Research on High-Precision Dynamic Modeling and Performance Evaluation of Inertially Stabilized Platforms

Abstract

The complex influence of various disturbances on an inertially stabilized platform (ISP) restricts the further improvement of its servo performance. This article investigates the mapping relationship between internal and external disturbances and servo performance by establishing a high-precision dynamics model of the servo device with simulation and experiment. For internal disturbances, a nonlinear model of friction and backlash is established based on a BP neural network, and the transmission error is reconstructed based on the principle of main order invariance. For external disturbances, the road disturbance torque, changing inertia, and mass imbalance torque are modeled. The quantitative mapping relationship between internal and external disturbances and servo performance is obtained through simulation, in which friction and road disturbance are the largest internal and external factors affecting the servo performance, respectively. These conclusions are verified by load simulation experiments on a certain type of servo device, in which the servo performance is improved by 17% for every 25% reduction of friction torque, and the servo performance is reduced by 12% for every 33% increase of road disturbance torque. The research results provide a reference for servo device selection, performance indicator assignment, and performance prediction of the ISP.