This paper is concerned with the modeling and control of processes with inp
ut dynamic nonlinearity. Rather than modeling the overall process with a no
nlinear model, it is proposed to represent the process by a composite model
of a linear model (LM) and a feedforward neural network (FNN). The LM is t
o capture the dominant linear dynamics, while the FNN is to approximate the
remaining nonlinear dynamics. The controller, in correspondence, consists
of two sub-controllers: a linear predictive controller (LPC) designed based
on the LM, and an iterative inversion controller (IIC) designed based on t
he FNN. These two sub-controllers work together in a cascade fashion that t
he LPC computes the desired reference input to the IIC via an analytic pred
ictive control algorithm and the IIC then determines the process manipulate
d variable. Since the neural network is used to model the nonlinear dynamic
s only, not the overall process, a relatively small sized network is requir
ed, thus reducing computational requirement. The combination of linear and
nonlinear controls results in a simple and effective controller for a class
of nonlinear processes, as illustrated by the simulations in this paper. (
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