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Distributed adaptive tracking control for synchronization of unknown networked Lagrangian systems.

Gang Chen1, Frank L Lewis

  • 1College of Automation, Chongqing University, Chongqing, China. chengang@cqu.edu.cn

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|December 24, 2010
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Summary

This study presents a distributed adaptive control protocol for multiple Lagrangian vehicle systems to achieve cooperative tracking of a target. The method uses neural networks to handle unknown dynamics, ensuring synchronization even with approximation errors.

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Area of Science:

  • Robotics and Control Systems
  • Networked Systems Engineering
  • Artificial Intelligence in Control

Background:

  • Cooperative tracking control is crucial for coordinated multi-agent systems.
  • Lagrangian vehicle systems present complex dynamics that are challenging to model accurately.
  • Distributed control architectures are essential for scalability and robustness in networked systems.

Purpose of the Study:

  • To develop a distributed adaptive control protocol for cooperative tracking in heterogeneous Lagrangian vehicle systems.
  • To ensure synchronization of all networked vehicles to a target system with unknown dynamics.
  • To address challenges posed by unknown system dynamics and non-constant neural network approximation errors.

Main Methods:

  • A distributed adaptive control protocol is designed using a decentralized proportional-plus-derivative term.
  • Neural networks (NNs) are employed at each node to approximate unknown system dynamics.
  • A robust term is incorporated to mitigate external disturbances and NN approximation errors.

Main Results:

  • The proposed protocol guarantees that all networked Lagrangian systems synchronize with the target system.
  • The control strategy effectively handles systems with different dynamics and unknown target dynamics.
  • Simulation examples validate the effectiveness of the developed algorithms in achieving cooperative tracking.

Conclusions:

  • The developed distributed adaptive protocol enables robust cooperative tracking for heterogeneous Lagrangian vehicle systems.
  • The use of neural networks and a robust term enhances the system's ability to handle uncertainties.
  • This research contributes to advancing control strategies for complex networked robotic systems.