文章提出一种约束系统的无偏模型预测控制算法，此方法利用人工干扰来代替建模误差。该算法基于新的扩展系统结构和双控制律实现，其中，双控制律由线性稳定控制和动态预测控制组成。与以往方法相比，该算法不需要计算稳态输入和状态目标，也不需要使用增广状态观测器，具有扩展系统模型可控和模型失配处理等特点。通过考虑状态约束和输入约束，ＭＰＣ控制器的设计既能达到无偏控制的目的，又能在存在干扰和模型失配的情况下保证约束满足。针对连续非线性夹套搅拌槽式反应器（ＣＳＴＲ）模型进行了仿真，说明了该算法的有效性。

This paper proposes an offset-free model predictive control ( MPC) algorithm for constrained nonlinear systems, by utilizing an artificial disturbance to replace the modeling error. The algorithm is based on a new expanded system structure and the dual part control law consisting of the linear stabilizing control and the dynamic predictive control. Compared with previous techniques, there is no need for calculating the steady state input, state targets and using an observer for augmented states. Other characteristics of this algorithm include controllable expanded system model and handling model mismatch. By consideration of the state and input constraints, design of the MPC controller not only achieves the aim of offset free control but also guarantees the constraint satisfaction in the presence of disturbance and model mismatch. The particular characteristics of the pro posed algorithm are illustrated via a simulation using a continuous nonlinear jacketed stirred tank reactor( CSTR) model.