Adaptive Controller Design Directly from Plant Data

Abstract

Controller design for nonlinear dynamic processes has been of great interest in the chemical industry. Various nonlinear controller design strategies have been studied in the literature. Among them, adaptive controller is a well-established solution for this issue. In this thesis, a new adaptive controller design method is proposed based on the virtual reference feedback tuning (VRFT) method which was originally developed for linear controller design. This new method is termed as enhanced VRFT (EVRFT) design to account for the difference from the linear VRFT method. In the proposed method, not only a second-order reference model is employed instead of the first-order reference model commonly used in the linear VRFT design, but also the parameters of reference model are updated at each sampling instance to ensure the adaptive nature of the design strategy. In addition, to complete the on-line adaptation process, the database is updated at each sampling instance by adding the current process data into it and a relevant dataset is selected from the current database according to the k-nearest neighborhood criterion. Two different adaptive controllers are developed implementing the EVRFT strategy, i.e. an adaptive PID controller and an adaptive Internal Model Controller. Simulation results show that both proposed controllers give improved control performance than the linear PID controller designed using VRFT method. They are also shown to be quite robust in the presence of modeling error and can tolerate reasonable process noise through simulation studies.