1. Technical Field
The present invention relates to linear systems, and more particularly to a method for identifying friction parameters for linear module.
2. Description of Related Art
For automated equipment using ball screws, the automated equipment's accuracy of positioning mainly relies on the ball screw's preload that eliminates backlash in the ball screw and increase the rigidity of the ball screw. However, such preload inevitably increases friction between the contacting surfaces, and leads to quadrant errors when the screw shaft changes directions at a high speed, thereby affecting adversely the accuracy of the automated equipment.
For addressing this issue, a known approach involves using a LuGrefriction model to build up a relation curve between the friction torque and the velocity, and then identifying the relevant parameters by means of curve fitting. However, the use of the LuGrefriction model requires many times of fixed velocity friction tests, making this known approach greatly limited and thus less feasible in practice. In addition, in the process of performing curve fitting, since there are too many parameters remain unknown, the identification is quite difficult.
The primary objective of the present invention is to provide a method for identifying friction parameters for a linear module, which eliminates the use of multiple fixed velocity friction tests, so as to make the parameter-identifying process much easier and much more feasible in practice.
For achieving the foregoing objective, the disclosed method comprises three steps. The first step is to provide a parametric equation that is written as: Tm=Jα+Tcsgn(ω)+(Ts−Tc)e−(ω/ω
Thereby, the disclosed method divides the linear module's moving velocity into a high-speed segment interval and a low-speed segment interval, so that all the relevant parameters can be identified during the linear module's one reciprocating movement, so as to make the parameter-identifying process much easier and much more feasible in practice.
Referring to
In the step a) S1, a first equation is derived from a LuGrefriction model. The first equation is written as Tf=Tcsgn(ω)+(Ts−Tc)e−(ω/ω
Tm=Jα+Tcsgn(ω)+(Ts−Tc)e−(ω/ω
In the step b) S2, when ω is much greater than ωs, the linear module is in the high-speed segment. At this time, (Ts−Tc)e−(ω/ω
In a first approach, sinusoidal velocity planning (as shown in
and making Y=AX, where Y is a vector composed of the motor's output torques, A is a matrix composed of the motor output shaft's angular acceleration and the motor output shaft's angular speed, and X is a vector composed of the parameters to be identified. At this time, the previous matrix can be rewritten into:
and by using the least square method, J, Tc and σ2 can be obtained.
The second approach is to use trapezoidal velocity planning (as shown in
so as to derive
After σ2 and Tc are derived,
can be obtained by using the measuring signals in the high-speed segment (ω is much greater than ωs) and the parametric equation of the step a).
In the step c) S3, when ω is smaller than ωs or close to ωs, the linear module is located in the low-speed segment interval. At this time, (Ts−Tc)e−(ω/ω
As a first approach, the unknown parameters and the parameters identified in step b) are separated and their logarithms are taken, respectively, so as to make the parametric equation of the step a) become a linear equation that is written as p=q−ω2·r, where p=ln(Tm−Jα−Tcsgn(ω)−σ2ω), and q=ln(Ts−Tc), r=1/(ωs)2. Since p can be determined by substituting the known parameters, and ω can be found through direct measurement, q and r can be easily obtained, and in turn Ts and ωs can be identified.
As a second approach, the parametric equation is first rewritten into: Tm−Jα=(Ts−Tc)e−(ω/ω
To sum up, the disclosed method divides the linear module's moving velocity into a high-speed segment interval and a low-speed segment interval, so that by making the linear module perform only one reciprocating movement, all the relevant parameters can be identified. As compared to the prior art, the present invention makes identification of the parameters much more easier and much more feasible in practice.
| Number | Date | Country | Kind |
|---|---|---|---|
| 103122131 | Jun 2014 | TW | national |