Estimator Design For Engine Speed Limiter

Estimator Design For Engine Speed Limiter

Professor: Riadh Habash

TA: Wei Yang

Presented By:Beshir, AbebaBeshir, AbebaKharrat, AmineKharrat, AmineHu, ZhiyuanHu, ZhiyuanSun,YuSun,YuHe, NanHe, Nan

Contents

• References

• Background

• Project Objective

• Kalman Observer & Design

• Experiment & Results

• Conclusion

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/kalman.html?cmdname=kalman

• http://auto.howstuffworks.com/engine1.htm• http://www.cs.unc.edu/~welch/kalman/

– Kalman Filter Tutorial

Background

• 3 cases: watercraft propeller:

Partially loaded (partially

submerged)

Unloaded (completely emerged)

Fully loaded (completely submerged)

Project Objective

• Design observer to estimate state variables:– Load Torque (Tload)

– Engine Speed (N)

Observer (State Estimation)

PlantObserver

(state estimator)

u(t) y(t) xhat(t)

u(t) = Teng y(t) = N, Tload

(2 outputs)

Xhat(t) = Nhat, Tloadhat(2 state variables)

System Modeling

eDisturbancRandomW

TorqueLoadT

TorqueEngineT

SpeedEngineN

inertiaengineisII

CWhere

WTTdt

dNC

T

Load

eng

TLoadeng

:

:

.:

:

,60

2: 1

1

System Modeling (cont’d)

loadedfully

unloaded

loadingtherepresentswhere

NfT

lyrespectivecylinderainfueland

airofamountthearemandmmm

AFRRatioFuelAiriswhere

NgT

Load

fafa

eng

:1

:0

:

)(

.

,66.14/

)(:

),(

System Modeling (cont’d)

• To estimate TLoad.

noiseprocessisw

noisetmeasuremenisv

TuCNfk

CG

k

CB

k

CA

T

Nx

Where

tvtCxty

tGwtButAxdt

tdx

functionspacestateandFromdt

dNf

dt

dN

dN

df

dt

dTNfT

WTTdt

dNC

englin

linlinLoad

LoadLoad

TLoadeng

],10

01[],

)(

0/1[

],/1

[],0

/10[],[

:

)()()(

)()()()(

:),2()1(

)2()()(

)1(

max

1

11

1

Kalman Filter

• Estimates the state of a system for measurements containing random errors (noise).

• Relatively recent development in filtering (1960)

Kalman Filter (Cont’d)

Circles -- vectors,

Squares -- matrices

Stars -- Gaussian noise with the associated covariance matrix at the lower right.

Fk -- state transition modelBk -- control-input modelwk -- the process noise

Kalman Filter (Cont’d)

Predict(k) Update(k)

K+1

Kalman Filter phases:

Experiment & Results

Input Data (Teng)

Experiment & Results (Cont’d)

Output Data (N, TLoad)

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

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

Questions ?