Control Architecture for Flexible Production Systems Bengt Lennartson, Martin Fabian, Petter Falkman 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

I – Background and Strategy (pg307) Flexible Production System I – Background and Strategy (pg307) 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 Desired Capabilities Highly flexible resources Robots Machine tools Humans Non flexible resources Software Controller hardware “Generic system architecture” Create one model that can be applied to all processes and then optimize the model Parallel Execution Benefits Diagnostics Information Handling Optimization Verification

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

III – Resources (pg308-309) 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

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

V – Controller (pg 310-311) 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

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

VII – Conclusions 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