Abstract
Controller tuning and parameter optimization are crucial in system design to improve closed-loop system performance. Bayesian optimization has been established as an efficient model-free controller tuning and adaptation method. However, Bayesian optimization methods are computationally expensive and therefore difficult to use in real-time critical scenarios. In this work, we propose a real-time purely data-driven, model-free approach for adaptive control, by online tuning low-level controller parameters. We base our algorithm on GoOSE, an algorithm for safe and sample-efficient Bayesian optimization, for handling performance and stability criteria. We introduce multiple computational and algorithmic modifications for computational efficiency and parallelization of optimization steps. We further evaluate the algorithm's performance on a real precision-motion system utilized in semiconductor industry applications by modifying the payload and reference stepsize and comparing it to an interpolated constrained optimization-based baseline approach.
Abstract (translated)
控制器调整和参数优化在系统设计中至关重要，以提高闭环系统的性能。贝叶斯优化被证明是一种有效的模型无关控制器调整和适应方法。然而，贝叶斯优化方法在实时关键场景中计算代价较高，因此难以使用。在这项工作中，我们提出了一种实时的数据驱动、模型无关的自适应控制方法，通过在线调整低级控制器参数来实现。我们基于GoOSE算法，该算法用于安全且具有采样效率的贝叶斯优化，处理性能和稳定性标准。我们引入了多个计算和算法修改以提高计算效率和优化步骤的并行化。我们进一步通过修改负载和参考步长并将其与拟合约束优化基于基准方法进行比较，评估算法的性能在用于半导体工业应用的实时精度运动系统上。
URL
https://arxiv.org/abs/2404.14602
