论文标题
比较模型预测和强化学习方法的容错控制
Comparison of Model Predictive and Reinforcement Learning Methods for Fault Tolerant Control
论文作者
论文摘要
易于故障控制器中的理想属性是系统操作过程中系统变化的适应性。自适应控制器不需要为可能的故障列举最佳控制策略。相反，它可以实时近似一个。我们为基于层次增强学习的离散时间系统提供了两个自适应断层控制方案。在存在传感器噪声和持续故障的情况下，我们将它们的性能与模型预测控制器进行比较。在C-130平面的燃油箱模型上测试了控制器。我们的实验表明，基于增强学习的控制器比故障，部分可观察到的系统模型和变化的传感器噪声水平的模型预测控制器更强。
A desirable property in fault-tolerant controllers is adaptability to system changes as they evolve during systems operations. An adaptive controller does not require optimal control policies to be enumerated for possible faults. Instead it can approximate one in real-time. We present two adaptive fault-tolerant control schemes for a discrete time system based on hierarchical reinforcement learning. We compare their performance against a model predictive controller in presence of sensor noise and persistent faults. The controllers are tested on a fuel tank model of a C-130 plane. Our experiments demonstrate that reinforcement learning-based controllers perform more robustly than model predictive controllers under faults, partially observable system models, and varying sensor noise levels.
