Nonlinear controller design from Plant data

In this thesis, data-based controller design methods for nonlinear process systems are developed. In the Just-in-Time learning (JITL) modeling framework, which is capable of modeling the dynamic systems with a range of operating regimes, four adaptive control strategies are proposed. These controller design methods exploit the current process information provided by the JITL to adjust the controller parameters online or calculate the control action to compensate the changes in process dynamics caused by process nonlinearity. In the Virtual Reference Feedback Tuning (VRFT) design framework, the design of feedback controller can be carried out directly based on the measured process input and output data without resorting to the identification of a process model. To extend the VRFT design to nonlinear systems, two adaptive VRFT design procedures are developed. Compared with the previous work, these adaptive control strategies can be implemented online without heavy computational burden.

Author dc:creator

• YASUKI KANSHA

• Just-in-Time Learning,Virtual Reference Feedback Tuning,LQI Controller Design,Internal Model Control,PID Controller,Generalized Predictive Control