Abstract :
The inherent time-varying nonlinearity and complexity usually exist in chemical processes. The design of control structure should
be properly adjusted based on the current state. In this paper, an improved conventional PID control scheme using linearization
through a specified neural network is developed to control nonlinear processes. The linearization of the neural network model is
used to extract the linear model for updating the controller parameters. In the scheme of the optimal tuning PID controller, the
concept of general minimum variance and constrained criterias are also considered. In order to meet most of the practical application
problems, several variations of the proposed method, including the momentum filter, the updating criterion and the adjustment
of the step size of the control action, are presented to make the proposed algorithm more practical. To demonstrate the
potential applications of the proposed strategies, two simulation problems, including a pH neutralization and a batch reactor, are
applied.
