An Intelligent Framework for High-Precision Dynamic Force Measurement of Large Sources Based on GAARBP and Bayesian Optimization

The measurement of dynamic forces within spacecraft sources is critical for safeguarding mission accuracy and stable operation. Two major challenges impede accurate measurement of dynamic forces from large sources: the severe nonlinearity of platforms; leading to inaccuracies in traditional methods; and the explosion of redundant sensor combinations, lowering optimization efficiency. To address these issues, this article proposes an intelligent framework that synergistically integrates the genetic algorithm with adaptive-learning-rate-based back propagation (GAARBP) and Bayesian optimization (BO). The GAARBP module addresses the nonlinear input-output mapping problem of the platform, and enhances the efficiency and accuracy of model training through adaptive learning rate adjustment. The BO module employs a blockwise Matérn kernel function tailored for sensors on horizontal and vertical planes to construct a Gaussian process surrogate model, alongside an expected improvement-based acquisition function for efficient optimization. These two modules operate in a closed loop, wherein GAARBP provides accurate fitting errors to guide BO’s selection, and BO feeds back optimal sensors to enhance GAARBP’s model training. Experimental results show that the framework achieves higher measurement accuracy (error: 2.28%) and superior decoupling performance (error: 1.13%), sufficient to satisfy the measurement requirements of large vibration sources in spacecraft. Moreover, compared with conventional methods (exhaustive search, D-optimization, particle swarm optimization, etc.), the proposed framework selects the best sensor combination more effectively for the optimization problem of six sensors selected from 24, with outcomes that are virtually globally optimal. This article offers a novel, effective approach to synergistically solving nonlinear dynamic force measurement and sensor optimization in complex environments. It thereby promotes the intelligent advancement of testing technologies in these fields.