Control systems are among the most important subsystems for their ability to undertake indispensable functions in safety-critical systems. Since many key components of such systems follow different performance degradation paths, therefore it is important to have an approach capable of correctly estimating the performance of control systems containing a variety of degraded components. One solution is to endow an existing estimation approach to equip with a capability to cope with uncertainties and inadequate system specifications. This paper presents a hybrid model capable of improving existing approaches by applying the Laplace transform to the time-varying model of the control system while taking into account the varying behaviors of components over different time slices. Reliability is estimated through an event-based Monte Carlo simulation that does not require knowledge of the exact reliability function. System reliability is improved by using the particle swarm optimization method. The method searches for the optimal parameters of the control strategy by compensating for the loss in effectiveness caused by degraded components. The proposed approach is validated through a case study conducted on a simulated cooling system. Numerical results have shown that the proposed approach is capable of improving the reliability of control systems subject to total run time constraints.
['Huadong Mo', 'Min Xie']
A Dynamic Approach to Performance Analysis and Reliability Improvement of Control Systems With Degraded Components