Human-Machine Robot Control System Parameter Identification

To complete the control of exoskeleton carrying robot perfectly, the human-machine interaction forces model should be identified, which can be simulated using spring-damper model, that is, the coefficient elasticity and damping should be gotten. For the coupling of the several joints, the parameters should be optimized from the system global performance. In this paper, estimation of distribution algorithm(EDA) is used to identification interaction parameters. Second-order EDA based on general structure Gauss network is introduced to replace the condition probability density function, the crossover and mutation operators are added to speed the evolution process. Combining the individual energy-entropy selection, the detail human-machine interaction forces identification method using the improved estimation of distribution algorithm is given and the human-machine carrying robot control system simulation results show the validity of the method.