快速可行的灾后恢复是弹性电力系统的重要一环。提出一种弹性导向的考虑修复不确定性和V2G(vehicle to grid,V2G)的城市电力系统动态供电恢复方法,灾后短期内应用V2G站中的有限电动汽车资源为城市电网提供电能支撑,同时派遣抢修队对故...快速可行的灾后恢复是弹性电力系统的重要一环。提出一种弹性导向的考虑修复不确定性和V2G(vehicle to grid,V2G)的城市电力系统动态供电恢复方法,灾后短期内应用V2G站中的有限电动汽车资源为城市电网提供电能支撑,同时派遣抢修队对故障线路进行抢修作业,实现系统快速恢复。对V2G和电动汽车灾前、灾后行为进行建模,通过灾前自由调度统计各V2G站附近的电动汽车数量,按照响应意愿比例得到灾后阶段各V2G站反向输电功率;对包含行驶行为和修复行为的动态修复行为进行建模,采用滚动时域优化方法决策修复的先后顺序,得到抢修队最优动态修复方案。最后,设计多组算例以验证所提方法的可行性,结果表明,该方法可有效支撑灾后城市电力系统快速恢复,提高系统弹性。展开更多
To address the shortcomings of single-step decision making in the existing deep reinforcement learning based unmanned aerial vehicle(UAV)real-time path planning problem,a real-time UAV path planning algorithm based on...To address the shortcomings of single-step decision making in the existing deep reinforcement learning based unmanned aerial vehicle(UAV)real-time path planning problem,a real-time UAV path planning algorithm based on long shortterm memory(RPP-LSTM)network is proposed,which combines the memory characteristics of recurrent neural network(RNN)and the deep reinforcement learning algorithm.LSTM networks are used in this algorithm as Q-value networks for the deep Q network(DQN)algorithm,which makes the decision of the Q-value network has some memory.Thanks to LSTM network,the Q-value network can use the previous environmental information and action information which effectively avoids the problem of single-step decision considering only the current environment.Besides,the algorithm proposes a hierarchical reward and punishment function for the specific problem of UAV real-time path planning,so that the UAV can more reasonably perform path planning.Simulation verification shows that compared with the traditional feed-forward neural network(FNN)based UAV autonomous path planning algorithm,the RPP-LSTM proposed in this paper can adapt to more complex environments and has significantly improved robustness and accuracy when performing UAV real-time path planning.展开更多
Aiming at the shortcoming that the traditional industrial manipulator using off-line programming cannot change along with the change of external environment,the key technologies such as machine vision and manipulator ...Aiming at the shortcoming that the traditional industrial manipulator using off-line programming cannot change along with the change of external environment,the key technologies such as machine vision and manipulator control are studied,and a complete manipulator vision tracking system is designed.Firstly,Denavit-Hartenberg(D-H)parameters method is used to construct the model of the manipulator and analyze the forward and inverse kinematics equations of the manipulator.At the same time,a binocular camera is used to obtain the threedimensional position of the target.Secondly,in order to make the manipulator track the target more accurately,the fuzzy adaptive square root unscented Kalman filter(FSRUKF)is proposed to estimate the target state.Finally,the manipulator tracking system is built by using the position-based visual servo.The simulation experiments show that FSRUKF converges faster and with less error than the square root unscented Kalman filter(SRUKF),which meets the application requirements of the manipulator tracking system,and basically meets the application requirements of the manipulator tracking system in the practical experiments.展开更多
文摘快速可行的灾后恢复是弹性电力系统的重要一环。提出一种弹性导向的考虑修复不确定性和V2G(vehicle to grid,V2G)的城市电力系统动态供电恢复方法,灾后短期内应用V2G站中的有限电动汽车资源为城市电网提供电能支撑,同时派遣抢修队对故障线路进行抢修作业,实现系统快速恢复。对V2G和电动汽车灾前、灾后行为进行建模,通过灾前自由调度统计各V2G站附近的电动汽车数量,按照响应意愿比例得到灾后阶段各V2G站反向输电功率;对包含行驶行为和修复行为的动态修复行为进行建模,采用滚动时域优化方法决策修复的先后顺序,得到抢修队最优动态修复方案。最后,设计多组算例以验证所提方法的可行性,结果表明,该方法可有效支撑灾后城市电力系统快速恢复,提高系统弹性。
基金supported by the Natural Science Basic Research Prog ram of Shaanxi(2022JQ-593)。
文摘To address the shortcomings of single-step decision making in the existing deep reinforcement learning based unmanned aerial vehicle(UAV)real-time path planning problem,a real-time UAV path planning algorithm based on long shortterm memory(RPP-LSTM)network is proposed,which combines the memory characteristics of recurrent neural network(RNN)and the deep reinforcement learning algorithm.LSTM networks are used in this algorithm as Q-value networks for the deep Q network(DQN)algorithm,which makes the decision of the Q-value network has some memory.Thanks to LSTM network,the Q-value network can use the previous environmental information and action information which effectively avoids the problem of single-step decision considering only the current environment.Besides,the algorithm proposes a hierarchical reward and punishment function for the specific problem of UAV real-time path planning,so that the UAV can more reasonably perform path planning.Simulation verification shows that compared with the traditional feed-forward neural network(FNN)based UAV autonomous path planning algorithm,the RPP-LSTM proposed in this paper can adapt to more complex environments and has significantly improved robustness and accuracy when performing UAV real-time path planning.
基金supported by Natural Science Basic Research Program of Shaanxi(2022JQ-593)Key Research and Development Program of Shaanxi(2022GY-089)。
文摘Aiming at the shortcoming that the traditional industrial manipulator using off-line programming cannot change along with the change of external environment,the key technologies such as machine vision and manipulator control are studied,and a complete manipulator vision tracking system is designed.Firstly,Denavit-Hartenberg(D-H)parameters method is used to construct the model of the manipulator and analyze the forward and inverse kinematics equations of the manipulator.At the same time,a binocular camera is used to obtain the threedimensional position of the target.Secondly,in order to make the manipulator track the target more accurately,the fuzzy adaptive square root unscented Kalman filter(FSRUKF)is proposed to estimate the target state.Finally,the manipulator tracking system is built by using the position-based visual servo.The simulation experiments show that FSRUKF converges faster and with less error than the square root unscented Kalman filter(SRUKF),which meets the application requirements of the manipulator tracking system,and basically meets the application requirements of the manipulator tracking system in the practical experiments.
