PNNL

Abstract

Through the project, a transfer-learning based optimal control methodology was developed. This can significantly reduce the time and data required for model learning and increase operation robustness and resilience via data-driven controller optimization. This methodology includes three major elements: 1) extraction of control knowledge or knowledge of tuning control parameter from a source feedback control task; 2) transferring of control knowledge from the source control task to a target control task (initialization of control parameters for the target control task); and 3) fast-adaptation of control parameters for the target task to achieve optimized performance.