<p>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.</p><p>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. Apart from models there is a need for some sort of algorithm for the identification and some different recursive algorithms are presented. The main subject of this thesis is the identification of parameters and the excitation signals needed for the identification of each parameter.</p><p>The models and algorithms presented in this thesis work in a principle point of view. Despite this they work in varying extent for the different types of parameters. Estimation of linear and nonlinear friction and linear spring stiffnes works relatively well. Nonlinear spring stiffnes and hysteresis have not been possible to estimate. Backlash which is estimated with a hybrid variant of a RPEM which is not fully recursive works best. When it is not possible to identify the parameters suggestions on other solutions are given, such as for example extension of the model, use of other algorithms or optimization of the excitation signal.</p>

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. Apart from models there is a need for some sort of algorithm for the identification and some different recursive algorithms are presented. The main subject of this thesis is the identification of parameters and the excitation signals needed for the identification of each parameter. The models and algorithms presented in this thesis work in a principle point of view. Despite this they work in varying extent for the different types of parameters. Estimation of linear and nonlinear friction and linear spring stiffnes works relatively well. Nonlinear spring stiffnes and hysteresis have not been possible to estimate. Backlash which is estimated with a hybrid variant of a RPEM which is not fully recursive works best. When it is not possible to identify the parameters suggestions on other solutions are given, such as for example extension of the model, use of other algorithms or optimization of the excitation signal.