Robust Model Predictive Path Integral Control: Analysis and Performance Guarantees
Robust Model Predictive Path Integral Control: Analysis and Performance Guarantees
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DOI：
10.1109/lra.2021.3057563
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发表时间：
2021-04-01
影响因子：
5.2
通讯作者：
Theodorou, Evangelos A.
中科院分区：
文献类型：
--
作者：
Gandhi, Manan S.;Vlahov, Bogdan;Theodorou, Evangelos A.
在这篇文章中，我们提出了一种新的鲁棒模型预测路径积分控制（RMPPI）的决策体系结构，并研究了它的性能保证和对越野导航的适用性。所提出的体系结构的关键构建块是由标称和实际动态组成的系统的增强状态空间表示，不同类型跟踪控制器的占位符，标称状态传播的安全逻辑，以及考虑到底层跟踪控制能力的重要采样方案。利用这些成分，我们得到了动态系统自由能增长的一个函数，它是任务约束满意度、底层跟踪控制器的性能和RMPPI中使用的随机优化的抽样误差的函数。为了验证自由能增长的界限，我们使用两种类型的跟踪控制器进行了仿真实验，即迭代线性二次高斯和基于收缩-度量的控制。我们用GT自动驾驶汽车进一步证明了RMPPI在实际硬件中的适用性。我们的实验表明，通过缓解上述模型预测控制器缺乏鲁棒性或过度保守性的问题，RMPPI优于MPPI和Tube-MPPI。RMPPI在灵活性和对干扰的鲁棒性方面提供了两个世界中最好的。
In this letter we propose a novel decision making architecture for Robust Model-Predictive Path Integral Control (RMPPI) and investigate its performance guarantees and applicability to off-road navigation. Key building blocks of the proposed architecture are an augmented state space representation of the system consisting of nominal and actual dynamics, a placeholder for different types of tracking controllers, a safety logic for nominal state propagation, and an importance sampling scheme that takes into account the capabilities of the underlying tracking control. Using these ingredients, we derive a hound on the free energy growth of the dynamical system which is a function of task constraint satisfaction level, the performance of the underlying tracking controller, and the sampling error of the stochastic optimization used within RMPPI. To validate the bound on free energy growth, we perform experiments in simulation using two types of tracking controllers, namely the iterative Linear Quadratic Gaussian and Contraction-Metric based control. We further demonstrate the applicability of RMPPI in real hardware using the GT AutoRally vehicle. Our experiments demonstrate that RMPPI outperforms MPPI and Tube-MPPI by alleviating issues of the aforementioned model predictive controllers related to either lack of robustness or excessive conservatism. RMPPI provides the best of the two worlds in terms of agility and robustness to disturbances.