Disturbance classification and rejection using pattern recognition methods

The design of disturbance classification and accommodation techniques for complex chemical systems is generating considerable research interest. In this work, we propose to employ a pattern recognition method, viz., the fuzzy clustering, to classify disturbances. Various operating modes of the plant, including normal and anticipated/measured fault modes, are represented as clusters in a suitable dynamic clustering space. The key idea is to classify any unmeasured disturbance in terms of nominal and known disturbance modes by assigning the appropriate membership to these known clusters. For the rejection of the unmeasured disturbances, the deployment of composite controllers that are built by fuzzy aggregation of the individual controller actions is proposed. Validation results using closed-loop simulation involving a simple illustrative single-input-single-output control loop and also a nonlinear, multivariable continuous stirred tank reactor are presented to demonstrate the validity of the methodology in classification and accommodation of unmeasured steplike disturbances.