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Distributed control for a class of nonlinear systems based on distributed high-gain observer.

Haotian Xu1, Shuai Liu1, Shangwei Zhao2

  • 1School of Control Science and Engineering, Shandong University, Jinan 250061, China.

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|March 20, 2023
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This study introduces distributed high-gain observers for nonlinear systems with distributed measurements, enabling model uncertainty handling and overcoming separation principle limitations for robust control design.

Keywords:
Distributed controlDistributed state estimateHigh-gain observerNonlinear system

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Area of Science:

  • Control Systems Engineering
  • Nonlinear Dynamics
  • Distributed Systems

Background:

  • Designing distributed control laws for nonlinear systems with spatially separated measurements presents challenges in state reconstruction.
  • Existing research on distributed observers for nonlinear systems and their application in control is limited.

Purpose of the Study:

  • To develop distributed high-gain observers for a class of nonlinear systems with distributed measurement outputs.
  • To design a distributed observer-based output feedback control law for these systems.
  • To address model uncertainty and the inapplicability of the separation principle in distributed nonlinear control.

Main Methods:

  • Development of distributed high-gain observers tailored for nonlinear systems.
  • Design of an output feedback control law utilizing state estimates from the observers.
  • Establishment of sufficient conditions to guarantee convergence of observer error dynamics and closed-loop system states.

Main Results:

  • Successfully designed distributed high-gain observers capable of handling model uncertainty.
  • Developed a novel distributed observer-based output feedback control strategy.
  • Proved conditions ensuring the convergence of observer errors and closed-loop system states to a small invariant set.

Conclusions:

  • The proposed distributed high-gain observers and control strategy effectively address challenges in nonlinear systems with distributed measurements.
  • The method demonstrates robustness to model uncertainty and overcomes limitations of the separation principle.
  • Simulation results validate the practical effectiveness of the developed approach for distributed control design.