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
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.
