A Mechatronic Motor-Table System Identification Based on an Energetics Fitness Function

In this paper, system identification by the self-learning particle swarm optimization (SLPSO) with a new energetics fitness functions (FFs) is proposed to identify a mechatronic motor-table system. First, the completed mathematical model containing both mechanical and electrical equations is successfully formulated. Second, a new energetics FF containing an energy balance equation are proposed and employed in the SLPSO to identify the unknown parameters of a mechatronic system. It is found that the system identification using this new FF, unknown parameters can be identified well and the all states have better results converging toward the real ones. On the other hand, when the FF is only a part of the state errors, not all parameters are able to be accurately identified and only partial states converge. Therefore, the new FF with an energy balance equation is adopted in experiments for a real mechatronic motor-table system and the unknown parameters are successfully identified by the SLPSO experimentally.