LAUSR

The mechatronic elevator system driven by a permanent magnet synchronous motor (PMSM) is modeled by both the mechanical and electrical equations. The dimensionless forms are also derived for the purpose of practicable upward and downward movements. In this brief, the high-degree normalized polynomial trajectory (NPT) is designed by the particle swarm optimization (PSO) and self-learning particle swarm optimization (SLPSO) methods to search for its coefficients by minimizing the input absolute electrical energy (IAEE). It is found that the 19-degree (19-D) NPT by the SLPSO has the minimum IAEE for the mechatronic elevator system. Then, the adaptive controller is designed to track the 19-D NPT for exhibiting the robustness and energy-saving characteristics. Finally, comparisons between 7-D and 19-D NPTs are tracked numerically and experimentally. It is found that the IAEE by the proposed method can save above 31% than that of the exact 7-D NPT for the mechatronic elevator system. Moreover, the methodology can also be applied to mechatronic system, especially driven by a PMSM.