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Related Experiment Videos

Synchronization of decentralized multiple-model systems by market-based optimization.

Rainer Palm1

  • 1Siemens AG, Munich D-81919, Germany.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|September 17, 2004
PubMed
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Market-based optimization synchronizes decentralized systems by treating them as economic agents. This novel approach ensures stable system behavior even with unstable local components.

Area of Science:

  • Control Systems Engineering
  • Artificial Intelligence
  • Optimization Theory

Background:

  • Decentralized systems require robust optimization methods for resource allocation.
  • Market-based algorithms offer a multi-agent framework for competition and cooperation.
  • Synchronization of multiple-model systems is a complex control challenge.

Purpose of the Study:

  • To apply market-based optimization to synchronize a set of local multiple-model systems.
  • To extend the market-based approach to subsystems modeled as Takagi-Sugeno (TS) fuzzy systems.
  • To achieve similar dynamical behavior across subsystems with differing parameters.

Main Methods:

  • Interpreting market-based algorithms as multi-agent scenarios with competing/cooperating agents.

Related Experiment Videos

  • Applying distributed resource allocation principles from economic systems.
  • Utilizing Takagi-Sugeno (TS) fuzzy systems to represent local subsystems.
  • Developing a method to find subsystem compositions for synchronized dynamics.

    • Main Results:

    • Successfully applied market-based optimization to synchronize local multiple-model systems.
    • Demonstrated the extension to Takagi-Sugeno (TS) fuzzy system representations.
    • Showcased synchronization of systems with potentially unstable local components through stable weighted combinations.
    • Achieved similar dynamical behavior among subsystems with varied parameters.

    Conclusions:

    • Market-based optimization is an effective strategy for synchronizing decentralized systems.
    • The approach is versatile, accommodating Takagi-Sugeno (TS) fuzzy models.
    • Stable synchronization is achievable even with inherently unstable subsystems.
    • This method provides a novel way to manage complex, distributed systems.