一种基于模型参考自适应辨识的半自动模型预测控制方法  

The Semi-automatic Model Predictive Control Method Based on Model Reference Adaptive Identification Algorithm

作　　者：庞强[1,2] 夏琼[1] 邹涛[2] 丛秋梅[2] 

机构地区：[1]东北大学流程工业综合自动化国家重点实验室,辽宁沈阳110819 [2]中国科学院沈阳自动化研究所信息服务与智能控制技术研究室,辽宁沈阳110016

出　　处：《信息与控制》2014年第6期681-689,696,共10页Information and Control

基　　金：国家自然科学基金资助项目(61374112);国家863计划资助项目(2014AA041802);中科院重点部署项目(KGZD-EW-302);博士后基金资助项目(2013M530953);流程工业综合自动化国家重点实验室基础科研业务费资助项目(2013ZCX02)

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(model predictive control,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(finite step response)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.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.

关 键 词：半自动模型预测控制 多变量辨识 模型失配 自适应辨识 频率响应 持续激励 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]