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Interactive and Visualized Online Experimentation System for Engineering Education and Research

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

Distributed model predictive control for constrained nonlinear systems with decoupled local dynamics.

Meng Zhao¹, Baocang Ding¹

¹College of Automation, Chongqing University, Chongqing 400044, China.

ISA Transactions

|December 3, 2014

Summary

This summary is machine-generated.

This study introduces distributed model predictive control (MPC) for large-scale nonlinear systems. It ensures stability by decomposing constraints and optimizing local objectives, enabling efficient control with reduced communication.

Keywords:

Compatibility condition Distributed control Exponentially stability Model predictive control (MPC)

Related Experiment Videos

Last Updated: Apr 20, 2026

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

Area of Science:

Control Engineering
Systems Science
Applied Mathematics

Background:

Large-scale nonlinear systems present significant control challenges.
Centralized model predictive control (MPC) can be computationally intensive for such systems.
Distributed control strategies are needed to manage complexity and communication overhead.

Purpose of the Study:

To develop a distributed model predictive control (MPC) strategy for nonlinear large-scale systems.
To ensure closed-loop stability in a distributed MPC framework.
To reduce communication requirements between subsystems.

Main Methods:

Decomposition of the overall cost function into local optimization problems for each subsystem.
Fixed neighbor identification based on coupled states in the cost function.
Decomposition of centralized compatibility constraints into local controllers.
Application of a quasi-infinite horizon MPC algorithm for each local controller.
Analysis of communication protocols involving current states and optimized inputs.

Main Results:

Successfully disassembled the global objective function into local optimization tasks.
Ensured closed-loop stability by decomposing compatibility constraints.
Demonstrated low communication overhead, transferring only essential state and input information.
Proved exponential stability for the global closed-loop system.

Conclusions:

The proposed distributed MPC approach is effective for nonlinear large-scale systems.
The method guarantees system stability while minimizing communication.
This framework offers a scalable and efficient solution for complex control problems.