Tuning parameter

In HPCB (Hierarchical Predictive Control for Batch) systems, the tuning parameters refer to the variables that can be adjusted to optimize the performance of the control algorithm.
These parameters play a crucial role in determining how the controller behaves and how well it can regulate the batch process.
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• Prediction Horizon
  This parameter determines the number of future time steps over which the controller predicts the process behavior. A longer prediction horizon allows the controller to account for future process dynamics and disturbances but can increase computational complexity.
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• Control Horizon
  The control horizon defines the length of the control action applied by the controller. It determines how far into the future the controller plans its actions. A longer control horizon enables the controller to consider longer-term process objectives but can lead to more conservative control actions.
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• Weights and Penalties
  HPCB often involves optimizing an objective function that incorporates multiple control objectives.
  Tuning the weights and penalties associated with these objectives allows the user to prioritize different goals.
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• Constraints
  HPCB often operates under various constraints, such as limits on process variables. Tuning the constraint parameters involves setting appropriate limits and penalties to ensure that the controller operates within the desired operational boundaries.
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• Model Parameters
  HPCB relies on accurate process models for prediction and control.
  Tuning the model parameters involves adjusting the model's parameters to improve its accuracy and align it with the actual process behavior.
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