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Summary

This study introduces a distributed model predictive control algorithm using dual decomposition for complex industrial processes. The new method significantly reduces computational load, enhancing online optimization capabilities for wider industry adoption.

Keywords:
double-layerdual decompositionmodel predictive control

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Area of Science:

  • Chemical Engineering
  • Control Systems
  • Optimization Theory

Background:

  • Centralized control in large-scale industrial processes faces computational challenges with increasing complexity.
  • Existing double-layer model predictive control (MPC) algorithms struggle with online optimization due to high computational demands.
  • Widespread industrial implementation of MPC is hindered by its computational complexity.

Purpose of the Study:

  • To propose a distributed double-layer MPC algorithm based on dual decomposition for multivariate constrained systems.
  • To reduce the computational complexity of process control in large-scale industrial applications.
  • To enhance the online optimization capability of MPC for industrial settings.

Main Methods:

  • Development of two improved dual decomposition MPC methods: one based on subsystem quadratic programming and another on constraint zones.
  • Mathematical proof of convergence for the constraint zone-based method to control variable constraint boundaries.
  • Proposal of a distributed double-layer MPC algorithm utilizing dual decomposition based on constraint zones with a modified objective function.

Main Results:

  • The dual decomposition method based on constraint zones demonstrated superior online optimization ability compared to the quadratic programming approach.
  • The proposed distributed algorithm effectively reduces computational complexity while achieving control objectives.
  • The modified objective function allows simultaneous tracking of steady-state optimization values for controlled and manipulated variables.

Conclusions:

  • The developed distributed double-layer MPC algorithm based on dual decomposition offers a computationally efficient solution for complex industrial processes.
  • The constraint zone-based dual decomposition method is crucial for improving online optimization performance.
  • This research holds significant potential for advancing the industrial application of double-layer MPC.