一种间歇过程的综合预测迭代学习控制方法

为了提高迭代学习控制方法在间歇过程轨迹跟踪问题中的收敛速度,本文将批次间的比例型迭代学习控制与批次内的模型预测控制相结合,提出了一种综合应用方法.首先根据间歇过程的线性模型,预测出比例型迭代学习控制的系统输出,然后在批次内采用模型预测控制,通过极小化一个二次型目标函数来获得控制增量.该方法可使系统输出跟踪期望轨迹的速度比比例型迭代学习控制方法更快些.最后通过仿真实例验证了该方法的有效性.

An integrated predictive iterative learning control for batch process

In order to improve the convergence speed of iterative learning control (ILC), an integrated scheme for tracking problem of batch process is proposed by combining batch-to-batch P-type ILC and within-batch model predictive control (MPC). Based on a predefined batch-wise linear model of the process, the output of traditional P-type ILC can be predicted, and then MPC is induced to minimize a quadratic objective function within the current batch. The input is updated within the batch so that the output may approach the reference trajectory faster. An illustrative example is presented to demonstrate the performance of the proposed scheme.