1. Project Goals: The Hardware: The Problem: Results to date:
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.
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advisor
The Problem:
Wayne Book 1 2 3 4 5 6
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, ρ, A, L m F
w(x,t)
CAMotion Inc. x
Mean Square Error of
Tip Position Estimate (Simulation)
Kalman Filter
7.6e-4
Sliding Mode Observer
1e-4
Model of Flexible Sub-System based on
Assumed Modes Method
Next steps:
Explain the pictures if not obvious
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).
Closed-Loop Control of Experimental Test-Bed