Ahmad Hussain AlBayati
Fault tolerant control (FTC), fault prognosis (FP), optimal actuator fault diagnosis observer (AFO), intelligent optimal actuator fault diagnosis observer (IAFO), sequential sampling filter (SSF)
This paper aims to bridge the gap between classic model base observer and intelligent observer based on the model base and probability distributions to monitor the plant which is affected by actuator fault and sensor noise simultaneously. The paper includes the implementing and designing of two new optimal actuator observers which also relies on two optimal theories. The first new optimal actuator fault observer is a model base observer based on Lyapunov conditions while, the novelty of the second optimal actuator observer comes from introducing a fault diagnosis algorithm based on a sequential sampling filter (SSF) as a prominent part of the algorithm to filter the diagnosed fault in using the rules of the algorithm. The filters depend on the likelihood ratio, which consists of two hypothetical distributions; the target distribution and the sampling distribution. The new intelligent observer has given rise to innovative algorithms in the field of fault detection and diagnose observer. Moreover, the algorithms verify Lyapunov conditions. In addition, to demonstrate the integrity of the observers, a comparison has been made in the presence of white sensor noise, which is the main reason for inaccurate measurement in addition to two types of actuator faults; Gaussian and non-Gaussian. The study used to verify the performance, where two physical and relative error criteria have been used to evaluate the performance. However, the simulation results appear the flexibility of the second observer to diagnose the actuator fault with sensor noise together due to the number of parameters in diagnosing rules and parameters of the two probabilities.
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