论文标题
配置：对闭环控制系统优化的有效有效的全局优化，并具有未建模的约束
CONFIG: Constrained Efficient Global Optimization for Closed-Loop Control System Optimization with Unmodeled Constraints
论文作者
论文摘要
在本文中，采用了一种简单而有效的约束全局优化算法的配置算法，用于优化具有未建模约束的未知系统的闭环控制性能。现有的基于高斯流程的闭环优化方法只能保证本地收敛（例如SafeOpt），或者根本没有已知的最佳保证（例如，预期的预期改进），而最近引入的配置算法已被证明可以享受理论的全局最佳保证。在这项研究中，我们证明了配置算法在应用中的有效性。该算法首先应用于人工数值基准问题，以证实其有效性。然后将其应用于连续搅拌坦克反应器的经典约束稳态优化问题。仿真结果表明，我们的配置算法可以通过流行的CEI（预期改进）算法实现性能竞争，该算法没有已知的最佳保证。因此，配置算法提供了一种新工具，具有可证明的全球最佳保证和竞争性的经验性能，以优化具有软限制的系统的闭环控制性能。最后但并非最不重要的一点是，开源代码可作为Python软件包可用，以促进未来的应用程序。
In this paper, the CONFIG algorithm, a simple and provably efficient constrained global optimization algorithm, is applied to optimize the closed-loop control performance of an unknown system with unmodeled constraints. Existing Gaussian process based closed-loop optimization methods, either can only guarantee local convergence (e.g., SafeOPT), or have no known optimality guarantee (e.g., constrained expected improvement) at all, whereas the recently introduced CONFIG algorithm has been proven to enjoy a theoretical global optimality guarantee. In this study, we demonstrate the effectiveness of CONFIG algorithm in the applications. The algorithm is first applied to an artificial numerical benchmark problem to corroborate its effectiveness. It is then applied to a classical constrained steady-state optimization problem of a continuous stirred-tank reactor. Simulation results show that our CONFIG algorithm can achieve performance competitive with the popular CEI (Constrained Expected Improvement) algorithm, which has no known optimality guarantee. As such, the CONFIG algorithm offers a new tool, with both a provable global optimality guarantee and competitive empirical performance, to optimize the closed-loop control performance for a system with soft unmodeled constraints. Last, but not least, the open-source code is available as a python package to facilitate future applications.
