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Override Control Based on NARX Model for Ecuador’s Oil Pipeline System

Abstract:

Override control systems based on Adaptive Neuro-Fuzzy Inference System (ANFIS) allows to optimize the work of a process by series of pbkp_redictions of its possible inputs and outputs. It is fully compatible with linear and non-linear systems, but the requirement of a controller with the enough processing capabilities to ensure that all the involved operations are done in real time. For extraction of the dynamic properties and identification of a plant model this research work uses a Nonlinear Autoregressive Exogenous (NARX) neural networks. With this model the authors develop the simulation of the Neuro-Fuzzy Controllers trained by ANFIS, which are part of an override control structure for an Oil & Gas pipeline process. Finally, the behavior of neuro-fuzzy controllers is analyzed by effects of disturbances and human-errors because of the non-correlation in the setpoints of the main variables.