This paper researches on a kind of control architecture for autonomous undelwater vehicle （AUV）. After describing the hybrid property of the AUV control system, we present the hierarchical AUV control architecture. ...This paper researches on a kind of control architecture for autonomous undelwater vehicle （AUV）. After describing the hybrid property of the AUV control system, we present the hierarchical AUV control architecture. The architecture is organized in three layers： mission layer, task layer and execution layer. State supervisor and task coordinator are two key modules handling discrete events, so we describe these two modules in detail. Finally, we carried out a series of tests to verify this architecture The test results show that the AUV can perform autonomous missions effectively and safely. We can conclude the control architecture is valid and practical.展开更多
In this paper,we propose a real-time energy-efficient anticipative driving control strategy for connected and automated hybrid electric vehicles(HEVs).Considering the inherent complexities brought about by the velocit...In this paper,we propose a real-time energy-efficient anticipative driving control strategy for connected and automated hybrid electric vehicles(HEVs).Considering the inherent complexities brought about by the velocity profile optimization and energy management control,a hierarchical control architecture in the model predictive control(MPC)framework is developed for real-time implementation.In the higher level controller,a novel velocity optimization problem is proposed to realize safe and energy-efficient anticipative driving.The real-time control actions are derived through a computationally efficient algorithm.In the lower level controller,an explicit solution of the optimal torque split ratio and gear shift schedule is introduced for following the optimal velocity profile obtained from the higher level controller.The comparative simulation results demonstrate that the proposed strategy can achieve approximately 13%fuel consumption saving compared with a benchmark strategy.展开更多
文摘This paper researches on a kind of control architecture for autonomous undelwater vehicle （AUV）. After describing the hybrid property of the AUV control system, we present the hierarchical AUV control architecture. The architecture is organized in three layers： mission layer, task layer and execution layer. State supervisor and task coordinator are two key modules handling discrete events, so we describe these two modules in detail. Finally, we carried out a series of tests to verify this architecture The test results show that the AUV can perform autonomous missions effectively and safely. We can conclude the control architecture is valid and practical.
基金supported by in part by the China Automobile Industry Innovation and Development Joint Fund(No.U1864206)in part by the National Nature Science Foundation of China(No.62003244)+1 种基金in part by the Jilin Provincial Science and Technology Department(No.20200301011RQ)in part by the Jilin Provincial Science Foundation of China(No.20200201062JC).
文摘In this paper,we propose a real-time energy-efficient anticipative driving control strategy for connected and automated hybrid electric vehicles(HEVs).Considering the inherent complexities brought about by the velocity profile optimization and energy management control,a hierarchical control architecture in the model predictive control(MPC)framework is developed for real-time implementation.In the higher level controller,a novel velocity optimization problem is proposed to realize safe and energy-efficient anticipative driving.The real-time control actions are derived through a computationally efficient algorithm.In the lower level controller,an explicit solution of the optimal torque split ratio and gear shift schedule is introduced for following the optimal velocity profile obtained from the higher level controller.The comparative simulation results demonstrate that the proposed strategy can achieve approximately 13%fuel consumption saving compared with a benchmark strategy.
