自适应预测权重的空战鲁棒机动决策方法

针对空战态势变化剧烈的近距空战问题,提出一种具有自适应状态预测权重调整机制的鲁棒机动决策优化方法。首先,为使无人作战飞行器对态势参数的变化波动具有一定的鲁棒性,设计了鲁棒态势函数来表征空战态势。然后,针对目标机动的不确定性问题,利用可达集理论对目标机动意图和状态提前预测,同时采用自适应预测权重系数对无人作战飞行器的攻击和防御进行调整。最后,利用改进的共生生物优化算法对机动决策控制量寻优。仿真结果表明,无人作战飞行器运用该方法能够生成一条理想的近距攻击占位轨迹,实现近距自主空战攻击占位。

Robust maneuvering decision-making method for air combat using adaptive prediction weight

Aiming at the problem of close range air combat with drastic changes in air combat situation, a robust maneuvering decision-making optimization method with adaptive state prediction weight adjustment mechanism is proposed. Firstly, in order to make the unmanned combat air vehicle (UCAV) robust to the fluctuation of situation parameters, a robust situation function is designed to represent the air combat situation. Then, aiming at the uncertainty of target maneuvering, the reachable set theory is used to predict the maneuvering intention and state of the target in advance, and the adaptive prediction weight coefficient is used to adjust the attack and defense of the UCAV. Finally, the improved symbiotic biological optimization algorithm is used to optimize the maneuvering decision control variables. The simulation results show that the UCAV can gene-rate an ideal close range attack occupation trajectory by using this method, and realize the close range autonomous air combat attack occupation.