论文标题
增强健康意识控制策略的学习方法
A Reinforcement Learning Approach to Health Aware Control Strategy
论文作者
论文摘要
健康感知控制（HAC）已成为基于系统/组件的失败预后或剩余的有用寿命（RUR）预测关键组件的预测，因此成为了控制综合的领域之一。 RUL的数学动态（过渡）模型很少可用，这一事实使RUL信息很难被纳入控制范式。本文提出了一个新颖的健康意识控制框架，该框架通过整合全球系统过渡数据（由模拟真实系统的分析模型生成）和RUL预测来学习基于加强学习的方法来学习最佳控制策略。在每个步骤中产生的规则预测被跟踪到RUL的所需值。后者集成到最大化以学习最佳控制的成本函数中。使用直流电动机和轴磨损的模拟研究了提出的方法。
Health-aware control (HAC) has emerged as one of the domains where control synthesis is sought based upon the failure prognostics of system/component or the Remaining Useful Life (RUL) predictions of critical components. The fact that mathematical dynamic (transition) models of RUL are rarely available, makes it difficult for RUL information to be incorporated into the control paradigm. A novel framework for health aware control is presented in this paper where reinforcement learning based approach is used to learn an optimal control policy in face of component degradation by integrating global system transition data (generated by an analytical model that mimics the real system) and RUL predictions. The RUL predictions generated at each step, is tracked to a desired value of RUL. The latter is integrated within a cost function which is maximized to learn the optimal control. The proposed method is studied using simulation of a DC motor and shaft wear.
