中文摘要：

多变量预测控制在应用中经常会遇到模型失配的问题，最终导致控制器不能满足控制要求．本文提出了一种模型预测控制（modelpredictivecontrol，MPC）架构，通过被控对象和预测模型的频率响应误差判断模型是否失配；当模型失配时，首先对被控对象叠加持续激励信号；然后，通过改进的模型自适应辨识方法辨识对象的传递函数模型；最后，经过拉氏逆变换，将传递函数模型转化为FSR（finitestepresponse）模型，重新恢复多变量预测控制．该方法不需要进行离线辨识试验，实现了模型的多变量辨识；辨识的传递函数模型的动态特性更加清晰，便于分析和修改；经过拉氏逆变换得到的FSR模型更加平滑，能够消除因模型误差引起的静差．经过仿真实验，证明了该方法的有效性。

英文摘要：

The model mismatch problem appears in the application of multivariable predictive control algorithms that may lead a controller not to meet control requirements. We present a model predictive control（MPC） framework that uses the frequency response error between the controlled plant and the predictive model as the criterion to determine whether model mismatch exists. If model mismatch occurs, a persistent excitation signal is added to the controlled plant first, and then the transfer function model of the plant is identified by an improved model a- daptive identification algorithm. Finally, the transfer function model is transformed into a finite step response （FSR） model via inverse Laplace transform, and multivariable predictive control is reactivated. Using this new method, an offline identification test becomes unnecessary, and multivariable identification can be achieved. The dynamic characteristics of the identified transfer function model are even clearer and more convenient for a- nalysis and modification. After inverse Laplace transform, the FSR model runs more smoothly and can eliminate the offset caused by model errors. Simulation results show the effectiveness of the proposed method.