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Distributed model predictive control for consensus of nonlinear systems via parametric sensitivity.

Tianyu Yu1, Fei Zhao1, Zuhua Xu1

  • 1State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China.

ISA Transactions
|November 19, 2024
PubMed
Summary
This summary is machine-generated.

This study presents a distributed model predictive control (DMPC) scheme to solve nonlinear consensus problems. The new method enhances optimization efficiency and ensures optimality and robustness in multi-agent systems.

Keywords:
Consensus problemDistributed model predictive controlNonlinear programmingParametric sensitivity

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Area of Science:

  • Control Systems Engineering
  • Robotics
  • Optimization Theory

Background:

  • Nonlinear consensus problems are critical in multi-agent systems.
  • Existing distributed control methods face challenges with efficiency and scalability.

Purpose of the Study:

  • To develop an efficient and robust distributed model predictive control (DMPC) scheme for nonlinear consensus problems.
  • To enhance optimization efficiency through a two-stage input computation strategy.

Main Methods:

  • A two-stage input computation strategy involving a background and an online stage.
  • Development of three variants of sensitivity equations based on communication load.
  • Distributed optimization for calculating future inputs and correcting background inputs.

Main Results:

  • The proposed DMPC scheme demonstrates enhanced optimization efficiency.
  • Rigorous derivation of the algorithm's optimality and robustness.
  • Successful demonstration in a multi-vehicle system with varying communication topologies.

Conclusions:

  • The developed DMPC scheme effectively addresses nonlinear consensus problems.
  • The parametric sensitivity approach offers a superior solution for distributed control.
  • The method is validated for its performance in complex multi-agent scenarios.