中文摘要：

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rain-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。

英文摘要：

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 rain-max problem, in which the data uncertainties have been taken into account. For lower computational burden, the rain-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.