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Control Architecture for Flexible Production Systems

Control Architecture for Flexible Production Systems

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Control Architecture for Flexible Production Systems

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  1. Control Architecture for Flexible Production Systems BengtLennartson, Martin Fabian, PetterFalkman Automation Laboratory, Department of Signals and Systems Chalmers University of Technology Göteborg, Sweeden From the Proceedings of the 2005 IEEE International Conference on Automation Science and Engineering Presented by B. Taylor Newill 12 November 2007

  2. Flexible Production System • Easy to change production volume and flow • Easy to modify and upgrade production equipment • Hardware • Software • Simultaneously produce different products in a single production cell or unit Current Capabilities • Highly flexible resources • Robots • Machine tools • Humans • Non flexible resources • Software • Controller hardware Desired Capabilities “Generic system architecture” Create one model that can be applied to all processes and then optimize the model Parallel Execution I – Background and Strategy (pg307) Benefits • Diagnostics • Information Handling • Optimization • Verification

  3. Generic System Architecture • Production system where both hardware and software are flexible • Separation of resources – simplify handling changes to the system • Enables parallel execution • Scalable Architecture Hierarchy • Architecture applicable to all levels • Applicable throughout the lifecycle II – Control Architecture for FPS (pg307-308)

  4. Generic Resource Models (GeRMs) • Producers • Machine-tools • Tanks • Reactors • Movers • Robots • AGVs • Pipes • Pumps • Locations • Buffers Generic Message-Passing Structure (GeMPS) • State Machine Structure • Command Messages • Handshake Messages • Capabilities • Coordination III – Resources (pg308-309)

  5. The Controller • Three Controller Tasks • Supervision • Scheduling • Dispatching Supervisor Scheduler Dispatcher • Chooses which product route accesses which resource • Chooses an algorithm • Uses GeRMs to control with GeMPS • Tracks individual products • Computes the algorithm • Synchronize object utilization of common available resources • Avoid blocked states • Creates algorithms IV – Controller (pg 310-311)

  6. Example Process Tree • 5 Resource Models • E.g. Parts of a paint • 2 Product Specifications • E.g. Colors, Red and Green • bxpy = “book” resource x for product y • uxpy = “un-book” resource x for product y V – Controller (pg 310-311)

  7. Example Applications • Scania Trucks and Buses • Rear-axle manufacturing cell • Multi Purpose Batch Plants (MPBP) Complex Robot Cells State Based Control • Volvo Cars • Parallel operation lists • Boolean resources • Product flow is sequential • Often multiple robots in a single cell • Resource is physical space VI – Application (pg 311-312)

  8. Conclusions • Enables Parallel Execution • Architecture for flexible production systems • Separates resources and processes • Easier to diagnose and/or optimize systems • Create better models • Theoretically based • Parallel execution • Adaptable to environment changes • Respects life-cycle • Highly resilient to disturbances (both internal and external) • Self proclaimed efficiency exceeds Holonic, Fractal, Bionic architectures VII – Conclusions