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 non-linear 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 feedforward-feedback 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.
KeywordsFuzzy System Feedforward Control Artificial Intelligence System Inlet Guide Vane Dynamic Mathematical Model
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