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Improving the feed-forward compensator in predictive control for setpoint tracking.

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This study enhances Model Predictive Control (MPC) by optimizing feed-forward compensators for better performance. Modifications improve systematic design and computational efficiency in real-time control systems.

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Area of Science:

  • Control Engineering
  • Automation Systems
  • Process Optimization

Background:

  • Model Predictive Control (MPC) feed-forward compensators can be suboptimal.
  • Existing MPC designs may not fully leverage constraint handling for efficiency.

Purpose of the Study:

  • To provide insights into suboptimal MPC feed-forward compensators.
  • To propose simple modifications for a more systematic and optimal feed-forward design.
  • To explore benefits of constraint handling for computational efficiency.

Main Methods:

  • Analysis of MPC feed-forward compensator design.
  • Development of modification strategies for feed-forward components.
  • Investigation of the impact of loop tuning on optimal procedures.
  • Evaluation of constraint handling for computational efficiency.
  • Laboratory testing on a programmable logic controller (PLC).

Main Results:

  • Demonstrated that optimal feed-forward design is dependent on underlying loop tuning.
  • Identified underutilized benefits of constraint handling procedures.
  • Showcased improved computational efficiency in online controller implementation.
  • Validated the proposed solution through a PLC laboratory test.

Conclusions:

  • The proposed modifications offer a more systematic and optimal approach to MPC feed-forward design.
  • Enhanced constraint handling significantly improves computational efficiency for real-time MPC.
  • The practical implementation on a PLC confirms the effectiveness of the developed solution.