This article focuses on the output-only recursive identification of time-varying systems by using parametric time-domain methods. A novel multivariate recursive Bayesian linear regression method is proposed based on the vector time-dependent autoregressive moving average model. The standard setup of univariate batch Bayesian linear regression is first extended to the multivariate case for multiple response signal modeling and further extended to the recursive case to meet the output-only recursive identification requirement of practical systems. A sliding window mechanism is finally applied to deemphasize data from the remote past and fix the computational complexity for each consecutive update, allowing the proposed method to be capable of tracking the time-varying dynamics online. The proposed multivariate recursive Bayesian linear regression method is first validated by a simple numerical system and subsequently applied to identify two mechanical systems with typical time-varying dynamics. Comparative identification results via Monte Carlo tests numerically and experimentally demonstrate the superior achievable accuracy and time-varying tracking capability of the proposed method.

本文重点介绍通过使用参数时域方法对时变系统进行仅输出的递归识别。基于矢量时间相关的自回归移动平均模型，提出了一种新颖的多元递归贝叶斯线性回归方法。单变量贝叶斯线性回归的标准设置首先扩展到用于多响应信号建模的多变量情况，然后再扩展到递归情况，以满足实际系统的仅输出递归识别要求。最后，将滑动窗口机制应用于远程数据的去加重处理，并固定每个连续更新的计算复杂度，从而使所提出的方法能够在线跟踪时变动态。首先通过一个简单的数值系统对提出的多元递归贝叶斯线性回归方法进行验证，然后将其应用于识别具有典型时变动力学的两个机械系统。通过蒙特卡洛测试的比较识别结果在数值上和实验上证明了所提出方法的优越的可实现精度和随时间变化的跟踪能力。