High-Precision Trajectory Tracking in Changing Environments Through - - PowerPoint PPT Presentation

β–Ά
high precision trajectory tracking in changing
SMART_READER_LITE
LIVE PREVIEW

High-Precision Trajectory Tracking in Changing Environments Through - - PowerPoint PPT Presentation

High-Precision Trajectory Tracking in Changing Environments Through " Adaptive Feedback and Iterative Learning Karime Pereida, Rikky R. P. R. Duivenvoorden, and Angela P. Schoellig Additional contributions from Dave Kooijman ICRA


slide-1
SLIDE 1

High-Precision Trajectory Tracking in Changing Environments Through β„’" Adaptive Feedback and Iterative Learning

Karime Pereida, Rikky R. P. R. Duivenvoorden, and Angela P. Schoellig

Additional contributions from Dave Kooijman ICRA Spotlight Talk May 30th, 2017

slide-2
SLIDE 2

Motivation

2 Karime Pereida

Unknown and changing disturbances Tracking error Varying payloads Wind Different systems Varying topology Varying weather Goal: achieve high tracking performance

slide-3
SLIDE 3

Objectives

3 Karime Pereida

Input

System 1

Output Tracking error Unknown and changing disturbances Repeatable and reliable behavior Improve over iterations High tracking performance even if dynamics change

System 2

Goal: achieve high tracking performance

slide-4
SLIDE 4

Proposed Approach

4 Karime Pereida

Repeatable and reliable behavior Improve over iterations High tracking performance even if dynamics change

β„’" Adaptive Controller Iterative Learning Controller

  • Define a reference (desired) behavior.

𝑇𝑧𝑑𝑒𝑓𝑛 π‘ƒπ‘£π‘’π‘žπ‘£π‘’ βˆ’ π‘†π‘“π‘”π‘“π‘ π‘“π‘œπ‘‘π‘“ π‘π‘π‘’π‘“π‘š π‘ƒπ‘£π‘’π‘žπ‘£π‘’ < 𝛿 ∝

: ;

  • Stay provably close to reference model.
  • Learns through repetition.
  • Fast convergence.
  • Zero tracking error not guaranteed.
  • Can compensate for systematic tracking

errors.

  • No re-learning if system or dynamics

change.

slide-5
SLIDE 5

Previous Work

5 Karime Pereida

Input

System 1

Output Tracking error Unknown and changing disturbances

β„’" Adaptive Controller Iterative Learning Controller

[6] B. AltΔ±n and K. Barton, β€œRobust iterative learning for high precision motion control through β„’" adaptive feedback,” Mechatronics, vol. 24, no. 6, pp. 549–561, 2014.

Simulation results

slide-6
SLIDE 6

Proposed Approach

Karime Pereida 6

Input

System 1

Output

β„’" Adaptive controller

Reference model behavior

Iterative Learning Controller

Updated Input Improve tracking over iterations

System 2

Disturbances

slide-7
SLIDE 7

Results

Karime Pereida 7

Input

System 1

Output

β„’" Adaptive controller

Reference model behavior

Iterative Learning Controller

Updated Input Improve tracking over iterations Disturbances

Proportional- Derivative Proportional- Integral- Derivative

slide-8
SLIDE 8

Results

Karime Pereida 8

Contributor: Dave Kooijman

Unknown and changing disturbances: Wind

slide-9
SLIDE 9

Results

Karime Pereida 9

Unknown and changing disturbances: Wind

Contributor: Dave Kooijman

slide-10
SLIDE 10

Transfer learning: no need to relearn

Results

Karime Pereida 10

Contributor: Dave Kooijman

slide-11
SLIDE 11

Transfer learning: no need to relearn

Results

Karime Pereida 11

Contributor: Dave Kooijman

slide-12
SLIDE 12

Summary

  • Extension work
  • K. Pereida, D. Kooijman, Rikky R. P. R. Duivenvoorden, and Angela P.

Schoellig, β€œTransfer Learning for High-Accuracy Trajectory Tracking Through β„’" Adaptive Feedback and Iterative Learning”, submitted to International Journal of Adaptive Control and Signal Processing.

  • Transfer learning from simulation to real system.
  • Use reference model to calculate input.

Karime Pereida 12

Input

System 1 Output β„’" Adaptive controller Reference model behavior Updated Input Improve tracking over iterations Disturbances Iterative Learning Controller

Repeatable and reliable behavior Improve over iterations High tracking performance even if dynamics change

slide-13
SLIDE 13

Thank you!

Karime Pereida PΓ©rez

<karime.pereida@robotics.utias.utoronto.ca>