Data-based predictive hybrid driven control for a class of imperfect networked systems

Abstract

A data-based predictive hybrid driven control (DPHDC) approach is presented for a class of networked systems compromising both computation and communication delays, packet dropouts and disturbances. First, network problems are classified in a generic way which is used to design a network problem detector (NPD) capable of detecting online current delays and packet dropouts. Second, a single-variable first-order proportional-integral (PI) -based adaptive grey model (PIAGM(1,1)) is designed to predict future network problems and, to predict system disturbances. Third, a hybrid driven scheme integrated an optimal small buffer (OSB) is constructed to allow the system to operate without any interrupts due to large delays or packet dropouts. Furthermore, the OSB size is online optimized using adaptive grey fuzzy cognitive map technique. Forth, a prediction-based model-free adaptive controller (PMFAC) is developed to compensate for network problems. The DPHDC stability is theoretically proved while its effectiveness is demonstrated through a case study