1. 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

2. 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

3. II – Control Architecture for FPS (pg307-308)
Generic System Architecture
II – Control Architecture for FPS (pg )
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

4. 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

5. 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

6. 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

7. 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

8. 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