In this paper, a two-dimensional recursive least squares identification method based on local polynomial modeling for batch processes is proposed. Using local polynomial modeling method to parameterize the time-varying characteristics of batch processes, a two-dimensional cost function along both time and batch directions is minimized to design the recursive least squares identification algorithm. Because this proposed method employs local polynomial modeling and utilizes two-dimensional data information to estimate model parameters, it can effectively improve the estimation accuracy and accelerate the convergence rate. Furthermore, the convergence property of the proposed method is analyzed. Finally, the simulation results show the superiority of the proposed method.

提出了一种基于局部多项式建模的批处理二维递推最小二乘辨识方法。使用局部多项式建模方法对批处理的时变特性进行参数化，将沿时间和批处理方向的二维成本函数最小化，以设计递归最小二乘辨识算法。由于该方法采用局部多项式建模，并利用二维数据信息来估计模型参数，因此可以有效地提高估计精度，加快收敛速度​​。此外，分析了该方法的收敛性。最后，仿真结果表明了该方法的优越性。