초록 ▼

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 spee

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.

대표청구항 ▼

[ We claim:] [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 t

[ We claim:] [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; anddetermining a machine process model at a time k, the machine process model having the following form: EQU 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; andw represents measurement modeling and sensor errors.