A parallel neural network-based controller （PNNC） is presented for the motion control of underwater vehicles in this paper. It consists of a real-time part, a self-learning part and a desired-state programmer, and i...A parallel neural network-based controller （PNNC） is presented for the motion control of underwater vehicles in this paper. It consists of a real-time part, a self-learning part and a desired-state programmer, and it is different from normal adaptive neural network controller in structure. Owing to the introduction of the self-learning part, on-line learning can be performed without sample data in several sample periods, resulting in high learning speed of the controller and good control performance. The desired-state programmer is utilized to obtain better learning samples of the neural network to keep the stability of the controller. The developed controller is applied to the 4-degree of freedom control of the AUV “IUV- IV” and is successful on the simulation platform. The control performance is also compared with that of neural network controller with different structures such as normal adaptive neural network and different learning methods. Current effects and surge velocity control are also included to demonstrate the controller＇ s performance. It is shown that the PNNC has a great possibility to solve the problems in the control system design of underwater vehicles.展开更多
The analysis and simulation of power system are becoming more and more challenging as the complexity of system topology and components has been increased. In this paper, a hybrid parallel algorithm is proposed for the...The analysis and simulation of power system are becoming more and more challenging as the complexity of system topology and components has been increased. In this paper, a hybrid parallel algorithm is proposed for the real-time electromagnetic transient simulation （EMTS） of integrated power systems containing multiphase machines. The proposed algorithm is com- posed of a novel network partition method called component level parallelization and the Multi-Area Thevenin Equivalent （MATE） method, which extends the flexibility of the network partition in parallel simulation. Moreover, several methods are developed to enhance the efficiency of the communication and computation. Power systems with up to 410 single-phase elec- trical nodes and 336 switches are simulated in a time step of 50 ~ts to validate the proposed algorithm.展开更多
文摘A parallel neural network-based controller （PNNC） is presented for the motion control of underwater vehicles in this paper. It consists of a real-time part, a self-learning part and a desired-state programmer, and it is different from normal adaptive neural network controller in structure. Owing to the introduction of the self-learning part, on-line learning can be performed without sample data in several sample periods, resulting in high learning speed of the controller and good control performance. The desired-state programmer is utilized to obtain better learning samples of the neural network to keep the stability of the controller. The developed controller is applied to the 4-degree of freedom control of the AUV “IUV- IV” and is successful on the simulation platform. The control performance is also compared with that of neural network controller with different structures such as normal adaptive neural network and different learning methods. Current effects and surge velocity control are also included to demonstrate the controller＇ s performance. It is shown that the PNNC has a great possibility to solve the problems in the control system design of underwater vehicles.
基金supported by the National Natural Science Foundation of China (Grant Nos. 51277104,51207076)the Postdoctoral Science Foundation of China (Grant No. 20110490351)
文摘The analysis and simulation of power system are becoming more and more challenging as the complexity of system topology and components has been increased. In this paper, a hybrid parallel algorithm is proposed for the real-time electromagnetic transient simulation （EMTS） of integrated power systems containing multiphase machines. The proposed algorithm is com- posed of a novel network partition method called component level parallelization and the Multi-Area Thevenin Equivalent （MATE） method, which extends the flexibility of the network partition in parallel simulation. Moreover, several methods are developed to enhance the efficiency of the communication and computation. Power systems with up to 410 single-phase elec- trical nodes and 336 switches are simulated in a time step of 50 ~ts to validate the proposed algorithm.
