Pattern-based Process Characterization and Gain Scheduling for Nonlinear Chemical Processes

Abstract

Accurate characterization of process dynamics from on-line sensor data is the key issue in successful implementation of gain scheduling for controlling chemical processes. This work presents a development of pattern-based gain scheduling for process control. The approach employs process state maps constructed from windowed slices of multisensor plant trend data. Process identification is done using principles of similarity based pattern recognition. This technique provides a straightforward means to associate unique gain, integral time and/or derivative time controller settings with different states of the process. Simulation results show that better control performance may be achieved by use of gain scheduled controller as compared to the conventional fixed feedback systems.