针对在弱电网下直驱风电机组引起的次同步振荡(subsynchronousoscillation,SSO)现象,提出基于一阶总扰动偏差控制的微分前馈线性自抗扰控制器(linear active disturbance rejection control,LADRC),采用全改进LADRC控制策略抑制SSO现象...针对在弱电网下直驱风电机组引起的次同步振荡(subsynchronousoscillation,SSO)现象,提出基于一阶总扰动偏差控制的微分前馈线性自抗扰控制器(linear active disturbance rejection control,LADRC),采用全改进LADRC控制策略抑制SSO现象(“全”是指电压外环、电流内环以及PLL锁相环3个环节都采用相应的控制)。首先,建立直驱风电机组并网数学模型;其次,结合风电机组并网系统对改进LADRC控制器进行设计并对其进行特性分析,该控制器相较于传统LADRC,不仅减小系统的跟踪误差且抗干扰性能更强;最后,通过PSCAD/EMTDC仿真软件将本文策略与全PI、全传统LADRC进行仿真对比。结果表明:相较于全传统LADRC,本文方法在降低1.62%超调量的同时,缩短0.129 s系统调节时间,有效抑制SSO现象并且具有较好的适应性。展开更多
The guidance strategy is an extremely critical factor in determining the striking effect of the missile operation.A novel guidance law is presented by exploiting the deep reinforcement learning(DRL)with the hierarchic...The guidance strategy is an extremely critical factor in determining the striking effect of the missile operation.A novel guidance law is presented by exploiting the deep reinforcement learning(DRL)with the hierarchical deep deterministic policy gradient(DDPG)algorithm.The reward functions are constructed to minimize the line-of-sight(LOS)angle rate and avoid the threat caused by the opposed obstacles.To attenuate the chattering of the acceleration,a hierarchical reinforcement learning structure and an improved reward function with action penalty are put forward.The simulation results validate that the missile under the proposed method can hit the target successfully and keep away from the threatened areas effectively.展开更多
基金supported by the National Natural Science Foundation of China(62003021,91212304).
文摘The guidance strategy is an extremely critical factor in determining the striking effect of the missile operation.A novel guidance law is presented by exploiting the deep reinforcement learning(DRL)with the hierarchical deep deterministic policy gradient(DDPG)algorithm.The reward functions are constructed to minimize the line-of-sight(LOS)angle rate and avoid the threat caused by the opposed obstacles.To attenuate the chattering of the acceleration,a hierarchical reinforcement learning structure and an improved reward function with action penalty are put forward.The simulation results validate that the missile under the proposed method can hit the target successfully and keep away from the threatened areas effectively.
