论文标题
使用约束贝叶斯优化的数据驱动的MPC的数据驱动自动调整方法
A Data-Driven Automatic Tuning Method for MPC under Uncertainty using Constrained Bayesian Optimization
论文作者
论文摘要
模型预测控制器（MPC）的闭环性能对预测模型，控制器公式和调整参数的选择敏感。但是，通常优化预测模型以预测准确性而不是性能，并且通常手动进行MPC调整以满足（概率）约束。在这项工作中，我们展示了一种一般方法，用于自动化不确定性下MPC的调整。特别是，我们将自动调整问题提出作为约束的黑盒优化问题，可以通过无衍生化的优化解决。我们依靠贝叶斯优化的受约束变体（BO）来解决可以直接处理嘈杂且昂贵的评估功能的MPC调整问题。在连续搅拌的储罐反应器示例中，证明了所提出的自动调整方法的好处。
The closed-loop performance of model predictive controllers (MPCs) is sensitive to the choice of prediction models, controller formulation, and tuning parameters. However, prediction models are typically optimized for prediction accuracy instead of performance, and MPC tuning is typically done manually to satisfy (probabilistic) constraints. In this work, we demonstrate a general approach for automating the tuning of MPC under uncertainty. In particular, we formulate the automated tuning problem as a constrained black-box optimization problem that can be tackled with derivative-free optimization. We rely on a constrained variant of Bayesian optimization (BO) to solve the MPC tuning problem that can directly handle noisy and expensive-to-evaluate functions. The benefits of the proposed automated tuning approach are demonstrated on a benchmark continuously stirred tank reactor example.
