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Using an Evolutionary Optimization approach to
tune a PID controlled robotic arm
Taylor Newill – Noesis Solutions, NA Tech...
Agenda
Optimus Overview
Integrating Scilab
Genetic optimization of a PID
1
2
3
Future Development
4
Agenda
Optimus Overview
Integrating Scilab
Genetic optimization of a PID
1
2
3
Future Development
4
Optimus: A Technology Connection Platform
Creating a repeatable,
automated process:
• Multi code,
• Multi CPU,
• Synced
Ra...
Optimus: An Open Platform
• Can drive any CLI
• Embedded as a UCI/UCA
• Externally through generic
Interfaces
• External a...
Noesis Solutions - Solutions for Engineering Optimization
… more than 100+ person-years experience in PIDO
… sales offices...
Noesis Solutions
Most extensively deployed software of its kind…
A few default interfaces
 Automated
 Multi code
 Multi CPU
 Repeatable
CFD
20 Design
parameters
FEA
Post-Processing
Integrating Scilab in
Optimus
1
Should I Automate? (XKCD 1319)
Mouse over Text:
‘Automating’ comes from the
roots ‘auto-’ meaning ‘self-’ and
‘mating’, me...
Is it worth the time? (XKCD 1205)
Integrating Scilab scripts in Otimus
• User Customizable Interface
• The UCI is easily configured
with XML-files, respecti...
Scilab UCI
The SCE file is updated with every experiment, the xcos file is imported, then the
results are extracted and th...
Evolutionary Optimization of a
PID Controlled Robotic Arm
2
Robotic Arm
• Programmable mechanical arm
• The links of of the robot are
connected by joints allowing
– rotational (angul...
PID Control (Wikipedia)
• When the robotic arm is given a target, a
PID loop is used to control the movement of
the arm re...
The Challenge
• Typically, robotic arms are tuned by tuning one
PID loop at a time and cycling through the loops
until the...
Robotic Tools for Scilab/Xcos
• From the ‘Scilab Ninja’
– Dr. Varodom Toochinda
• Beta Version
– Kinematics
– Dynamics
– P...
Evolutionary Optimization
• Start with a population
covering the design space
• Make slight changes to input
variables of ...
Scilab/XCOS Setup
Parametric Usage of the Toolbox
• Optimus will detect any
controllable inputs
• Outputs can be
extracted
– From memory
– F...
Process Integration
Automation
Optimization Strategy
DOE
RSM
GLOBAL
OPTIMIZATION
LOCAL
OPTIMIZATION
GOAL
Design of Experiments
• Individual runs took about 30
seconds
• Wanted to adequately cover
the design space
– 3 inputs, 3 ...
Response Surface Model
• 18 different RSM’s
were tested, RBF was
selected
• Lowest error, created
quickly, easily
exported
Response Surface Model
Evolutionary Optimization
• Multiple strategies
will be compared
• All run on RSM
• Results validated with
simulation
• Ob...
Comparison of Optimization Strategies
Baseline vs. Optimal
Performance Impact
Strategy Experiments Normalized
PID Error
Validated Difference
from Nominal
Nominal 0 892553
Differenti...
Resource Impact
Task Time w/o
Optimus
Time with
Optimus
Total Time Saved
Create Robotic arm model 120 min 120 min 0
Create...
Is it worth the time?
• Workflow development
took 10 minutes
• For one tuning routine
Optimus saved
– 3 hours of human tim...
With Scilab and Optimus you can
Conclusion
Save Time Consolidate Knowledge
• Drive Scilab and combine
with other tools
• A...
Questions & Answers
www.noesissolutions.com
info@noesissolutions.com
taylor.newill@noesissolutions.com
Web
E-mail
ScilabTEC 2015 - Noesis Solutions
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ScilabTEC 2015 - Noesis Solutions

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"Using an Evolutionary Optimization approach to tune a PID controlled robotic arm"
By Taylor Newill, Noesis Solutions for ScilabTEC 2015

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ScilabTEC 2015 - Noesis Solutions

  1. 1. Using an Evolutionary Optimization approach to tune a PID controlled robotic arm Taylor Newill – Noesis Solutions, NA Technical Services Silvia Poles – Noesis Solutions, Engineering Manager
  2. 2. Agenda Optimus Overview Integrating Scilab Genetic optimization of a PID 1 2 3 Future Development 4
  3. 3. Agenda Optimus Overview Integrating Scilab Genetic optimization of a PID 1 2 3 Future Development 4
  4. 4. Optimus: A Technology Connection Platform Creating a repeatable, automated process: • Multi code, • Multi CPU, • Synced Rapid identification of key design variables: • Histograms • Sobol indices, ANOVA • Taguchi Process Integration Design of Experiments • Create surrogate models from any dataset • Multiple approaches are tried and the best one can be used Response surface models Optimization • Gradient • Evolutionary • Hybrid Robust Design • Design for Six Sigma • Taguchi • FORM • FOSM Optimization & Robust Design
  5. 5. Optimus: An Open Platform • Can drive any CLI • Embedded as a UCI/UCA • Externally through generic Interfaces • External algorithms can be used with ‘User Optimization’ • Driven from simple XML script • Any code can be used, python, C, scilab, etc. Connect any Software Apply any Algorithm • Java based GUI • ‘One click’ connection with queuing systems Drive from any Environment All functionality available through python API Full API
  6. 6. Noesis Solutions - Solutions for Engineering Optimization … more than 100+ person-years experience in PIDO … sales offices across Europe, US and Asia realizing double digit profitable growth for 18+ years. A leading software & services provider A strong worldwide presence … Optimus is our only product and focus, and we partner with all major CAE and mathematical modeling vendors An independent innovation partner Noesis Solutions
  7. 7. Noesis Solutions Most extensively deployed software of its kind…
  8. 8. A few default interfaces
  9. 9.  Automated  Multi code  Multi CPU  Repeatable CFD 20 Design parameters FEA Post-Processing
  10. 10. Integrating Scilab in Optimus 1
  11. 11. Should I Automate? (XKCD 1319) Mouse over Text: ‘Automating’ comes from the roots ‘auto-’ meaning ‘self-’ and ‘mating’, meaning ‘screwing’
  12. 12. Is it worth the time? (XKCD 1205)
  13. 13. Integrating Scilab scripts in Otimus • User Customizable Interface • The UCI is easily configured with XML-files, respecting a very simple syntax • Drag and drop functionality for easy multiple disciplines
  14. 14. Scilab UCI The SCE file is updated with every experiment, the xcos file is imported, then the results are extracted and then calculations are done on the resultant curves
  15. 15. Evolutionary Optimization of a PID Controlled Robotic Arm 2
  16. 16. Robotic Arm • Programmable mechanical arm • The links of of the robot are connected by joints allowing – rotational (angular) displacement – translational (linear) displacement • The links of the robot form a kinematic chain • The terminus of the kinematic chain is the end effector
  17. 17. PID Control (Wikipedia) • When the robotic arm is given a target, a PID loop is used to control the movement of the arm relative to the target • PID is a proportional-integral-derivative controller • It is a control loop feedback mechanisms • A PID controller calculates an error value as the difference between a measured process variable and a desired target • The controller attempts to minimize the error by adjusting the process through use of a manipulated variable.
  18. 18. The Challenge • Typically, robotic arms are tuned by tuning one PID loop at a time and cycling through the loops until the overall behavior is satisfactory. • This process can be time consuming and is not guaranteed to converge to the best overall tuning. • In this example we will automate the tuning of 1 PID, multiple PID’s have since been added
  19. 19. Robotic Tools for Scilab/Xcos • From the ‘Scilab Ninja’ – Dr. Varodom Toochinda • Beta Version – Kinematics – Dynamics – Path generation – Control
  20. 20. Evolutionary Optimization • Start with a population covering the design space • Make slight changes to input variables of the best performing experiments • Make a new population based on the best performing experiments
  21. 21. Scilab/XCOS Setup
  22. 22. Parametric Usage of the Toolbox • Optimus will detect any controllable inputs • Outputs can be extracted – From memory – From output files – From response variables
  23. 23. Process Integration
  24. 24. Automation
  25. 25. Optimization Strategy DOE RSM GLOBAL OPTIMIZATION LOCAL OPTIMIZATION GOAL
  26. 26. Design of Experiments • Individual runs took about 30 seconds • Wanted to adequately cover the design space – 3 inputs, 3 responses – 81 experiments tested • Latin Hypercube DOE was used
  27. 27. Response Surface Model • 18 different RSM’s were tested, RBF was selected • Lowest error, created quickly, easily exported
  28. 28. Response Surface Model
  29. 29. Evolutionary Optimization • Multiple strategies will be compared • All run on RSM • Results validated with simulation • Objective is to reduce PID error and reduce experiment count • Single Objective Optimization Algorithms Tested – Differential – Self adaptive – Simulated Annealing – CMA-ES – Particle Swarm
  30. 30. Comparison of Optimization Strategies
  31. 31. Baseline vs. Optimal
  32. 32. Performance Impact Strategy Experiments Normalized PID Error Validated Difference from Nominal Nominal 0 892553 Differential 256 660291 659332 26.1% Self Adaptive 333 660575 660027 26.05% Annealing 153 660100 659301 26.13% Particle Swarm 664 660085 658948 26.17% CMA-ES 233 661889 661149 25.92%
  33. 33. Resource Impact Task Time w/o Optimus Time with Optimus Total Time Saved Create Robotic arm model 120 min 120 min 0 Create workflow 0 5 min -5 min Test each PID setting (3) 2 min 0 6 min Tune entire PID (3) 60 min 0 180 min Simulate arm movement 2 min 2 min 0 Run Optimization (253 simulations) 506 CPU min 200 CPU min 306 CPU min
  34. 34. Is it worth the time? • Workflow development took 10 minutes • For one tuning routine Optimus saved – 3 hours of human time – 5 CPU hours
  35. 35. With Scilab and Optimus you can Conclusion Save Time Consolidate Knowledge • Drive Scilab and combine with other tools • Automate Repetitive Tasks • Maximize efficient use of your simulation resources • Simplify your design work by focusing on key parameters • Automate parametric studies • Intelligent optimization methods • Create fast and accurate meta models • Share model data through Excel, etc… Improve Performance
  36. 36. Questions & Answers www.noesissolutions.com info@noesissolutions.com taylor.newill@noesissolutions.com Web E-mail

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