Adaptive inferential control for chemical processes

Abstract A SISO adaptive inferential control system is proposed to deal effectively with many real process control problems characterized by unmeasured disturbances, nonlinearities and/or time-varying process parameters, the proposed control system couples the basic structure of an inferential control (internal model control) with an on-line system identification method for estimation of the process model parameters and optimal adjustment of the controller. To obtain a stable approximation to the inverse of the process model as the controller, the use of adaptive inverse modeling based on a discrete convolution model (adaptive deconvolution model) is examined with two different process models, transfer function and convolution models. Simulation results of two numerical examples are presented to illustrate and compare the performance of the proposed adaptive inferential control system with those of conventional PID control and inferential control. The proposed control system is shown to perform well with both minimum and nonminimum phase systems in the presence of unmeasured load changes and dead time variations. In addition, Vogel-Edgar's adaptive dead time compensator and its modified controller (pole-zero placement adaptive controller) for discrete convolution models are analyzed from the viewpoint of adaptive inferential control.