Method and apparatus for determining pitch and ground speed of an earth moving machines

• US 5,860,480 A
• Filed: 04/08/1997
• Issued: 01/19/1999
• Est. Priority Date: 04/08/1997
• Status: Expired due to Fees

• Current Assignee: Caterpillar Incorporated
• Original Assignee: Caterpillar Incorporated
• Inventors: Jayaraman, Gangadhar, Lee, Eric F., Kleimenhagen, Karl W., Chizeck, Howard J., Stratton, Kenneth L.
• Primary Examiner(s): Will, Thomas B.
• Assistant Examiner(s): PEZZUTO, ROBERT ERIC
• Application Number:
  US08/835,889
• Time in Patent Office: 651 Days
• Field of Search: 172/2, 172/4, 172/4.5, 172/7, 172/9, 172/826, 364/424.07, 364/449, 364/450, 342/63, 342/64, 342/65, 342/357
• US Class Current: 172/2
• CPC Class Codes: E02F 3/845 using mechanical sensors to determine the blade position, e.g. inclinometers, gyroscopes, pendulums
• CPC Class Codes: E02F 9/2045 Guiding machines along a predetermined path for graders E02F3/841; machines for construction of roads E01C19/004
• CPC Class Codes: G01C 9/00 Measuring inclination, e.g. by clinometers, by levels
• CPC Class Codes: G01P 3/50 for measuring linear speed G01P3/56 takes precedence

Abstract

The present invention is directed toward estimating certain operating parameters of an earth moving machine. Advantageously, the present invention utilizes a Kalman filter to estimate the pitch, pitch rate and ground speed of the earth moving machine as a function of the sensed pitch and ground speed signals. By estimating the pitch and ground speed, the present invention overcomes the prior problems of sensing signal noise and bias. The present invention overcomes these problems by combining pitch, pitch rate and ground speed, and determining an estimate of pitch and ground speed by using a sensor measurement model, machine process model and Kalman filter update equations.

Claims

1. A method for determining pitch and ground speed of an earth moving machine, including the steps of:

determining a set of equations that model a set of operating parameters of the earth moving machine;

sensing a plurality of operating parameters on the earth moving machine including the steps of sensing the machine pitch, pitch rate, and ground speed and responsively producing a set of sensed parameter signals;

receiving the sensed parameter signals, and determining a pitch estimate and a ground speed estimate using least-means-squared estimation techniques performed by a Kalman filter in response to the sensed parameter signals and the modeling equations; and

determining a machine process model at a time k, the machine process model having the following form;

space="preserve" listing-type="equation">x(k+1)=Fx(k)+Gu(k)+w(k)

where;

x represents process variables;

u represents input variables;

k represents a unit of time;

F represents a machine system matrix;

G represents an input matrix; and

w represents measurement modeling and sensor errors.

2. A method, as set forth in claim 1, including the step of determining a measurement model at a time k, the measurement model having the following form:

space="preserve" listing-type="equation">y(k)=Hx(k)+y(k)

where;

H represents an output matrix; and

v represents sensed machine operating parameters.

3. A method, as set forth in claim 2, including the step of updating an error covariance matrix at a time k based on measurements taken up to time k, the error covariance matrix having the following form:

space="preserve" listing-type="equation">P<

k|k>

=P<

k|k-1>

-P<

k|k-1>

H.sup.T (HP<

k|k-1>

H.sup.T +R.sub.v) .sup.-1 HP<

k|k-1>

where;

R_v represents a measurement noise covariance matrix.

4. A method, as set forth in claim 3, including the step of updating a Kalman gain matrix at a time k, the Kalman gain matrix having the following form:

space="preserve" listing-type="equation">L(k)=P<

k|k>

H.sup.T R.sub.v.sup.-1.

5.

5. A method, as set forth in claim 4, including the step of determining an estimate of x at a time k based on measurements taken up to time k, the estimate having the following form:

space="preserve" listing-type="equation">x<

k|k>

=x<

k|k-1>

+L(k)(y(k)-Hx<

k|k-1>

).

6.

6. A method, as set forth in claim 5, including the step of updating an error covariance matrix at a time k+1, the error covariance matrix at a time k+1 having the following form:

space="preserve" listing-type="equation">P<

k+1|k>

=FP<

k|k>

F.sup.T +R.sub.W

where;

R_w, represents a covariance matrix.

7. A method, as set forth in claim 6, including the step of determining an extrapolation to time k+1 of the estimate of x at time k, the extrapolation having the following form:

space="preserve" listing-type="equation">x<

k+1|k>

=Fx<

k|k>

+Gu(k)

wherein the pitch and ground speed estimates are determined from the extrapolation equation.

8. An apparatus for determining pitch and ground speed of an earth moving machine, comprising:

a ground speed sensor measures the ground speed of the machine and responsively produces a ground speed signal;

a pitch sensor measures the pitch of the machine and responsively produces a pitch signal;

a pitch rate sensor measures the pitch rate of the machine and responsively produces a pitch rate signal; and

a Kalman filter receives the ground speed, pitch and pitch rate signals, and responsively determines a pitch estimate and a ground speed estimate using least-means-squared estimation techniques that include determining a machine process model at a time k, the machine process model having the following form;

space="preserve" listing-type="equation">x(k+1)=Fx(k)+Gu(k)+w(k)

where;

x represents process variables;

u represents input variables;

k represents a unit of time;

F represents a machine system matrix;

G represents an input matrix; and

represents measurement modeling and sensor errors.

9. An apparatus, as set forth in claim 8, wherein said least squares estimation techniques include determining a measurement model at a time k, the measurement model having the following form:

space="preserve" listing-type="equation">y(k)=Hx(k)+y(k)

where;

H represents an output matrix; and

v represents sensed machine operating parameters.

10. An apparatus, as set forth in claim 8, wherein said least squares estimation techniques include updating an error covariance matrix at a time k based on measurements taken up to time k, the error covariance matrix having the following form:

space="preserve" listing-type="equation">P<

k|k>

=P<

k|k-1>

-P<

k|k-1>

H.sup.T (HP<

k|k-1>

H.sup.T +R .sub.v).sup.-1 HP<

k|k-1>

where;

R_v represents a measurement noise covariance matrix.

11. An apparatus, as set forth in claim 8, wherein said least squares estimation techniques include updating a Kalman gain matrix at a time k, the Kalman gain matrix having the following form:

space="preserve" listing-type="equation">L(k)=P<

k|k>

H.sup.T R.sub.v.sup.-1.

12. An apparatus, as set forth in claim 8, wherein said least squares estimation techniques include determining an estimate of x at a time k based on measurements taken up to time k, the estimate having the following form:

space="preserve" listing-type="equation">x<

k|k>

=x<

k|k-1+L(k)(y(k) -Hx<

k|k-1>

).

13. An apparatus, as set forth in claim 8, wherein said least squares estimation techniques include updating an error covariance matrix at a time k+1, the error covariance matrix at a time k+1 having the following form:

space="preserve" listing-type="equation">P<

k+1|k>

=FP<

k|k>

F.sup.T +R.sub.W

where;

R_w represents a covariance matrix.

14. An apparatus, as set forth in claim 8, wherein said least squares estimation techniques include determining an extrapolation to time k+1 of the estimate of x at time k, the extrapolation having the following form:

space="preserve" listing-type="equation">x<

k+1|k>

=Fx<

k|k>

+Gu(k)

wherein the pitch and ground speed estimates are determined from the extrapolation equation.