引用本文:宋红超,王昕,王振雷.基于切换的非线性多模型二阶段广义预测控制[J].控制理论与应用,2024,41(11):2147~2156.[点击复制]
SONG Hong-chao,WANG Xin,WANG Zhen-lei.Nonlinear multi-model second level generalized predictive control based on switching[J].Control Theory and Technology,2024,41(11):2147~2156.[点击复制]
基于切换的非线性多模型二阶段广义预测控制
Nonlinear multi-model second level generalized predictive control based on switching
摘要点击 153  全文点击 33  投稿时间：2022-07-18  修订日期：2024-07-20
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DOI编号  10.7641/CTA.2023.20640
  2024,41(11):2147-2156
中文关键词  非线性系统  多模型  二阶段自适应  广义预测控制
英文关键词  nonlinear system  multiple models  second level adaptive  generalized predictive control
基金项目  国家重点研发计划课题项目(2022YFB3304701), 国家自然科学基金重大课题项目(62293504), 国家自然科学基金面上项目(62173147), 上海 市科委高新技术领域项目(22DZ1101500), 中央高校基本科研业务费专项资金资助.新技术领域项目(22DZ1101500)及中央高校基本科研业务费专项资金
作者单位E-mail
宋红超 华东理工大学 能源化工过程智能制造教育部重点实验室 1262593158@qq.com 
王昕 上海交通大学 电工与电子技术中心  
王振雷* 华东理工大学 能源化工过程智能制造教育部重点实验室 wangzhen_l@ecust.edu.cn 
中文摘要
      针对一类参数跳变引起零动态不稳定的非线性离散时间系统, 本文提出一种基于误差切换策略的非线性多模型二阶段广义预测控制器设计方法. 首先, 将未知参数的空间划分为多个子集, 并在每个子集中建立多个非线性预测模型, 并对未知参数进行辨识; 进而, 利用带约束的二阶段自适应方法获得每个子集虚拟模型的参数估计值,并以此计算对应的广义预测控制作用, 从而更好的处理零动态不稳定问题: 为了有效改善参数跳变对系统的影响,利用模型输出误差性能指标选取每一时刻最优的广义预测控制器控制非线性系统, 并进行稳定性分析. 最后, 通过对比现存方法的仿真结果表明本文所提出的广义预测控制器有良好的跟踪性能和抗干扰能力.
英文摘要
      A nonlinear multiple models second-level generalized predictive controller design method based on an error switching strategy is proposed for a class of nonlinear discrete-time systems with zero dynamic instability caused by parameter jumps. Initially, the space of unknown parameters is divided into multiple subsets, and multiple nonlinear prediction models are established in each subset, with the identification of unknown parameters. Subsequently, a second-level adaptive method with constraints is utilized to obtain the parameter estimates of each subset of the virtual model, and the corresponding generalized predictive control effect is calculated to address the zero dynamic instability issue more effectively. To mitigate the impact of parameter jumps on the system, the model output error performance index is employed to select the optimal generalized predictive controller for controlling the nonlinear system at each moment, followed by stability analysis. Ultimately, simulation results, when compared with existing methods, demonstrate that the proposed generalized predictive controller exhibits excellent tracking performance and anti-interference capabilities.