具有碰撞约束的控制工程整定问题的贝叶斯优化和元分析
Benchmark of Bayesian Optimization and Metaheuristics for Control Engineering Tuning Problems with Crash Constraints
摘要：
基于黑盒优化的控制器调整允许自动调整性能关键参数，大部分是任意的高级闭环控制目标。然而，一个全面的基准测试不同的黑盒优化控制工程问题尚未进行。因此，在这方面的贡献，11个不同版本的贝叶斯优化（BO）进行了比较，7元和其他基线上的一组10个确定性模拟单目标调整控制问题。结果表明，确定性噪声，低多模态，和大量的领域与不可行的参数化（碰撞约束）表征控制工程调整问题。因此，一个灵活的方法来处理碰撞约束与BO。与标准BO相比，显示了样品效率的提高。此外，基准测试结果表明，模式搜索（PS）的预算25 d的目标函数的评估和问题的维数d = 2的表现最好。贝叶斯自适应直接搜索，BO和PS的组合，被证明是最有效的样本为3 <= d <= 5。使用这些优化器而不是随机搜索，控制器性能平均提高6.6%，最高可达16.1%。
英文摘要：
Controller tuning based on black-box optimization allows to automatically tune performance-critical parameters w.r.t. mostly arbitrary high-level closed-loop control objectives. However, a comprehensive benchmark of different black-box optimizers for control engineering problems has not yet been conducted. Therefore, in this contribution, 11 different versions of Bayesian optimization (BO) are compared with seven metaheuristics and other baselines on a set of ten deterministic simulative single-objective tuning problems in control. Results indicate that deterministic noise, low multimodality, and substantial areas with infeasible parametrizations (crash constraints) characterize control engineering tuning problems. Therefore, a flexible method to handle crash constraints with BO is presented. A resulting increase in sample efficiency is shown in comparison to standard BO. Furthermore, benchmark results indicate that pattern search (PS) performs best on a budget of 25 d objective function evaluations and a problem dimensionality d of d = 2. Bayesian adaptive direct search, a combination of BO and PS, is shown to be most sample efficient for 3 <= d <= 5. Using these optimizers instead of random search increases controller performance by on average 6.6% and up to 16.1%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
