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Cooperative optimization-based distributed model predictive control for constrained nonlinear large-scale systems

Ahmad Mirzaei1, Amin Ramezani1

  • 1Control Department, Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran.

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Summary
This summary is machine-generated.

This study introduces cooperative distributed model predictive control (DMPC) for large-scale systems. The novel cooperative optimization enhances subsystem performance and guarantees system stability and convergence.

Keywords:
Cooperative optimizationDistributed model predictive controlInterconnected nonlinear large-scale systems

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Area of Science:

  • Control Systems Engineering
  • Nonlinear System Dynamics
  • Large-Scale Systems Analysis

Background:

  • Interconnected nonlinear large-scale systems present significant control challenges.
  • Distributed control strategies are crucial for managing system complexity and ensuring robustness.
  • Model Predictive Control (MPC) offers a powerful framework for handling constraints and optimizing system performance.

Purpose of the Study:

  • To develop a cooperative distributed model predictive control (DMPC) strategy for constrained interconnected nonlinear large-scale systems.
  • To introduce a novel cooperative optimization technique that enhances the global cost function of individual subsystems.
  • To ensure the feasibility, stability, and convergence of the proposed DMPC approach for complex systems.

Main Methods:

  • A cooperative distributed model predictive control (DMPC) framework is proposed.
  • Each subsystem optimizes its control actions by solving a global cost function, a combination of all subsystem cost functions.
  • Feasibility is guaranteed by appropriate selection of the sampling time.
  • Sufficient conditions for system stability and state convergence are mathematically derived.

Main Results:

  • The proposed cooperative optimization significantly improves the global cost function for each subsystem.
  • The DMPC approach guarantees feasibility when the sampling time is appropriately chosen.
  • Mathematical conditions for stability and convergence of the system states to the origin's neighborhood are established.
  • The effectiveness is validated through simulations on a nonlinear quadruple-tank system, including both minimum-phase and nonminimum-phase models.

Conclusions:

  • The cooperative DMPC approach provides an effective method for controlling constrained interconnected nonlinear large-scale systems.
  • The novel cooperative optimization enhances individual subsystem performance while ensuring overall system stability and convergence.
  • The method's applicability is demonstrated on a challenging nonlinear quadruple-tank system, highlighting its practical relevance.