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

2. Contents References Background Project Objective
Kalman Observer & Design
Experiment & Results
Conclusion

3. References Engine Speed Limiter for Watercrafts Engine Speed Control
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
Kalman Filter Tutorial

4. Background 3 cases: watercraft propeller:
Fully loaded (completely submerged)
Partially loaded (partially submerged)
Unloaded (completely emerged)

5. Project Objective Design observer to estimate state variables:
Load Torque (Tload)
Engine Speed (N)

6. 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)

7. System Modeling

8. System Modeling (cont’d)

9. System Modeling (cont’d)
To estimate TLoad.

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

11. Kalman Filter (Cont’d)
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

12. Kalman Filter (Cont’d)
Kalman Filter phases:

13. Experiment & Results
Input Data (Teng)

14. Experiment & Results (Cont’d)
Output Data (N, TLoad)

15. 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

16. Conclusion (Cont’d) 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

17. Questions ?