شناسایی حلقه بسته از یک ربات صنعتی که شامل انعطاف پذیری است
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|18217||2003||10 صفحه PDF||سفارش دهید||5368 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Control Engineering Practice, Volume 11, Issue 3, March 2003, Pages 291–300
Closed-loop identification of an industrial robot of the type ABB IRB 1400 is considered. Data are collected when the robot is subject to feedback control and moving around axis one. Both black-box and physically parameterized models are identified. A main purpose is to model the mechanical flexibilities. It is found that a model consisting of three-masses connected by springs and dampers gives a good description of the dynamics of the robot.
System identification is an established modeling tool in engineering and numerous successful applications have been reported. The theory is well developed, see e.g., Ljung (1999) or Söderström and Stoica (1989), and there are powerful software tools available, e.g., the System Identification Toolbox for Matlab (Ljung, 2000). Industrial robots represent an interesting challenge for system identification methods, and an overview of identification in robotics can be found in Kozlowski (1998). One application area for system identification within robotics is identification of the parameters in the kinematic description of the robot, while a second area deals with the problem of identifying the parameters in the dynamical model of the robot. A third area is to determine the parameters on the joint level including, for example, friction, motor characteristics, etc. Recent results from the latter two areas may be found in, e.g., Wang, Bi, and Zou (1996), Grotjahn, Daemi, and Heimann (2001) and Gautier and Poignet (2001). In these papers it is assumed that the robot is rigid.
نتیجه گیری انگلیسی
A physically parameterized linear three-mass model of the open loop dynamics of an industrial robot during one axis movement has been identified. Three data sets, with different input properties, have been used. Cross validations show that a model of OE type, i.e., without disturbance model, is adequate, even though data have been collected in closed loop. The estimates of moments of inertia and spring stiffness, obtained using different data sets, are fairly stable, while the estimates of friction coefficients and dampings fluctuate considerably. A possible explanation is the presence of nonlinearities in the real robot. There are a number of aspects of the presented results that are subjects for future work. One important problem is to apply the same technique for identification of several axes simultaneously. Some initial work dealing with axes two and three have started. The choice of exciting signal can also be studied further, and one step in this direction is taken in Norrlöf et al. (2002). A further topic is to continue the work in Aberger (2000) and investigate the effects of nonlinearities, like e.g., nonlinear spring stiffness, on the model properties.