A dynamics model for Self-Propelled Modular Transporter*

This study constructs a three-degree-of-freedom (3-DOF) dynamics model for a Self-Propelled Modular Transporter (SPMT) with six axles. A novel particle swarm optimization with variable control factors and elitist learning (PSO-VFEL) is developed to quantify unknown terms and is compared with three existing optimizers, including an initial PSO, a traditional PSO, and a PSO with variable control factors (PSO-VF). Compared to the existing optimizers, the PSO-VFEL demonstrates higher flying flexibility and particle diversity, attributed to the generationally variable factors and additional guidance from the elitist learning strategy. Simulation data from the Carla software is employed for model calibration. Results show that the developed PSO-VFEL is superior for calibrating the dynamics model, achieving fitness improvements of 31.852%, 20.231%, and 5.154% compared to the initial PSO, the traditional PSO, and the PSO-VF, respectively. This research provides a dynamics model and a complementary calibrator for engineering applications.