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

庞强, 夏琼, 邹涛, 丛秋梅

庞强, 夏琼, 邹涛, 丛秋梅. 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[J]. 信息与控制, 2014, 43(6): 681-689,696. DOI: 10.13976/j.cnki.xk.2014.0681
引用本文: 庞强, 夏琼, 邹涛, 丛秋梅. 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[J]. 信息与控制, 2014, 43(6): 681-689,696. DOI: 10.13976/j.cnki.xk.2014.0681
PANG Qiang, XIA Qiong, ZOU Tao, CONG Qiumei. The Semi-automatic Model Predictive Control Method Based on Model Reference Adaptive Identification Algorithm[J]. INFORMATION AND CONTROL, 2014, 43(6): 681-689,696. DOI: 10.13976/j.cnki.xk.2014.0681
Citation: PANG Qiang, XIA Qiong, ZOU Tao, CONG Qiumei. The Semi-automatic Model Predictive Control Method Based on Model Reference Adaptive Identification Algorithm[J]. INFORMATION AND CONTROL, 2014, 43(6): 681-689,696. DOI: 10.13976/j.cnki.xk.2014.0681
庞强, 夏琼, 邹涛, 丛秋梅. 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[J]. 信息与控制, 2014, 43(6): 681-689,696. CSTR: 32166.14.xk.2014.0681
引用本文: 庞强, 夏琼, 邹涛, 丛秋梅. 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[J]. 信息与控制, 2014, 43(6): 681-689,696. CSTR: 32166.14.xk.2014.0681
PANG Qiang, XIA Qiong, ZOU Tao, CONG Qiumei. The Semi-automatic Model Predictive Control Method Based on Model Reference Adaptive Identification Algorithm[J]. INFORMATION AND CONTROL, 2014, 43(6): 681-689,696. CSTR: 32166.14.xk.2014.0681
Citation: PANG Qiang, XIA Qiong, ZOU Tao, CONG Qiumei. The Semi-automatic Model Predictive Control Method Based on Model Reference Adaptive Identification Algorithm[J]. INFORMATION AND CONTROL, 2014, 43(6): 681-689,696. CSTR: 32166.14.xk.2014.0681

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

基金项目: 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)
详细信息
    作者简介:

    庞强(1981-),男,博士生,助理研究员.研究领域为先进过程控制与优化,能源管理与能效分析,多变量模型辨识与预测控制.

    通讯作者:

    庞强, pangqiang@sia.cn

  • 中图分类号: TP273

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

  • 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.
    Abstract: 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 adaptive 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 analysis 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.
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出版历程
  • 收稿日期:  2014-01-08
  • 发布日期:  2014-12-19

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