This work introduces a novel control strategy called Iterative Linear Quadratic Regulator for Iterative Tasks (i2LQR), which aims to pursue optimal performance for iterative tasks in a dynamic environment. The proposed algorithm is reference-free and utilizes historical data from previous iterations to enhance the performance of the autonomous system. Unlike existing algorithms, the i2LQR computes the optimal solution in an iterative manner at each timestamp, rendering it well-suited for iterative tasks with changing constraints at different iterations. To evaluate the performance of the proposed algorithm, we conduct numerical simulations for an iterative task aimed at minimizing time consumption. The results show that i2LQR achieves the optimal performance as the state-of-the-art algorithm in static environments, and outperforms the state-of-the-art algorithm in dynamic environments with both static and dynamics obstacles.

翻译：本文提出一种名为“面向迭代任务的迭代线性二次型调节器”（i2LQR）的新型控制策略，旨在动态环境中实现迭代任务的最优性能。该算法无需参考轨迹，通过利用历史迭代数据提升自主系统的性能。与现有算法不同，i2LQR在每个时间步以迭代方式计算最优解，使其尤其适用于不同迭代步中约束条件发生变化的迭代任务。为评估算法性能，我们针对以最小化时间消耗为目标的迭代任务开展了数值仿真实验。结果表明：i2LQR在静态环境中能达到与现有最优算法相当的性能，而在包含静态与动态障碍物的动态环境中，其性能显著优于现有最优算法。