Improving Model Predictive Path Integral using Covariance Steering

Improving Model Predictive Path Integral using Covariance Steering
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使用协方差引导改进模型预测路径积分

DOI:
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发表时间:
2021
期刊:
arXiv.org
影响因子:
--
通讯作者:
P. Tsiotras
P. Tsiotras
中科院分区:
--
文献类型:
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作者:
Ji Yin;Zhiyuan Zhang;Evangelos A. Theodorou;P. Tsiotras

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- 本文提出了一种新的控制方法,自治系统在不确定性下运行。将联合收割机模型预测路径积分(MPPI)控制与协方差导引(CS)理论相结合,得到了一般非线性系统的鲁棒控制器。提出的协方差控制模型预测路径积分(CC-MPPI)控制器解决了由于意外的干扰和不确定性在一些MPPI实现中观察到的性能下降。也就是说,在环境变化太快或MPPI推出期间的模拟动态没有捕获实际动态中的噪声和不确定性的情况下,基线MPPI实现可能导致发散。所提出的CC-MPPI控制器通过在预测范围的末端控制展开轨迹的分散来避免发散。此外,CC-MPPI具有可调节的轨迹采样分布,可以根据环境进行更改,以实现有效的采样。使用地面车辆在具有挑战性的环境中导航的数值例子证明了所提出的方法。
—This paper presents a novel control approach for autonomous systems operating under uncertainty. We combine Model Predictive Path Integral (MPPI) control with Covariance Steering (CS) theory to obtain a robust controller for general nonlinear systems. The proposed Covariance-Controlled Model Predictive Path Integral (CC-MPPI) controller addresses the performance degradation observed in some MPPI implementations owing to unexpected disturbances and uncertainties. Namely, in cases where the environment changes too fast or the simulated dynamics during the MPPI rollouts do not capture the noise and uncertainty in the actual dynamics, the baseline MPPI implementation may lead to divergence. The proposed CC-MPPI controller avoids divergence by controlling the dispersion of the rollout trajectories at the end of the prediction horizon. Furthermore, the CC-MPPI has adjustable trajectory sampling distributions that can be changed according to the environment to achieve efficient sampling. Numerical examples using a ground vehicle navigating in challenging environments demonstrate the proposed approach.
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