Identifying dynamic parameters of a space robot based on improved genetic algorithm

To decrease the dynamic parameter errors of a space robot and improve accuracy of path planning, according to the angular momentum conservation equation, and based on the difference between the angular momentums estimated by nominal dynamic parameters and the real ones, an error model is built for the dynamic parameter identification, then the fitness function used for Genetic Algorithm (GA) is also presented. For ″early maturity″ phenomenon easily occurred in the conventional GA, an improved GA is presented based on small bound, big mutation and elite reservation tactics. At last, as an example of a six-joint space robot, a simulation is carried out. The results show that the improved GA increases calculative efficiency and identification accuracy in spite of complicated parameters.