Scholarlink

In this Chapter, a robust 2D ILCIterative Learning Control (ILC) method based on the state feedbackState feedback structure is presented for linear or nonlinear batch processesBatch process that can be practically divided into a series of piecewise affine operating regionsPiecewise affine operating region. By using a 2D system description of the process for batch control design, both time-varyingTime-varyingprocess uncertaintiesProcess uncertainties and nonrepetitive load disturbanceLoad disturbance are taken into account, together with the process input and state constraintsState constraint for implementation. By introducing the desired performance objective or robust control objectiveControl objective in combination with the 2D Lyapunov–Krasovskii functions that can guarantee monotonic state energy (or output error) decrease along both the time and batch directions, LMILinear Matrix Inequality (LMI) conditions are correspondingly established for the ILCIterative Learning Control (ILC)controller designController designand performance optimizationPerformance optimization. In these LMI conditions, there are adjustable convergenceConvergence indices for the time and batch directions, and an adjustable closed-loop robust control performance level. The effectiveness of the proposed ILCIterative Learning Control (ILC) method is demonstrated through the application to a highly nonlinear continuous stirred tank reactorContinuous Stirred Tank Reactor (CSTR) (CSTR) subject to batch-to-batch uncertainties and load disturbanceLoad disturbance.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Closed-Loop ILC Scheme with State Feedback

  • 章节

  • 发表日期：

    2026年

查看全文
  • Tao Liu,
  • Shoulin Hao,
  • Youqing Wang,
  • Dewei Li

摘要

In this Chapter, a robust 2D ILCIterative Learning Control (ILC) method based on the state feedbackState feedback structure is presented for linear or nonlinear batch processesBatch process that can be practically divided into a series of piecewise affine operating regionsPiecewise affine operating region. By using a 2D system description of the process for batch control design, both time-varyingTime-varyingprocess uncertaintiesProcess uncertainties and nonrepetitive load disturbanceLoad disturbance are taken into account, together with the process input and state constraintsState constraint for implementation. By introducing the desired performance objective or robust control objectiveControl objective in combination with the 2D Lyapunov–Krasovskii functions that can guarantee monotonic state energy (or output error) decrease along both the time and batch directions, LMILinear Matrix Inequality (LMI) conditions are correspondingly established for the ILCIterative Learning Control (ILC)controller designController designand performance optimizationPerformance optimization. In these LMI conditions, there are adjustable convergenceConvergence indices for the time and batch directions, and an adjustable closed-loop robust control performance level. The effectiveness of the proposed ILCIterative Learning Control (ILC) method is demonstrated through the application to a highly nonlinear continuous stirred tank reactorContinuous Stirred Tank Reactor (CSTR) (CSTR) subject to batch-to-batch uncertainties and load disturbanceLoad disturbance.