i2LQR: 動的環境における反復タスクのための反復型LQR
i2LQR: Iterative LQR for Iterative Tasks in Dynamic Environments
過去の反復データを活用し、各時刻で最適解を反復計算する参照不要のLQR制御則を提案。動的障害物を含む環境で学習ベースMPCを上回る性能を示した。
著者: Yifan Zeng, Suiyi He, Han Hoang Nguyen, Yihan Li, Zhongyu Li, Koushil Sreenath, Jun Zeng
分類: eess.SY, cs.RO, cs.SY, math.OC
原文アブストラクト
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.
