In modern industrial robots the components in the transmission contain nonlinearities. These nonlinearities need to be to estimated either for better control or to use the parameters for diagnosis of the system. There is a lot of work done within system identification and mainly within the field of iterative parameter estimation.This thesis considers Recursive grey-box identification for a nonlinear model of the transmission in an industrial robot. The nonlinearities that are identified are friction, spring stiffnes, hysteresis and backlash. These nonlinearities are a part of the models that are presented in this thesis.

When a helicopter is flying, the dynamics vary depending on, for example, speed and position. Hence, a time-invariant linear model cannot describe its properties under all flight conditions. It is therefore desirable to update the linear helicopter model continuously during the flight. In this thesis, two different Recursive estimation methods are presented, LMS (Least Mean Square) and adaptation with a Kalman filter. The main purpose of the system estimation is to get a model which can be used for feedback control.