A Framework for Time-Consistent, Risk-Sensitive Model Predictive Control: Theory and Algorithms
A Framework for Time-Consistent, Risk-Sensitive Model Predictive Control: Theory and Algorithms
复制标题
时间一致、风险敏感的模型预测控制框架：理论和算法
DOI：
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发表时间：
2019
影响因子：
6.8
通讯作者：
M. Pavone
中科院分区：
文献类型：
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作者：
Sumeet Singh;Yinlam Chow;Anirudha Majumdar;M. Pavone
在本文中，我们提出了一个针对受随机乘性不确定性影响的线性系统的风险敏感型模型预测控制（MPC）框架。我们的关键创新在于将累积成本的时间一致动态风险评估作为要最小化的目标函数。这个框架在风险评估的时间一致性方面具有公理性依据，适用于动态优化，并且具有统一性，因为它涵盖了从风险中性（即期望）到最坏情况的全范围风险偏好。在此框架内，我们提出并分析了一种可证明具有稳定性的在线风险敏感型MPC算法。此外，通过利用时间一致动态风险度量的对偶表示，我们将MPC控制律的计算转化为一个可实时实现的凸优化问题。文中给出并讨论了仿真结果。
In this paper, we present a framework for risk-sensitive model predictive control (MPC) of linear systems affected by stochastic multiplicative uncertainty. Our key innovation is to consider a time-consistent, dynamic risk evaluation of the cumulative cost as the objective function to be minimized. This framework is axiomatically justified in terms of time-consistency of risk assessments, is amenable to dynamic optimization, and is unifying in the sense that it captures a full range of risk preferences from risk neutral (i.e., expectation) to worst case. Within this framework, we propose and analyze an online risk-sensitive MPC algorithm that is provably stabilizing. Furthermore, by exploiting the dual representation of time-consistent, dynamic risk measures, we cast the computation of the MPC control law as a convex optimization problem amenable to real-time implementation. Simulation results are presented and discussed.