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:这项工作引入了一个新的控制战略,名为“迭代任务迭代线性二次曲线调控” (i2LQR),目的是为动态环境中的迭代任务追求最佳性能。 提议的算法是无参考的, 并使用以往迭代的历史数据来提高自主系统的性能。 与现有的算法不同, i2LQR 以迭代方式在每次印章上以迭代方式计算最佳的解决方案, 使其非常适合在不同迭代中不断变化的制约下迭代任务。 为了评估拟议算法的性能, 我们为一项迭代任务进行数字模拟, 目的是尽量减少时间消耗。 结果表明, i2LQR 实现了作为静态环境中最先进的算法的最佳性能, 并且超越了动态环境中静态和动态障碍中最先进的算法。</s>