This work introduces a novel control strategy called Iterative Linear Quadratic Regulator for Iterative Tasks (i2LQR), which aims to improve closed-loop performance with local trajectory optimization 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 completion time. The results show that i2LQR achieves an optimized performance with respect to learning-based MPC (LMPC) as the benchmark in static environments, and outperforms LMPC in dynamic environments with both static and dynamics obstacles.

翻译：本文提出了一种名为“迭代任务迭代线性二次型调节器”（i2LQR）的新型控制策略，旨在动态环境中通过局部轨迹优化提升迭代任务的闭环性能。该算法无需参考轨迹，利用历史迭代数据增强自主系统的性能。与现有算法不同，i2LQR在每个时间戳以迭代方式计算最优解，使其特别适用于不同迭代中约束条件变化的迭代任务。为评估所提算法的性能，我们针对旨在最小化完成时间的迭代任务进行了数值仿真。结果表明，在静态环境中，i2LQR相较于基于学习的模型预测控制（LMPC）基准实现了更优性能；而在同时包含静态与动态障碍物的动态环境中，其性能超越LMPC。