Joint dynamics modeling and parameter identification for space robot applications

Long-term mission identification and model validation for in-flight manipulator control system in almost zero gravity with hostile space environment are extremely important for robotic applications. In this paper, a robot joint mathematical model is developed where several nonlinearities have been taken into account. In order to identify all the required system parameters, an integrated identification strategy is derived. This strategy makes use of a robust version of least-squares procedure (LS) for getting the initial conditions and a general nonlinear optimization method (MCS - multilevel coordinate search - algorithm) to estimate the nonlinear parameters. The approach is applied to the intelligent robot joint (IRJ) experiment that was developed at DLR for utilization opportunity on the International Space Station (ISS). The results using real and simulated measurements have shown that the developed algorithm and strategy have remarkable features in identifying all the parameters with good accuracy.