To solve the path following control problem for unmanned surface vehicles(USVs),a control method based on deep reinforcement learning(DRL)with long short-term memory(LSTM)networks is proposed.A distributed proximal po...To solve the path following control problem for unmanned surface vehicles(USVs),a control method based on deep reinforcement learning(DRL)with long short-term memory(LSTM)networks is proposed.A distributed proximal policy opti-mization(DPPO)algorithm,which is a modified actor-critic-based type of reinforcement learning algorithm,is adapted to improve the controller performance in repeated trials.The LSTM network structure is introduced to solve the strong temporal cor-relation USV control problem.In addition,a specially designed path dataset,including straight and curved paths,is established to simulate various sailing scenarios so that the reinforcement learning controller can obtain as much handling experience as possible.Extensive numerical simulation results demonstrate that the proposed method has better control performance under missions involving complex maneuvers than trained with limited scenarios and can potentially be applied in practice.展开更多
In the paper, the effect of heat-treatment on the strength and toughness of AIN-SiC whisker composites with Y2O3 + SiO2 additives have been studied. When the Sample confining 10wt% Y2O3+SiO2(.Y2O3/SiO2^l/0. 66) -was t...In the paper, the effect of heat-treatment on the strength and toughness of AIN-SiC whisker composites with Y2O3 + SiO2 additives have been studied. When the Sample confining 10wt% Y2O3+SiO2(.Y2O3/SiO2^l/0. 66) -was treated at 1330癈 in air for 140 hours ithe flexural strength of composites ivas raised from 481 MPa to 784 MPa the toughness ruas also enhanced slightly. The phase composi-tion infrastructure and grain boundary phase structure have been char-acterized by combining XDR, SEM, TEM/EDXA and HREM tech-niques, reinforcenent and toughening mechanism of the composites re-sults from the crystallization of glass phase in the grain boundary at the high temperature oxidizing atmosphere to form the crossing struc-ture of 2H?sialon fibrous phase and SiC whisker展开更多
基金supported by the National Natural Science Foundation(61601491)the Natural Science Foundation of Hubei Province(2018CFC865)the China Postdoctoral Science Foundation Funded Project(2016T45686).
文摘To solve the path following control problem for unmanned surface vehicles(USVs),a control method based on deep reinforcement learning(DRL)with long short-term memory(LSTM)networks is proposed.A distributed proximal policy opti-mization(DPPO)algorithm,which is a modified actor-critic-based type of reinforcement learning algorithm,is adapted to improve the controller performance in repeated trials.The LSTM network structure is introduced to solve the strong temporal cor-relation USV control problem.In addition,a specially designed path dataset,including straight and curved paths,is established to simulate various sailing scenarios so that the reinforcement learning controller can obtain as much handling experience as possible.Extensive numerical simulation results demonstrate that the proposed method has better control performance under missions involving complex maneuvers than trained with limited scenarios and can potentially be applied in practice.
文摘In the paper, the effect of heat-treatment on the strength and toughness of AIN-SiC whisker composites with Y2O3 + SiO2 additives have been studied. When the Sample confining 10wt% Y2O3+SiO2(.Y2O3/SiO2^l/0. 66) -was treated at 1330癈 in air for 140 hours ithe flexural strength of composites ivas raised from 481 MPa to 784 MPa the toughness ruas also enhanced slightly. The phase composi-tion infrastructure and grain boundary phase structure have been char-acterized by combining XDR, SEM, TEM/EDXA and HREM tech-niques, reinforcenent and toughening mechanism of the composites re-sults from the crystallization of glass phase in the grain boundary at the high temperature oxidizing atmosphere to form the crossing struc-ture of 2H?sialon fibrous phase and SiC whisker
