论文标题
具有概率性能保证的自动控制器调整的数据驱动的方案优化
Data-Driven Scenario Optimization for Automated Controller Tuning with Probabilistic Performance Guarantees
论文作者
论文摘要
不确定性下的复杂系统的高级控制策略的系统设计和验证在很大程度上仍然是一个开放的问题。尽管对自动化控制器调整的Blackbox优化方法有望，但它们通常缺乏对解决方案质量的正式保证，这对于控制安全系统的控制尤为重要。本文着重于获得自动化控制器调整的闭环性能保证，可以将其作为不确定性下的黑盒优化问题配制。我们使用非凸情景理论的最新进展来提供有关闭环性能度量概率的无分配结合。为了减轻数据驱动的方案优化方法的计算复杂性，我们将自己限制为一组离散的候选调整参数。我们建议使用受约束的贝叶斯优化从不同的随机种子点多次生成这些候选物。我们将提出的方法用于调整由七个高度非线性微分方程建模的半匹配反应器的经济非线性模型预测控制器。
Systematic design and verification of advanced control strategies for complex systems under uncertainty largely remains an open problem. Despite the promise of blackbox optimization methods for automated controller tuning, they generally lack formal guarantees on the solution quality, which is especially important in the control of safety-critical systems. This paper focuses on obtaining closed-loop performance guarantees for automated controller tuning, which can be formulated as a black-box optimization problem under uncertainty. We use recent advances in non-convex scenario theory to provide a distribution-free bound on the probability of the closed-loop performance measures. To mitigate the computational complexity of the data-driven scenario optimization method, we restrict ourselves to a discrete set of candidate tuning parameters. We propose to generate these candidates using constrained Bayesian optimization run multiple times from different random seed points. We apply the proposed method for tuning an economic nonlinear model predictive controller for a semibatch reactor modeled by seven highly nonlinear differential equations.
