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

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. Agenda
Optimus Overview
Integrating Scilab
Genetic optimization of a PID
1
2
3
Future Development
4

3. Agenda
Optimus Overview
Integrating Scilab
Genetic optimization of a PID
1
2
3
Future Development
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. 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. 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. Noesis Solutions
Most extensively deployed software of its kind…

8. A few default interfaces

9.  Automated
 Multi code
 Multi CPU
 Repeatable
CFD
20 Design
parameters
FEA
Post-Processing

10. Integrating Scilab in
Optimus
1

11. Should I Automate? (XKCD 1319)
Mouse over Text:
‘Automating’ comes from the
roots ‘auto-’ meaning ‘self-’ and
‘mating’, meaning ‘screwing’

12. Is it worth the time? (XKCD 1205)

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. 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

16. Evolutionary Optimization of a
PID Controlled Robotic Arm
2

17. 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

18. 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.

19. 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

20. Robotic Tools for Scilab/Xcos
• From the ‘Scilab Ninja’
– Dr. Varodom Toochinda
• Beta Version
– Kinematics
– Dynamics
– Path generation
– Control

21. 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

22. Scilab/XCOS Setup

23. Parametric Usage of the Toolbox
• Optimus will detect any
controllable inputs
• Outputs can be
extracted
– From memory
– From output files
– From response variables

24. Process Integration

25. Automation

26. Optimization Strategy
DOE
RSM
GLOBAL
OPTIMIZATION
LOCAL
OPTIMIZATION
GOAL

27. 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

28. Response Surface Model
• 18 different RSM’s
were tested, RBF was
selected
• Lowest error, created
quickly, easily
exported

29. Response Surface Model

30. 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

31. Comparison of Optimization Strategies

32. Baseline vs. Optimal

33. 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%

34. 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

35. Is it worth the time?
• Workflow development
took 10 minutes
• For one tuning routine
Optimus saved
– 3 hours of human time
– 5 CPU hours

36. 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

37. Questions & Answers
www.noesissolutions.com
info@noesissolutions.com
taylor.newill@noesissolutions.com
Web
E-mail