Event‐triggered data‐ and knowledge‐driven adaptive quality iterative learning control with uncertainty for a pharmaceutical cyber‐physical system

Abstract

AbstractIn the context of Pharma 4.0, a cyber‐physical systems (CPSs)‐based pharmaceutical quality control (PQC) mode holds a critical position in ensuring the quality of drug products. This paper is concerned with a PQC problem with uncertainty embodied in ever‐changing critical material attributes, which present new challenges related to costs and efficiency during pharmaceutical development. So, an event‐triggered data‐ and knowledge‐driven adaptive PQC framework is proposed. First, a data‐ and knowledge‐driven adaptive iterative learning control‐based PQC scheme is developed with the assistance of process knowledge that also contains much additional information reflecting the laws and trends governing process operations. Second, an event‐triggering condition suitable for the PQC tasks is designed and embedded in the controller design to reduce some unnecessary computing and communication loads. Furthermore, the integration of process data and knowledge is used for the adaptive adjustment of control parameters and the determination of initial control directions. Finally, several data experiments illustrate the effectiveness of the proposed methods.