Control Architecture for Flexible Production Systems

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

• 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

• 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

• 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

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VI Application (pg 311-312)

• Example Applications

• 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

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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