Applying neural networks to on-line updated PID controllers for nonlinear process control

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.