Optimization and Systems Theory Seminar
Friday, February 15 2008, 11.00-12.00, Room 3721, Lindstedtsvägen 25

Erik Wernholt, Linköpings Universitet
E-mail: erikw@isy.liu.se

Some Aspects of System Identification in Robotics

Industrial robots are today essential components in the manufacturing  industry where they are used to save costs, increase productivity and  quality, and eliminate dangerous and laborious work. High demands on  accuracy and speed of the robot motion require that the mathematical  models, used in the motion control system, are accurate. The models are used to describe the complicated nonlinear relation between the robot motion and the motors that cause the motion. Accurate dynamic robot models are needed in many areas, such as mechanical design, performance simulation, control, diagnosis, and supervision.

A trend in industrial robots is toward lightweight robot structures, where the weight is reduced but with a preserved payload capacity. This is motivated by cost reduction as well as safety issues, but results in a weaker (more compliant) mechanical structure with enhanced elastic effects. For high performance, it is therefore necessary to have models describing these elastic effects.

In this talk, we will deal with identification of dynamic robot models, which means that measurements from the robot motion are used to estimate unknown parameters in the models. The measured signals are angular position and torque of the motors. Identifying robot models is a challenging task since an industrial robot is a multivariable, nonlinear, unstable, and resonant system. The unknown parameters (typically spring-damper pairs) in a physically parameterized nonlinear dynamic model are identified, mainly in the frequency domain, using estimates of the nonparametric frequency response function (FRF) in different robot configurations/positions. Each nonparametric FRF then describe the local behavior around an operating point. The nonlinear parametric robot model is linearized in the same operating points and the optimal parameters are obtained by minimizing the discrepancy between the nonparametric FRFs and the parametric FRFs (the FRFs of the linearized parametric robot model).

The talk will cover the selection of optimal robot configurations/positions, some methods for estimation of the multivariable nonparametric FRF, and a comparison of different parameter estimators. The usefulness of the proposed identification procedure is illustrated by experiments where the identified nonlinear robot model gives a good global description of the dynamics in the frequency range of interest.

Calendar of seminars Last update: January 7, 2007 by Marie Lundin.