Parameter variation estimation for nonlinear systems using a "stacked" linear model structure

To obtain optimal performance in a controlled system, it is important to have accurate knowledge of the system parameters and equilibrium points. This paper considers estimation of the parameter variations of system from their assumed values using steady-state data from selected equilibrium points. It is seen that the data from yield information about each parameter variation. This always occurs, for example, if the number of sensors is less than the number of parameters. Hence, this paper shows how the data from several equilibrium points may be fed into a Kalman filter based on a "stacked" linear model to obtain more accurate estimation of the parameter variations of the system. The results are applied to a CSTR and it is seen that the data from four appropriately chosen equilibrium points is sufficiently rich to accurately estimate the parameter variations. However, no combination of three or fewer operating points is able to perform the desired estimation.