Robust Nonlinear Observer for Noncollocated Flexible Motion System

1
Robust Non-linear Observer for Non-collocated
Flexible Motion System
Project Goals
The Hardware
Flexible Beam 1/8 1018 Steel, 12.5 Long, 1
3/8 Wide Actuator Anorad Linear Motor Sensors
PCB Accelerometer, Anorad Linear
Encoder Controller LabVIEW Realtime 8.5
Target-PC with NI-6052E DAQ Board

• Contribute to the field of active vibration
  suppression in motion systems.
• Examine the robustness of a Sliding Mode Observer
  in presence of model uncertainty.

advisor
The Problem
Wayne Book
Results to date
Robotic arms are subject to bending, torsion and
axial compression. In certain applications, to
ensure accuracy and repeatability of the useful
end-point of the robotic arm, the flexible nature
of the arm must be taken into account during
design. Immediate benefits of this research
include
System identification of rigid sub-system and
flexible sub-system has been completed. Flexible
sub-system has been modeled using an Assumed
Modes Method model which models the first three
flexible modes of the flexible beam. Efforts
have been focused on the design and computer
simulation of a stochastic estimator, the Kalman
filter. States including tip position have been
estimated based on corrupted measurements from
sensors. The Kalman filter has been applied to
the test-bed in open-loop, at an execution rate
of 1khz. The Sliding Mode Observer has been
simulated in open-loop form, based on a simpler
4th order system model. Initial results look
promising.
Magnitude (dB)
Motion of a Single Flexible Link
student
Frequency (Hz)

• Improved control of long-reach space and
  lower-cost industrial manipulators.

• Improved accuracy and precision of general
  robotic manipulators with non-collocated
  actuators and sensors.

System Identification Model vs. Experimental
Data
Mohsin Waqar
CAMotion Depalletizer
NASA Space Manipulator
Base Tip
Position
The Approach
Time (sec)
Produce a useful model for a single flexible link
which represents the non-minimum phase behavior
accurately. Research and identify suitable state
estimators and select an appropriate feedback
control scheme. Benchmark the closed-loop
estimator performance by examining robustness of
the closed-loop system to parameter uncertainty.

Industry Sponsors
LabVIEW Simulation State Feedback Control with
Kalman Filter
E, I, ?, A, L
m
F
w(x,t)
CAMotion Inc.
x
Model of Flexible Sub-System based on Assumed
Modes Method
Next steps

• Extend the Sliding Mode Observer to Full Order
  System Model
• Compare robustness of Kalman Filter and Sliding
  Mode Observer as part of closed-loop system (both
  in simulation and on test-bed).

Explain the pictures if not obvious
Closed-Loop Control of Experimental Test-Bed