论文标题
使用约束贝叶斯优化的安全意识到的级联控制器调整
Safety-Aware Cascade Controller Tuning Using Constrained Bayesian Optimization
论文作者
论文摘要
本文提出了一种基于贝叶斯优化的PID级联控制器的安全调整，以安全调整PID级联控制器增益。优化目标由数据驱动的性能指标组成，并使用高斯流程进行建模。我们进一步引入了一个数据驱动的约束，该约束捕获了系统数据的稳定性要求。数值评估表明，由于量身定制的停止标准，所提出的方法的表现优于反馈自动调整，并迅速收敛到全球最佳距离。我们在模拟和实验中演示了该方法的性能。对于实验实施，除了引入安全性约束外，我们还整合了一种自动检测关键收益的方法，并将优化目标扩展到罚款，具体取决于当前候选人的接近性，指出了关键收益。最终的自动调整方法优化了系统性能，同时确保稳定性和标准化
This paper presents an automated, model-free, data-driven method for the safe tuning of PID cascade controller gains based on Bayesian optimization. The optimization objective is composed of data-driven performance metrics and modeled using Gaussian processes. We further introduce a data-driven constraint that captures the stability requirements from system data. Numerical evaluation shows that the proposed approach outperforms relay feedback autotuning and quickly converges to the global optimum, thanks to a tailored stopping criterion. We demonstrate the performance of the method in simulations and experiments. For experimental implementation, in addition to the introduced safety constraint, we integrate a method for automatic detection of the critical gains and extend the optimization objective with a penalty depending on the proximity of the current candidate points to the critical gains. The resulting automated tuning method optimizes system performance while ensuring stability and standardization
