利用BBR(Bottleneck bandwidth and RTT)算法虽可以实现在复杂网络中带宽的充分利用,但该算法对网络噪音所造成的丢包现象敏感,且该算法因存在协议内部不公平的问题而无法实现物联数据的实时高效获取。针对以上问题,提出了1种基于深度...利用BBR(Bottleneck bandwidth and RTT)算法虽可以实现在复杂网络中带宽的充分利用,但该算法对网络噪音所造成的丢包现象敏感,且该算法因存在协议内部不公平的问题而无法实现物联数据的实时高效获取。针对以上问题,提出了1种基于深度强化学习的起搏增益优化算法(Deep reinforcement learning of BBR,BBR-DRL)。首先,通过获取数据传输的往返时延、发送窗口大小和网络带宽等环境参数来实时感知网络状态;然后,结合环境参数,利用起搏增益进行动态调整,使得BBR算法能够及时与外部动态网络环境进行交互,从而降低丢包敏感度、提高不同往返时延(Round-trip time,RTT)流之间的公平性。实验结果表明,与经典BBR算法相比,所提出的BBR-DRL算法协议内部的公平性提高到了98.2%,丢包敏感性明显降低。展开更多
This paper investigates the boost phase＇s longitudinal autopilot of a ballistic missile equipped with thrust vector control. The existing longitudinal autopilot employs time-invariant passive resistor-inductor-capaci...This paper investigates the boost phase＇s longitudinal autopilot of a ballistic missile equipped with thrust vector control. The existing longitudinal autopilot employs time-invariant passive resistor-inductor-capacitor （RLC） network compensator as a control strategy, which does not take into account the time-varying missile dynamics. This may cause the closed-loop system instability in the presence of large disturbance and dynamics uncertainty. Therefore, the existing controller should be redesigned to achieve more stable vehicle response. In this paper, based on gain-scheduling adaptive control strategy, two different types of optimal controllers are proposed. The first controller is gain-scheduled optimal tuning-proportional-integral-derivative （PID） with actuator constraints, which supplies better response but requires a priori knowledge of the system dynamics. Moreover, the controller has oscillatory response in the presence of dynamic uncertainty. Taking this into account, gain-scheduled optimal linear quadratic （LQ） in conjunction with optimal tuning-compensator offers the greatest scope for controller improvement in the presence of dynamic uncertainty and large disturbance. The latter controller is tested through various scenarios for the validated nonlinear dynamic flight model of the real ballistic missile system with autopilot exposed to external disturbances.展开更多
文摘利用BBR(Bottleneck bandwidth and RTT)算法虽可以实现在复杂网络中带宽的充分利用,但该算法对网络噪音所造成的丢包现象敏感,且该算法因存在协议内部不公平的问题而无法实现物联数据的实时高效获取。针对以上问题,提出了1种基于深度强化学习的起搏增益优化算法(Deep reinforcement learning of BBR,BBR-DRL)。首先,通过获取数据传输的往返时延、发送窗口大小和网络带宽等环境参数来实时感知网络状态;然后,结合环境参数,利用起搏增益进行动态调整,使得BBR算法能够及时与外部动态网络环境进行交互,从而降低丢包敏感度、提高不同往返时延(Round-trip time,RTT)流之间的公平性。实验结果表明,与经典BBR算法相比,所提出的BBR-DRL算法协议内部的公平性提高到了98.2%,丢包敏感性明显降低。
基金National Natural Science Foundation of China (60904066)National Basic Research Program of China (2010CB327904)"Weishi" Young Teachers Talent Cultivation Foundation of Beihang University (YWF-11-03-Q-013)
文摘This paper investigates the boost phase＇s longitudinal autopilot of a ballistic missile equipped with thrust vector control. The existing longitudinal autopilot employs time-invariant passive resistor-inductor-capacitor （RLC） network compensator as a control strategy, which does not take into account the time-varying missile dynamics. This may cause the closed-loop system instability in the presence of large disturbance and dynamics uncertainty. Therefore, the existing controller should be redesigned to achieve more stable vehicle response. In this paper, based on gain-scheduling adaptive control strategy, two different types of optimal controllers are proposed. The first controller is gain-scheduled optimal tuning-proportional-integral-derivative （PID） with actuator constraints, which supplies better response but requires a priori knowledge of the system dynamics. Moreover, the controller has oscillatory response in the presence of dynamic uncertainty. Taking this into account, gain-scheduled optimal linear quadratic （LQ） in conjunction with optimal tuning-compensator offers the greatest scope for controller improvement in the presence of dynamic uncertainty and large disturbance. The latter controller is tested through various scenarios for the validated nonlinear dynamic flight model of the real ballistic missile system with autopilot exposed to external disturbances.
