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Static output feedback based distributed robust model predictive control for parallel systems in process networks.

Shuzhan Zhang1, Dongya Zhao1, Sarah K Spurgeon2

  • 1Department of Chemical Equipment and Control Engineering, College of Chemical Engineering, China University of Petroleum (East China), Qingdao, China.

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Summary
This summary is machine-generated.

A new distributed robust model predictive control strategy addresses unmeasured states in parallel systems. This method uses static output feedback and linear matrix inequalities, proving input-to-state stability for process networks.

Keywords:
Distributed model predictive controlParallel systemStatic output feedback control

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Area of Science:

  • Control Systems Engineering
  • Chemical Process Networks
  • Robust Control Theory

Background:

  • Parallel systems are common in process networks but present challenges due to coupling and constraints.
  • Existing control strategies often struggle with unmeasured states in such architectures.
  • Robust model predictive control (MPC) is a powerful framework for complex systems.

Purpose of the Study:

  • To develop a distributed robust model predictive control strategy for parallel systems with unmeasured states.
  • To address the inherent coupling and constraints within parallel system architectures.
  • To ensure stability and validate the proposed control approach.

Main Methods:

  • Utilized a static output feedback framework for controller design.
  • Formulated the control problem as a non-convex optimization problem.
  • Solved the non-convex problem using linear matrix inequalities (LMIs).
  • Demonstrated input-to-state stability (ISS) of the closed-loop system.

Main Results:

  • Successfully designed a distributed robust MPC controller for parallel systems.
  • The controller effectively handles unmeasured states and system constraints.
  • Linear matrix inequalities provided a feasible solution to the non-convex control problem.
  • Input-to-state stability was rigorously proven for the controlled system.

Conclusions:

  • The proposed distributed robust MPC strategy is effective for parallel systems with unmeasured states.
  • The LMI-based approach provides a systematic way to design robust controllers for these systems.
  • Validation through simulation and experimentation confirms the practical applicability of the method.