Data-based PID controller designs for nonlinear systems

Abstract

In this thesis, the data-based controller designs for nonlinear process are developed. Firstly, an adaptive PID control scheme is proposed in the fuzzy neural network modeling framework. By utilizing Lyapunov method, an updating algorithm is derived to adjust the PID parameters to guarantee the convergence of the predicted tracking error. Next, a self-tuning PID controller design is designed based on the JITL modeling technique. This proposed design method exploit the current process information from controller database and modeling database to realize on-line tuning of PID parameters. The controller database is constructed to store the PID parameters with their corresponding information vector, and the modeling database is employed for the standard use by JITL for the modeling purpose. The PID parameters are obtained from controller database and can be updated during on-line implementation.Simulation results are presented to demonstrate that the proposed control strategies give better performances than their conventional counterpart.