In this contribution, problems related to fault detection filter (FDF) design for uncertain discrete-time systems with both modelling errors and unknown input are studied.
In this contribution, problems related to the integrated design of observer-based robust H∞ controller and robust fault detection (RFD) system is studied for uncertain LTI systems with both modelling errors and unknown input.
First, the causes of SOC errors, for example, modelling error and uncertain initial error, are studied.
When we forecast the power load, the forecasting error mainly consists of two parts: the modelling error and the random error [40].
When the system starts, the maximal relative error is high because the system is hardly heated up (exact values close to zero) and the thermal parts are dominated by modelling error and numerical error.
In the ideal case where there is no modelling error and the signal for parameter learning is persistently exciting, perfect tracking is ensured.
These different methods comprise linear and nonlinear filters, neural networks and unknown input Kalman filters, that can achieve disturbance signal de-coupling and robustness properties with respect to modelling error and sensor measurement noise.
These different methods comprise linear and nonlinear filters, neural networks and unknown input Kaiman filters, that can achieve disturbance signal de-coupling and robustness properties with respect to modelling error and sensor measurement noise.
Mathematical modelling and updating of damped dynamic systems that involve some modelling errors and subsequent analysis based on those errors will lead to inaccuracy in the results.
The basic idea of our study is to use an optimal residual generator (assuming no modelling errors) as the reference residual model of the robust fault detection filter design for uncertain LTI systems with modelling errors and, based on it, to formulate the robust fault detection filter design as an H∞ model-matching problem.
