Pheromone Propagation Controller: The Linkage of Swarm Intelligence and Advanced Process Control

Statistical process control (SPC) is traditionally used in advanced process control (APC). However, SPC, which treats measurements as a series of isolated statistical data, employs different methods to deal with different problems. In this paper, we present a new perspective on process control, which treats the intercepts of the process in different runs as a social insect colony. Our novel algorithm, called the pheromone propagation controller (PPC), is a meta-heuristic method based on the assumption that the intercepts of the linear regression model have their own behavior and affect others nearby on different runs. The pheromone basket is an environment initially filled with intercepts, and then the "intercepts pheromones" in the basket propagate according to the modified digital pheromone infrastructure. After propagation, the intercept in the next run can be forecast by extrapolating the last two entities of the pheromone basket. Consequently, a revised process recipe can be obtained from the forecast intercepts and the linear regression model. We also propose a workable scheme for adaptively tuning the PPC propagation parameter. We discuss the PPC stability region and the strategy for tuning the propagation parameter as well as the effects of size of pheromone basket, model mismatch on the performance. Our simulation results show that the standard deviation and the mean square error for PPC, whether fixed or self-tuning, are more consistent than that of the EWMA, the predictor corrector control (PCC), and the double EWMA for five types of anthropogenic disturbance. We also examined a hybrid disturbance obtained from semiconductor fabrication. When system drifts, the PPC was superior to the other candidate controllers for all values of the PPC propagation parameters and weightings of the other controllers, whether fixed or self-tuning.