Girbés-Juan Vicent, Moll Joaquín, Sala Antonio, Armesto Leopoldo
Departament d'Enginyeria Electrònica (DIE), Universitat de València, 46100 Burjassot, Spain.
Instituto U. de Automática e Informática Industrial (ai2), Universitat Politècnica de Valencia, 46022 Valencia, Spain.
Sensors (Basel). 2023 Aug 18;23(16):7266. doi: 10.3390/s23167266.
In this paper, a procedure for experimental optimization under safety constraints, to be denoted as constraint-aware Bayesian Optimization, is presented. The basic ingredients are a performance objective function and a constraint function; both of them will be modeled as Gaussian processes. We incorporate a prior model (transfer learning) used for the mean of the Gaussian processes, a semi-parametric Kernel, and acquisition function optimization under chance-constrained requirements. In this way, experimental fine-tuning of a performance objective under experiment-model mismatch can be safely carried out. The methodology is illustrated in a case study on a line-follower application in a CoppeliaSim environment.
本文提出了一种在安全约束下进行实验优化的方法，称为约束感知贝叶斯优化。其基本要素是性能目标函数和约束函数；两者都将被建模为高斯过程。我们纳入了一个用于高斯过程均值的先验模型（迁移学习）、一个半参数核以及在机会约束要求下的采集函数优化。通过这种方式，可以在实验模型不匹配的情况下安全地对性能目标进行实验微调。该方法在CoppeliaSim环境中的线跟随应用案例研究中得到了说明。
