误差较高的问题,容易导致无人船发生安全事故,因此,应对基于自主航行系统的无人船海上航迹多目标控制技术加以研究。本文基于自主航行系统定位无人船,根据系统采集的海域环境信息规划无人船海上航迹,设计由动态神经网络结合扰动观测器的复合控制器,实现无人船海上航迹多目标跟踪控制。实验结果表明,研究技术可以实时跟踪控制无人船海上航迹,提高无人船航行的安全性,且在多目标跟踪控制误差上具有显著优势。

基于综合监测系统的无人船自主航行能力评价

为提升无人船自主航行测试与评价能力,提出一种应用多维信息统一采集、船-岸-海协同测量与模块化设计技术开发的无人船自主航行综合监测系统。该系统具有系统配置、数据实时采集与显示、数据存储与回放、船岸通信等功能。通过对无人船在执行自主循迹与自主避碰等典型自主航行任务时产生的航行数据进行实测与存储,结合评价指标的数学模型与量化算法对试验数据进行计算与分析,获得无人船自主航行能力的定量评价结果,为自主航行系统能力的测评提供重要支撑,并为后续升级与开发提供指导。

5G通信技术在船舶电气控制系统中的应用

文章探讨了5G通信技术在船舶电气控制系统中的应用。5G通信技术凭借其高带宽、低时延以及高可靠性,显著提升了船舶电气系统的实时监控、远程控制以及智能电网管理能力。同时,5G通信技术在自主航行与避障系统、船岸一体化通信中发挥着关键作用。通过优化能耗管理,提升系统的灵活性和适应性,5G通信技术为船舶电气系统的智能化发展提供了重要支持。

基于网络控制系统的水下自主航行器制导系统结构

在现代控制系统中,网络控制系统可以用最小成本实现柔性系统,该柔性系统可以完成包括功能可重新配置的许多任务;网络控制系统采用公用的总线结构,使得系统具有更加有效的灵活功能、更好的资源共享、降低系统的维护成本,网络控制系统的最大问题是由于网络而产生的时延和数据包的丢失;研究了基于网络控制原理的水下自主航行器制导系统,详细分析了水下自主航行器网络控制系统的稳定性问题和基于时间触发的TTCAN高层协议;论文旨在通过对水下自主航行器网络控制系统结构的研究,指出其结构的意义和进一步的研究领域。

自主水下航行器机械臂系统非线性PD控制器设计

针对自主水下航行器机械臂系统(AUVMS)的非线性、高耦合性和工作环境的复杂性,文中设计了基于线性扩张观测器(LESO)的控制方案。以带双连杆机械臂的自主水下航行器(AUV)为例,将系统分为5个子系统,将外部扰动(洋流、浪涌和涡流等干扰)与内部不确定性(建模不精确、耦合影响和测量误差等)视作总扰动,并设计LESO和线性反馈控制律进行估计与补偿。对LESO的收敛性与估计误差动力学进行了分析。考虑到AUV动力装置所产生的动力和力矩是有限的,在实际控制中需要对控制输入量针对动力装置的物理特性来设置上限,并且少有文献对带机械臂系统的AUV饱和控制进行研究,文中设计了具有输入饱和限制的非线性比例-微分(PD)控制器,并对控制器的稳定性进行了分析。利用LESO的总扰动思想来处理AUV机械臂系统的非线性、高耦合性和复杂扰动的控制难点,并以输入饱和控制器确保了控制方案的工业适用性。最后通过与传统PID与滑模控制器的对比分析及仿真验证了所提方案的有效性。

视景仿真可信度评估体系的扩充及其应用

随着视景仿真技术的发展,仿真可信度评估也应相应扩充,满足全面评价视景仿真系统的需求。首先针对目前视景仿真技术出现的新形式进行了探讨,提出了对可信度评估进行扩充的一种思路,给出了视景仿真系统可信度评估的体系结构,并对模糊AHP评估方法进行了改进以减少评判专家的主观臆断;然后依据扩充后的可信度评估体系并结合改进的模糊AHP评估方法对某新型远程自主式水下航行器仿真系统进行了评估,最后给出了该仿真系统的定量可信度。实践证明,扩充后的视景可信度评估适用范围更加广泛,具有一定应用前景和工程使用价值。

Application of unscented Kalman filter to novel terrain passive integrated navigation system

To improve the navigation accuracy of an autonomous underwater vehicle （AUV）, a novel terrain passive integrated navigation system （TPINS） is presented. According to the characteristics of the underwater environment and AUV navigation requirements of low cost and high accuracy, a novel TPINS is designed with a configuration of the strapdown inertial navigation system （SINS）, the terrain reference navigation system （TRNS）, the Doppler velocity sonar （DVS）, the magnetic compass and the navigation computer utilizing the unscented Kalman filter （UKF） to fuse the navigation information from various navigation sensors. Linear filter equations for the extended Kalman filter （EKF）, nonlinear filter equations for the UKF and measurement equations of navigation sensors are addressed. It is indicated from the comparable simulation experiments of the EKF and the UKF that AUV navigation precision is improved substantially with the proposed sensors and the UKF when compared to the EKF. The TPINS designed with the proposed sensors and the UKF is effective in reducing AUV navigation position errors and improving the stability and precision of the AUV underwater integrated navigation.

Nonlinear trajectory tracking control of a new autonomous underwater vehicle in complex sea conditions

Autonomous underwater vehicles (AUVs) navigating in complex sea conditions usually require a strong control system to keep the fastness and stability. The nonlinear trajectory tracking control system of a new AUV in complex sea conditions was presented. According to the theory of submarines,the six-DOF kinematic and dynamic models were decomposed into two mutually non-coupled vertical and horizontal plane subsystems. Then,different sliding mode control algorithms were used to study the trajectory tracking control. Because the yaw angle and yaw angle rate rather than the displacement of the new AUV can be measured directly on the horizontal plane,the sliding mode control algorithm combining cross track error method and line of sight method was used to fulfill its high-precision trajectory tracking control in the complex sea conditions. As the vertical displacement of the new AUV can be measured,in order to achieve the tracking of time-varying depth signal,a stable sliding mode controller was designed based on the single-input multi-state system,which took into account the characteristic of the hydroplane and the amplitude and rate constraints of the hydroplane angle. Moreover,the application of dynamic boundary layer can improve the robustness and control accuracy of the system. The computational results show that the designed sliding mode control systems of the horizontal and vertical planes can ensure the trajectory tracking performance and accuracy of the new AUV in complex sea conditions. The impacts of currents and waves on the sliding mode controller of the new AUV were analyzed qualitatively and quantitatively by comparing the trajectory tracking performance of the new AUV in different sea conditions,which provides an effective theoretical guidance and technical support for the control system design of the new AUV in real complex environment.

Dynamics Modeling and Simulation of Autonomous Underwater Vehicles with Appendages

To provide a simulation system platform for designing and debugging a small autonomous underwater vehicle＇s （AUV） motion controller, a six-degree of freedom （6-DOF） dynamic model for AUV controlled by thruster and fins with appendages is examined. Based on the dynamic model, a simulation system for the AUV＇s motion is established. The different kinds of typical motions are simulated to analyze the motion performance and the maneuverability of the AUV. In order to evaluate the influences of appendages on the motion performance of the AUV, simulations of the AUV with and without appendages are performed and compared. The results demonstrate the AUV has good maneuverability with and without appendages.

Actuator fault diagnosis of autonomous underwater vehicle based on improved Elman neural network

Autonomous underwater vehicles(AUV) work in a complex marine environment. Its system reliability and autonomous fault diagnosis are particularly important and can provide the basis for underwater vehicles to take corresponding security policy in a failure. Aiming at the characteristics of the underwater vehicle which has uncertain system and modeling difficulty, an improved Elman neural network is introduced which is applied to the underwater vehicle motion modeling. Through designing self-feedback connection with fixed gain in the unit connection as well as increasing the feedback of the output layer node, improved Elman network has faster convergence speed and generalization ability. This method for high-order nonlinear system has stronger identification ability. Firstly, the residual is calculated by comparing the output of the underwater vehicle model(estimation in the motion state) with the actual measured values. Secondly, characteristics of the residual are analyzed on the basis of fault judging criteria. Finally, actuator fault diagnosis of the autonomous underwater vehicle is carried out. The results of the simulation experiment show that the method is effective.