Using Pattern Recognition in Controller Adaptation and Performance Evaluation

Abstract

This work presents pattern recognition-based methods for controller adaptation and performance evaluation. These methods comprise a passive model-based adaptive control algorithm that is simple to use, easy to understand, stable, and fairly robust in a wide variety of applications. Controller adaptation in this work uses excitation diagnostics to initiate batch-wise regression of a process model to dynamic closed-loop process data. The process model is then employed in model-based controller tuning relations to update the controller's character. Controller performance evaluation is used to determine appropriate adjustments to the tuning relations such that an accurate process model will produce desired controller performance. These adaptive techniques are implemented using vector quantizing neural networks as efficient pattern recognition tools. The adaptive algorithm is presented in a structure that allows for the implementation of these advanced techniques without requiring the replacement of an existing feedback controller. This is demonstrated using a simulated nonlinear third order process and an IMC tuned PI controller with Smith Predictor.