Estimator Design For Engine Speed Limiter

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Estimator Design For Engine Speed Limiter
Presented By Beshir, Abeba Kharrat, Amine Hu,
Zhiyuan Sun,Yu He, Nan
Professor Riadh Habash TA Wei Yang
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Contents

• References
• Background
• Project Objective
• Kalman Observer Design
• Experiment Results
• Conclusion

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References

• Engine Speed Limiter for Watercrafts
• Philippe Micheau, R. Oddo and G. Lecours, from
  IEEE Transaction on Control Systems Technology
  VOL 14, NO 3, May 2006.
• Engine Speed Control
• Peter Wellstead and Mark Readman, control systems
  principles.co.uk
• An Observer-Based Controller Design Method for
  Improving Air/Fuel characteristics of Spark
  Ignition Engines
• By Seibum B. Choi and J. Karl Hedrick, IEEE
  TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL.
  6, NO. 3, MAY 1998
• http//www-ccs.ucsd.edu/matlab/toolbox/control/kal
  man.html?cmdnamekalman
• http//auto.howstuffworks.com/engine1.htm
• http//www.cs.unc.edu/welch/kalman/
• Kalman Filter Tutorial

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Background

• 3 cases watercraft propeller

Partially loaded (partially submerged)
Unloaded (completely emerged)
Fully loaded (completely submerged)
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Project Objective

• Design observer to estimate state variables
• Load Torque (Tload)
• Engine Speed (N)

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Observer (State Estimation)
y(t)
Plant
Observer (state estimator)
xhat(t)
u(t)
Xhat(t) Nhat, Tloadhat (2 state variables)
u(t) Teng
y(t) N, Tload (2 outputs)
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System Modeling
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System Modeling (contd)
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System Modeling (contd)

• To estimate TLoad.

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Kalman Filter

• Estimates the state of a system for measurements
  containing random errors (noise).
• Relatively recent development in filtering (1960)

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Kalman Filter (Contd)

• Circles -- vectors,
• Squares -- matrices
• Stars -- Gaussian noise with the associated
  covariance matrix at the lower right.

Fk -- state transition model Bk -- control-input
model wk -- the process noise
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Kalman Filter (Contd)
Kalman Filter phases
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Experiment Results
Input Data (Teng)
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Experiment Results (Contd)
Output Data (N, TLoad)
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Conclusion

• Kalman filter provides good estimate of state
  variables in presence of noise/disturbance.
• Advantages
• Can achieve virtually any filtering effect
• Forecasting characteristics using Least-Square
  model
• Reduce False alarms (filter disturbances)
• optimal multivariable filter

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Conclusion (Contd)

• Examples of application
• aerospace
• marine navigation
• nuclear power plant instrumentation
• demographic modeling
• manufacturing, and many others.
• Limitations/ Future improvements
• Speed filter speed is limited by the system
  architecture
• Cost

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• Questions ?