The model-driven architecture(MDA)/model-based systems engineering(MBSE)approach,in combination with the real-time Unified Modeling Language(UML)/Systems Modeling Language(SysML),unscented Kalman filter(UKF)algorithm,...The model-driven architecture(MDA)/model-based systems engineering(MBSE)approach,in combination with the real-time Unified Modeling Language(UML)/Systems Modeling Language(SysML),unscented Kalman filter(UKF)algorithm,and hybrid automata,are specialized to conveniently analyze,design,and implement controllers of autonomous underwater vehicles(AUVs).The dynamics and control structure of AUVs are adapted and integrated with the specialized features of the MDA/MBSE approach as follows.The computation-independent model is defined by the specification of a use case model together with the UKF algorithm and hybrid automata and is used in intensive requirement analysis.The platform-independent model(PIM)is then built by specializing the real-time UML/SysML’s features,such as the main control capsules and their dynamic evolutions,which reflect the structures and behaviors of controllers.The detailed PIM is subsequently converted into the platform-specific model by using open-source platforms to quickly implement and deploy AUV controllers.The study ends with a trial trip and deployment results for a planar trajectory-tracking controller of a miniature AUV with a torpedo shape.展开更多
为了提高水下自主机器人(autonomous underwater vehicle,AUV)在海洋执行任务时对深度和航向控制的品质,以及它的抗干扰能力。本文提出了一种新的方法,即通过训练人工神经网络来改变模糊PID(proportional integral derivative)控制的模...为了提高水下自主机器人(autonomous underwater vehicle,AUV)在海洋执行任务时对深度和航向控制的品质,以及它的抗干扰能力。本文提出了一种新的方法,即通过训练人工神经网络来改变模糊PID(proportional integral derivative)控制的模糊规则和隶属度函数,从而实现更加精确的模糊控制。最后将设计的模糊神经PID控制算法与建立的AUV动力模型相结合。为验证模糊神经PID控制器的有效性,将传统PID、模糊PID控制算法作为对比,同时,人为加入了干扰因素。通过MATLAB/Simulink仿真实验的验证发现,采用模糊神经PID控制器来控制AUV,可以获得更少的反应时间,更好的稳定性,以及更强的抗干扰性,而且控制效果远超其他控制方式。展开更多
The new AUV driven by multi-vectored thrusters not only has unique kinematic characteristics during the actual cruise but also exists uncertain factors such as hydrodynamic coefficients perturbation and unknown interf...The new AUV driven by multi-vectored thrusters not only has unique kinematic characteristics during the actual cruise but also exists uncertain factors such as hydrodynamic coefficients perturbation and unknown interference of tail fluid, which bring difficult to the stability of the AUV＇s control system. In order to solve the nonlinear term and unmodeled dynamics existing in the new AUV＇s attitude control and the disturbances caused by the external marine environment, a second-order sliding mode controller with double-loop structure that considering the dynamic characteristics of the rudder actuators is designed, which improves the robustness of the system and avoids the control failure caused by the problem that the design theory of the sliding mode controller does not match with the actual application conditions. In order to avoid the loss of the sliding mode caused by the amplitude and rate constraints of the rudder actuator in the new AUV＇s attitude control, the dynamic boundary layer method is used to adjust the sliding boundary layer thickness so as to obtain the best anti-chattering effects. Then the impacts of system parameters, rudder actuator＇s constraints and boundary layer on the sliding mode controller are computed and analyzed to verify the effectiveness and robustness of the sliding mode controller based on dynamic boundary layer. The computational results show that the original divergent second-order sliding mode controller can still effectively implement the AUV＇s attitude control through dynamically adjusting the sliding boundary layer thickness. The dynamic boundary layer method ensures the stability of the system and does not exceed the amplitude constraint of the rudder actuator, which provides a theoretical guidance and technical support for the control system design of the new AUV in real complex sea conditions.展开更多
In this paper,we investigate formation tracking control of autonomous underwater vehicles(AUVs)with model parameter uncertainties and external disturbances.The external disturbances due to the wind,waves,and ocean cur...In this paper,we investigate formation tracking control of autonomous underwater vehicles(AUVs)with model parameter uncertainties and external disturbances.The external disturbances due to the wind,waves,and ocean currents are combined with the model parameter uncertainties as a compound disturbance.Then a disturbance observer(DO)is introduced to estimate the compound disturbance,which can be achieved within a finite time independent of the initial estimation error.Based on a DO,a novel fixed-time sliding control scheme is developed,by which the follower vehicle can track the leader vehicle with all the states globally stabilized within a given settling time.The effectiveness and performance of the method are demonstrated by numerical simulations.展开更多
Aimed at uncertainties and model's impreciseness, nonlinearity and time-variability of depth control system in autonomous underwater vehicle (AUV), a depth predictive control method was put forward based on rough ...Aimed at uncertainties and model's impreciseness, nonlinearity and time-variability of depth control system in autonomous underwater vehicle (AUV), a depth predictive control method was put forward based on rough set (RS) and least squares support vector machine (LSSVM). By using RS theory, the monitor data attribute of AUV was reduced to eliminate the redundant information and to improve efficiency. Then, LSSVM model was trained by using the reduced rules, and its parameters were optimized by using chaos theory for the higher accurate control. Taken an AUV typed NPS Phoenix as an example, its depth step response, horizontal rudder and pitch change were simulated. The simulation results show that the method improves the model's accuracy and has better real-time response, fault-tolerant ability, reliability and strong anti-interfere capability.展开更多
文摘The model-driven architecture(MDA)/model-based systems engineering(MBSE)approach,in combination with the real-time Unified Modeling Language(UML)/Systems Modeling Language(SysML),unscented Kalman filter(UKF)algorithm,and hybrid automata,are specialized to conveniently analyze,design,and implement controllers of autonomous underwater vehicles(AUVs).The dynamics and control structure of AUVs are adapted and integrated with the specialized features of the MDA/MBSE approach as follows.The computation-independent model is defined by the specification of a use case model together with the UKF algorithm and hybrid automata and is used in intensive requirement analysis.The platform-independent model(PIM)is then built by specializing the real-time UML/SysML’s features,such as the main control capsules and their dynamic evolutions,which reflect the structures and behaviors of controllers.The detailed PIM is subsequently converted into the platform-specific model by using open-source platforms to quickly implement and deploy AUV controllers.The study ends with a trial trip and deployment results for a planar trajectory-tracking controller of a miniature AUV with a torpedo shape.
文摘为了提高水下自主机器人(autonomous underwater vehicle,AUV)在海洋执行任务时对深度和航向控制的品质,以及它的抗干扰能力。本文提出了一种新的方法,即通过训练人工神经网络来改变模糊PID(proportional integral derivative)控制的模糊规则和隶属度函数,从而实现更加精确的模糊控制。最后将设计的模糊神经PID控制算法与建立的AUV动力模型相结合。为验证模糊神经PID控制器的有效性,将传统PID、模糊PID控制算法作为对比,同时,人为加入了干扰因素。通过MATLAB/Simulink仿真实验的验证发现,采用模糊神经PID控制器来控制AUV,可以获得更少的反应时间,更好的稳定性,以及更强的抗干扰性,而且控制效果远超其他控制方式。
基金supported by National Hi-tech Research and Development Program of China (863 Program,Grant No.2006AA09Z235)Hunan Provincial Innovation Foundation for Postgraduate of China (Grant No. CX2009B003)
文摘The new AUV driven by multi-vectored thrusters not only has unique kinematic characteristics during the actual cruise but also exists uncertain factors such as hydrodynamic coefficients perturbation and unknown interference of tail fluid, which bring difficult to the stability of the AUV＇s control system. In order to solve the nonlinear term and unmodeled dynamics existing in the new AUV＇s attitude control and the disturbances caused by the external marine environment, a second-order sliding mode controller with double-loop structure that considering the dynamic characteristics of the rudder actuators is designed, which improves the robustness of the system and avoids the control failure caused by the problem that the design theory of the sliding mode controller does not match with the actual application conditions. In order to avoid the loss of the sliding mode caused by the amplitude and rate constraints of the rudder actuator in the new AUV＇s attitude control, the dynamic boundary layer method is used to adjust the sliding boundary layer thickness so as to obtain the best anti-chattering effects. Then the impacts of system parameters, rudder actuator＇s constraints and boundary layer on the sliding mode controller are computed and analyzed to verify the effectiveness and robustness of the sliding mode controller based on dynamic boundary layer. The computational results show that the original divergent second-order sliding mode controller can still effectively implement the AUV＇s attitude control through dynamically adjusting the sliding boundary layer thickness. The dynamic boundary layer method ensures the stability of the system and does not exceed the amplitude constraint of the rudder actuator, which provides a theoretical guidance and technical support for the control system design of the new AUV in real complex sea conditions.
基金supported in part by the National Natural Science Foundation of China(61573077,U1808205)the National Key Research and Development Program of China(2017YFA0700300)
文摘In this paper,we investigate formation tracking control of autonomous underwater vehicles(AUVs)with model parameter uncertainties and external disturbances.The external disturbances due to the wind,waves,and ocean currents are combined with the model parameter uncertainties as a compound disturbance.Then a disturbance observer(DO)is introduced to estimate the compound disturbance,which can be achieved within a finite time independent of the initial estimation error.Based on a DO,a novel fixed-time sliding control scheme is developed,by which the follower vehicle can track the leader vehicle with all the states globally stabilized within a given settling time.The effectiveness and performance of the method are demonstrated by numerical simulations.
文摘Aimed at uncertainties and model's impreciseness, nonlinearity and time-variability of depth control system in autonomous underwater vehicle (AUV), a depth predictive control method was put forward based on rough set (RS) and least squares support vector machine (LSSVM). By using RS theory, the monitor data attribute of AUV was reduced to eliminate the redundant information and to improve efficiency. Then, LSSVM model was trained by using the reduced rules, and its parameters were optimized by using chaos theory for the higher accurate control. Taken an AUV typed NPS Phoenix as an example, its depth step response, horizontal rudder and pitch change were simulated. The simulation results show that the method improves the model's accuracy and has better real-time response, fault-tolerant ability, reliability and strong anti-interfere capability.
