In the industrial field, production processes are becoming more complex and the requirements of the stability and reliability of operation and the quality of products are becoming stricter. Therefore, the study on fault diagnosis and estimation is of great significance and research on it is widely conducted.
There have been many studies on fault estimation using estimation of unknown input signals, and there are several such methods including augmented state estimation with unknown input. Such approaches are of practical significance because they can be effectively applied to fault diagnosis and fault tolerant control. Usually, fault estimation is carried out through three steps; fault detection, isolation and estimation. Only when the information on the size of fault is available, effective fault-tolerant control is possible
Jang Myong Jun, a researcher at the Faculty of Automation Engineering, has studied the robust fault estimation method for nonlinear discrete time systems by combining the general unknown input observation method and H∞ attenuation method, and proposed an observer algorithm guaranteeing the convergence of the observer and the required disturbance attenuation with respect to fault estimation error. In addition, he has solved fault estimation problems using linear matrix inequality (LMI), thus reducing calculation quantities.
The simulation results for a multi-tank system show that the proposed approach is effective.
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