Abstract:

A robust model predictive control（RAMPC）technique with an adaptive disturbance model is developed.The dynamics of unmeasured disturbances are modeled by ARMA model.In order to get accurate identification and faster convergence，a multi-iteration pseudo-linear regression（MIPLR）method is proposed.In addition，the optimization in MPC is formulated as a min-max problem，in which the data uncertainties have been taken into account.For lower computational burden，the min-max problem is reduced to a nonlinear min one，and is solved by multi-step linearization method.Numerical simulations have been demonstrated the effectiveness of the proposed methods.

Key words:

预测控制, 扰动模型, ARMA模型, 多次迭代, min-max问题, 多步线性化

摘要：

针对实际生产中扰动的时变性，提出了一种扰动自适应的鲁棒预测控制（RAMPC）算法以提高扰动抑制性能。采用时间序列（ARMA）模型在线辨识系统的不可测扰动，通过基于多次迭代思想的递推辨识算法（multi-iteration pseudo-linear regression，MIPLR）来保证在线辨识的质量和收敛速度。考虑到数据与辨识模型的不确定性，改用min-max形式描述MPC算法的控制作用优化命题，并将在线辨识过程中的误差数据引入min-max命题，使在线辨识与控制作用鲁棒优化求解紧密结合起来，提高算法鲁棒性。进一步将此min-max问题转换为一个等效的非线性min问题，并采用多步线性化方法实现快速求解，解决了传统min-max方法在线计算负荷高的问题。仿真结果表明了该算法的有效性。

关键词:

预测控制, 扰动模型, ARMA模型, 多次迭代, min-max问题, 多步线性化