A Process Knowledge-Based Controller for Maneuverability Improvement of a Nonlinear Industrial Process

This paper is concerned with the formulation of a process knowledge based controller (PKBC) for maneuverability improvement of nonlinear processes operation. The capacity for empirical knowledge acquisition from artificial intelligence systems was utilized in the development of the strategy. The PKBC is a neuro-fuzzy system obtained from process data. The GT 5001 type is the selected nonlinear process, for speed control during startup operation, where the GT has to follow a specific speed path that imposes tight regulation requirements for the control system, including fast response and precision. The proposed control strategy is a feedforwardfeedback one. In the feedback path a PID controller is used. In the feedforward path a PKBC provides most of the control signal for wide-range operation, diminishing the control effort on the PID controller. Simulation tests were carried on a dynamic mathematical model of the GT, and demonstrate the maneuverability improvement concerning the startup speed response.