Self-healing adaptive controller for industrial asset abnormality accommodation

Abstract

A training data store may contain training data associated with monitoring node values during normal operation of an industrial asset and simulated abnormal data. An offline model tuning platform accesses the training data from normal operation of the industrial asset and the simulated abnormal data in the training data store. Based on the training data from normal operation of the industrial asset, the simulated abnormal data, an abnormal operating condition, and a constrained optimization solution, controller tuning parameters are created for at least one tuned data-driven adaptive controller such that an operating condition of the industrial asset will move from the abnormal operating condition to a normal operation condition through a stable trajectory. An online monitoring platform receives a stream of current monitoring node values and, when the abnormal operating condition is detected, utilizes the controller tuning parameters to implement the at least one tuned data-driven adaptive controller.