标题： 有限时间范围下高斯-马尔可夫偏差中的预测补偿 显示英文标题

标题： Predictive Compensation in Finite-Horizon LQ Games under Gauss-Markov Deviations

摘要： 本文提出了一种预测补偿框架，用于在存在高斯-马尔可夫偏差的情况下，有限时域离散时间线性二次动态博弈的反馈纳什策略。一名玩家经历通过一阶自回归过程建模的相关随机偏差，而另一名玩家则使用预测策略来预见未来相关性的影响。推导了均值和协方差传播的显式递归关系，并通过期望成本的敏感性分析了性能改进。 显示英文摘要

摘要： This paper presents a predictive compensation framework for finite-horizon discrete-time linear quadratic dynamic games in the presence of Gauss-Markov deviations from feedback Nash strategies. One player experiences correlated stochastic deviations, modeled via a first-order autoregressive process, while the other compensates using a predictive strategy that anticipates the effect of future correlation. Closed-form recursions for mean and covariance propagation are derived, and the resulting performance improvement is analyzed through the sensitivity of expected cost.