A model-based reinforcement learning approach for maintenance optimization of degrading systems in a large state space

2021 ◽  
Vol 161 ◽  
pp. 107622
Author(s):  
Ping Zhang ◽  
Xiaoyan Zhu ◽  
Min Xie
2011 ◽  
Vol 52 (4) ◽  
pp. 372-390
Author(s):  
DUNG TIEN NGUYEN ◽  
XUERONG MAO ◽  
G. YIN ◽  
CHENGGUI YUAN

AbstractThis paper considers singular systems that involve both continuous dynamics and discrete events with the coefficients being modulated by a continuous-time Markov chain. The underlying systems have two distinct characteristics. First, the systems are singular, that is, characterized by a singular coefficient matrix. Second, the Markov chain of the modulating force has a large state space. We focus on stability of such hybrid singular systems. To carry out the analysis, we use a two-time-scale formulation, which is based on the rationale that, in a large-scale system, not all components or subsystems change at the same speed. To highlight the different rates of variation, we introduce a small parameter ε>0. Under suitable conditions, the system has a limit. We then use a perturbed Lyapunov function argument to show that if the limit system is stable then so is the original system in a suitable sense for ε small enough. This result presents a perspective on reduction of complexity from a stability point of view.


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