Linear and Adaptive Controller Designs from Plant Data

Abstract

In this thesis, several data-based linear and adaptive control strategies have been developed using the Virtual Reference Feedback Tuning (VRFT) method and the Just-in-Time Learning (JITL) technique, respectively. The main contributions of this thesis are as follows. Firstly, by extending the VRFT design framework to the continuous time systems, a direct PID design method is developed by using the process data available from open-loop tests, without resorting to the availability of a process model. Next, a one-step data-based IMC design method is developed through the enhancement of VRFT-based IMC design by three correlation equations obtained from extensive simulation studies, in which the IMC model and controller are designed simultaneously. Furthermore, an enhanced VRFT (EVRFT) method is proposed with its application to an adaptive PID controller design. In the EVRFT design, a second-order reference model is employed, and the controller parameters are obtained by updating the database and the reference model parameter at each sampling instance to improve the resulting control performance. Moreover, by incorporating the JITL technique into controller design, a self-tuning decentralized PID controller design method for multivariable system is developed. The PID parameters are updated based on not only the information provided by the JITL, but also an updating algorithm derived by the Lyapunov method. Finally, a new adaptive PID controller design method is developed by utilizing the JITL technique directly, without resorting to the common use of JITL as an estimator for process dynamics. Simulation results are presented to demonstrate that the proposed control strategies give comparable or better performance than their respective conventional counterparts.