Adversarially robust Bayesian optimization for efficient auto‐tuning of generic control structures under uncertainty
Adversarially robust Bayesian optimization for efficient auto‐tuning of generic control structures under uncertainty
复制标题
对抗性鲁棒贝叶斯优化，可在不确定性下实现通用控制结构的高效自动调整
DOI：
10.1002/aic.17591
复制
发表时间：
2022
期刊：
影响因子：
3.7
通讯作者：
Mesbah, Ali
中科院分区：
文献类型：
--
作者：
Paulson, Joel A.;Makrygiorgos, Georgios;Mesbah, Ali
基于优化和学习的控制器的性能关键取决于几个调整参数的选择，这些参数可以以高度非线性和非凸的方式影响闭环控制性能和约束满足。由于调节参数和一般闭环性能测量之间关系的黑箱性质，人们对使用无导数优化方法（包括可以处理昂贵的未知成本函数的贝叶斯优化（BO））的复杂控制结构的自动校准（即自动调节）产生了浓厚的兴趣。然而，将 BO 应用于自动调节时，一个公开的挑战是如何有效地处理闭环系统中不能归因于集总小尺度噪声项的不确定性。本文通过开发一种对抗性鲁棒 BO (ARBO) 方法来解决这一挑战，该方法特别适合于用于闭环仿真的昂贵系统模型中具有显着时不变不确定性的自动调整问题。 ARBO 依赖于高斯过程模型，该模型共同描述了调节参数和不确定性对闭环性能的影响。从这个联合高斯过程模型中，ARBO 使用交替置信约束程序来同时选择下一个候选调整和不确定性实现，这意味着每次迭代只需要一次昂贵的闭环模拟。 ARBO 的优点在两个案例研究中得到了证明，包括说明性问题和使用基准生物反应器问题自动调整非线性模型预测控制器。
The performance of optimization‐ and learning‐based controllers critically depends on the selection of several tuning parameters that can affect the closed‐loop control performance and constraint satisfaction in highly nonlinear and nonconvex ways. Due to the black‐box nature of the relationship between tuning parameters and general closed‐loop performance measures, there has been a significant interest in automatic calibration (i.e., auto‐tuning) of complex control structures using derivative‐free optimization methods, including Bayesian optimization (BO) that can handle expensive unknown cost functions. Nevertheless, an open challenge when applying BO to auto‐tuning is how to effectively deal with uncertainties in the closed‐loop system that cannot be attributed to a lumped, small‐scale noise term. This article addresses this challenge by developing an adversarially robust BO (ARBO) method that is particularly suited to auto‐tuning problems with significant time‐invariant uncertainties in an expensive system model used for closed‐loop simulations. ARBO relies on a Gaussian process model that jointly describes the effect of the tuning parameters and uncertainties on the closed‐loop performance. From this joint Gaussian process model, ARBO uses an alternating confidence‐bound procedure to simultaneously select the next candidate tuning and uncertainty realizations, implying only one expensive closed‐loop simulation is needed at each iteration. The advantages of ARBO are demonstrated on two case studies, including an illustrative problem and auto‐tuning of a nonlinear model predictive controller using a benchmark bioreactor problem.