Inverse Optimal Cooperative Control for Unmanned Surface Vessel Cluster
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摘要: 为实现通过数据驱动学习人为操作下的无人艇集群最优协同控制策略, 文中提出了一种线性二次型闭环微分博弈反演优化算法, 根据观测到的系统最优状态和控制输入轨迹辨识协同策略目标函数。首先, 根据观测到的含加性白噪声的最优系统状态和控制输入轨迹辨识最优反馈矩阵; 然后, 通过求解由纳什平衡充要条件推出的耦合代数黎卡提方程的解来辨识协同策略目标函数。所提出的反演优化算法能够获得满足给定系统状态和控制输入轨迹的最优协同策略目标函数; 同时, 该算法辨识出的目标函数可以用于实现针对特定任务场景的无人艇集群最优协同控制, 并为集群的对抗博弈提供新的思路和解决方案。Abstract: To realize an optimal cooperative control strategy of unmanned surface vessel(USV) clusters under artificial control through data-driven learning, a linear quadratic closed-loop differential game inverse optimization algorithm is proposed. The algorithm can identify the cooperative strategy objective function according to the optimal system state and control input trajectories. In this study, an optimal feedback matrix is first identified based on the observed optimal system state and control input trajectories with additive white noise. The cooperative strategy objective function is then identified after solving the coupled algebraic Riccati equations derived from the necessary and sufficient conditions for Nash equilibria. The proposed inverse optimization algorithm can obtain the optimal cooperative strategy objective function to satisfy the given system state and control input trajectories. The objective functions identified by the inverse optimization algorithm can then be used to achieve an optimal cooperative control of USV clusters for specific task scenarios and provide new ideas and solutions for cluster adversarial games.
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