Improving Model Predictive Path Integral using Covariance Steering
Improving Model Predictive Path Integral using Covariance Steering
复制标题
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:
影响因子:
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通讯作者:
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
—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.
影响因子:
3
作者:
Kazuhide Okamoto;M. Goldshtein;P. Tsiotras
通讯作者:
Kazuhide Okamoto;M. Goldshtein;P. Tsiotras
影响因子:
3
作者:
Balci, Isin M.;Bakolas, Efstathios
通讯作者:
Bakolas, Efstathios
DOI:
10.1109/cdc.2017.8264189
发表时间:
2017
期刊:
Conference on Decision and Control
影响因子:
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作者:
Goldshtein, Maxim;Tsiotras, Panagiotis
通讯作者:
Tsiotras, Panagiotis