Towards Self-Manageable and Adaptive Cyber-Physical Systems for Manufacturing Automation

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To remain competitive in today's global market, manufacturers require systems that are capable of quickly responding to change while maintaining stable and efficient operation. Increasingly, the industrial automation and control system is viewed as being central to achieving this goal. ******Current industrial automation and control systems are typically implemented using large, and often expensive, hardware platforms that support monolithic computer control applications. As a result, when the industrial automation and control system is installed, commissioning can take months to complete, and once the system is operational, changes are often complex and difficult. Both of these factors contribute significantly to the total cost of the project. The proposed research is intended to rectify these problems through the development of a distributed intelligent sensing and control solution that is inherently adaptable and dynamically re-configurable. This approach will take advantage of distributed artificial intelligence at the planning and control levels to achieve significantly shorter up-front commissioning times as well as significantly more responsiveness to change. These potential benefits, in combination with the current trend towards cyber-physical production through the fusion of physical and digital operations, will result in a much more attractive, low-cost automation solution for future manufacturers than current centralized solutions.******The objective of this research program is to develop innovative technologies and practical solutions to realize industrial automation systems that are both flexible (i.e., capable of reconfiguration) and responsive (i.e., capable of recovering from disturbances). More specifically, this work will build on the conceptual work on cyber-physical systems (CPS) with a focus on developing a design methodology and appropriate analysis techniques for device-level sensing and control in harsh, industrial environments (e.g., discrete-part manufacturing, chemical and process industries). Our approach focuses on design strategies to support self-management at the device level, with the specific goals of developing techniques and services for self-configuration and self-healing. The overall aim of this research is to help Canadian manufacturers remain competitive in today's global market by enabling them to respond quickly to change while maintaining stable system operation and efficient use of available resources.