IMPSO-Based Trajectory Optimization and Control of Liquid Apply Sound Deadener Spraying Robot for High-Speed Train

Applying liquid-applied sound deadener (LASD) in the interiors of high-speed trains effectively reduces noise and vibration, thereby enhancing passenger comfort. Currently, the application of LASD relies on manual spraying methods, with the quality dependent on the workers' experience, making it challenging to ensure uniform coating thickness over large areas. Additionally, the occupational health risks for the workers cannot be ignored. This paper analyzes the process requirements for spraying LASD in high-speed train interiors and designs an automated robotic spraying system suitable for this application. A kinematic and dynamic model of a six-degrees-of-freedom (6-DOF) spraying robot is established. A slice generation and boundary fitting method is applied to the STL model of the high-speed train, generating the initial spraying trajectory. Under time-optimal constraints, the generated trajectory points are optimized for multiple objectives using an improved particle swarm optimization (IMPSO) method. Furthermore, this study proposes an ISMC-RBF control algorithm, which utilizes an integral sliding mode control (ISMC) algorithm to improve the precision of robot trajectory tracking, and a radial basis function (RBF) neural network estimation method to suppress disturbances. Simulation results demonstrate that the optimized trajectory reduced the spraying operation time by approximately 30%, additionally, the ISMC-RBF controller can reduce trajectory tracking errors to 50% of those achieved with PID control, while significantly improving the system's dynamic response and steady-state performance. Overall, the proposed methods substantially increase the operational efficiency and precision of the spraying robot, providing effective technical support for high-speed train interior spraying.