Toward an Automated Learning Control Architecture for Cyber-Physical Manufacturing Systems

Abstract

Manufacturers are constantly looking to enhance the performance of their manufacturing systems by improving their ability to address disruptions and disturbances, while reducing cost and maximizing quantity and quality. Even though innovative mechanisms for adaptability and flexibility continuously contribute to the smart manufacturing evolutionary process, they generally stop short of providing a capability for coordinated on-line learning. This is especially true when that learning requires exploration outside of established operational boundaries or uses artificial intelligence (as opposed to purely human intelligence) as part of the dynamic implementation of learning. In this work, we provide a vision for the development of an automated learning control architecture to extend the adaptability and flexibility capabilities of manufacturing systems. As part of this vision, we describe a set of requirements and objectives that, if addressed, provide an environment to allow distributed and automated learning across the manufacturing ecosystem. We then provide an example communication and control architecture that meets these requirements and objectives by gathering information, building a dynamic knowledge base, distributing intelligence, making decisions, and adapting the control commands sent to the equipment and people across the manufacturing ecosystem. The example architecture leverages both centralized and distributed control strategies and the ability to switch between the strategies to gather and learn from information in the system. Example case studies are provided illustrating how this architecture can be used to improve manufacturing system performance.

Controller-Aware Dynamic Network Management for Industry 4.0

“…3). Similar settings are given in [14], [19]. The DTs monitor the process outputs of the physical resources and apply control inputs using the system dynamics and control models of the resources.…”

“…Efficient network resource allocation among service-critical physical components is a challenging problem in a CPMS scenario, due to the time-varying resource requirements that are sensitive to the communication and computation needs [7]. Novel networking features and capabilities have led to the development of more advanced and reconfigurable resource allocation architectures [13], [14]. Learning and AI methods, softwaredefined resource orchestration, and virtual sub-networks have been used for efficient resource allocation in the automation and control of CPMS [14], [15], [16].…”

“…Novel networking features and capabilities have led to the development of more advanced and reconfigurable resource allocation architectures [13], [14]. Learning and AI methods, softwaredefined resource orchestration, and virtual sub-networks have been used for efficient resource allocation in the automation and control of CPMS [14], [15], [16]. Hierarchical resource allocation mechanisms are popular for resource provisioning in industrial plants due to their flexibility in deployment and capability of being implemented in distributed fashion [17].…”

Controller-Aware Dynamic Network Management for Industry 4.0

“…Efficient network resource allocation among service-critical physical components is a challenging problem in a CPMS scenario, due to the time-varying resource requirements that are sensitive to the communication and computation needs [11]. The novel networking features and capabilities have led to the development of more advanced and reconfigurable resource allocation architectures [12], [13], [14], [15]. Learning and AI methods, software-defined resource orchestration, and virtual sub-networks have been proposed for automation and control of CPMS with efficient resource allocation [15], [16], [17].…”

“…The novel networking features and capabilities have led to the development of more advanced and reconfigurable resource allocation architectures [12], [13], [14], [15]. Learning and AI methods, software-defined resource orchestration, and virtual sub-networks have been proposed for automation and control of CPMS with efficient resource allocation [15], [16], [17]. Hierarchical resource allocation mechanisms are popular for resource provisioning in industrial plants due to their flexibility in deployment and capability of being implemented in distributed fashion [18], [19].…”