The pervasiveness of PID control in process industries stipulates the critical need for efficient autotuning techniques. Recently, the use of Bayesian optimization (BO) has been popularized to seek optimal PID parameters and automate the tuning procedure. To evaluate the overall risk-averse performance of PID controllers, scenario programming that considers a wide range of uncertain scenarios provides a systematic method, but induces extensive simulations and expensive computations. Parallel computing offers a viable method to address this issue, and thus we propose a novel parallel BO algorithm for the risk-averse tuning, which enjoys a higher efficiency in both surrogate modeling and surrogate optimization. For the latter, a multiacquisition-function strategy with diversity promotion is developed to generate widely scattered query points to parallelize experiments efficiently. For the former, a data-efficient stability-aware Gaussian process modeling strategy is designed, obviating the need for building an additional classifier as required by existing methods. Numerical examples and application to a real-world industrial bio-oil processing unit demonstrate that the proposed parallel BO algorithm considerably improves the efficiency of simulation-aided PID tuning and yields practically viable controller parameters under the risk-averse tuning framework.

中文翻译：

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基于情景规划和并行贝叶斯优化的风险规避 PID 整定


PID 控制在过程工业中的普遍性决定了对高效自整定技术的迫切需求。最近，贝叶斯优化 （BO） 的使用已得到普及，以寻求最佳 PID 参数并自动执行整定过程。为了评估 PID 控制器的整体风险规避性能，考虑各种不确定情景的情景规划提供了一种系统的方法，但会引起广泛的模拟和昂贵的计算。并行计算为解决这个问题提供了一种可行的方法，因此我们提出了一种新的并行 BO 算法进行风险规避调整，该算法在代理建模和代理优化方面都享有更高的效率。对于后者，开发了一种具有多样性提升的多采集函数策略，以生成广泛分散的查询点，以有效地并行化实验。对于前者，设计了一种数据高效的稳定性感知高斯过程建模策略，无需按照现有方法的要求构建额外的分类器。数值示例和在实际工业生物油处理单元中的应用表明，所提出的并行 BO 算法大大提高了仿真辅助 PID 整定的效率，并在风险规避整定框架下产生了实际可行的控制器参数。