横向控制过程测量数据的稀疏性以及模型的高维性、强耦合和不确定性使得系统模型难以辨识． 为快速、准确地辨识系统模型，文中提出了一种改进的递推 Levenberg-Marquart(RLM)算法． 首先阐述了横向控制过程的二维参数化模型和辨识该模型的阶跃辨识方法，然后通过修正 RLM 算法的目标函数来改进 RLM 算法，并应用改进 RLM 算法实时递推辨识系统参数化模型的对位、稳态空间响应和动态响应． 仿真实验与实际应用结果显示，该辨识方法不仅可以一致地辨识系统参数化模型的空间响应与动态响应，而且具有比传统 RLM 算法更快的收敛速度．

In cross-directional (CD) control processes,the system model is difficult to identify due to the sparse measurement data as well as the model 's high dimensionality,strong coupling and uncertainty.In order to identify the system model rapidly and accurately,an improved approach on the basis of the recursive Levenberg-Marquardt (RLM) algorithm is proposed.Firstly,the two-dimension parameterized model and step identification method of CD control processes are described.Secondly,the objective function of RLM algorithm is modified and an improved RLM algorithm is presented,which is then applied to the real-time and recursive identification of the mapping,steady-state spatial response and dynamic response of the parameterized model of CD control processes.The results of both simulation and practical application show that the proposed identification method can not only identify the spatial and dynamic responses of CD control processes simultaneously but also converge more rapidly than the traditional RLM algorithm.