Resumen de Contribution to reliable control of dynamic systems
This thesis presents sorne contributions to the field of Health-Aware Control (HAC) of dynamic systems.
In the first part of this thesis, a review of the concepts and methodologies related to reliability versus degradation and fault tolerant control versus health-aware control is presented. Firstly, in an attempt to unify concepts, an overview of HAC, degradation, and reliability modeling including some of the most relevant theoretical and applied contributions is given.
Moreover, reliability modeling is formalized and exemplified using the structure function, Bayesian networks (BNs) and Dynamic Bayesian networks (DBNs) as modeling tools in reliability analysis. In addition, some Reliability lmportance Measures (RIMs) are presented.
In particular, this thesis develops BNs models for overall system reliability analysis through the use of Bayesian inference techniques. Bayesian networks are powerful tools in system reliability assessment due to their flexibility in modeling the reliability structure of complex systems.
For the HAC scheme implementation, this thesis presents and discusses the integration of actuators health information by means of RIMs and degradation in Model Predictive Control (MPC) and Linear Quadratic Regulator algorithms.
In the proposed strategies, the cost function parameters are tuned using RIMs. The methodology is able to avoid the occurrence of catastrophic and incipient faults by monitoring the overall system reliability.
The proposed HAC strategies are applied to a Drinking Water Network (DWN) and a multirotor UAV system. Moreover, a third approach, which uses MPC and restricts the degradation of the system components is applied to a twin rotor system.
Finally, this thesis presents and discusses two reliability interpretations. These interpretations, namely instantaneous and expected, differ in the manner how reliability is evaluated and how its evolution along time is considered. This comparison is made within a HAC framework and studies the system reliability under both approaches.
