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Adaptive Critic Learning-Based Optimal Bipartite Consensus for Multiagent Systems With Prescribed Performance.

Lei Yan, Junhe Liu, Guanyu Lai

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    Summary
    This summary is machine-generated.

    This study introduces an adaptive critic learning scheme for optimal bipartite consensus, ensuring user-defined performance. The new method simplifies control structures and guarantees performance bounds, validated by simulations.

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    Area of Science:

    • Control Systems Engineering
    • Distributed Systems
    • Machine Learning

    Background:

    • Achieving user-predefined performance in distributed bipartite optimal consensus is challenging with existing complex identifier-actor-critic frameworks.
    • Prescribed performance guarantees are often unmet in current consensus schemes.

    Purpose of the Study:

    • To develop a simplified adaptive critic learning (ACL)-based optimal bipartite consensus scheme.
    • To ensure user-predefined settling time and steady accuracy independent of initial conditions.
    • To remove complex identifier and actor networks from existing control structures.

    Main Methods:

    • Integration of a novel error scaling function into a cost function.
    • Combination of the backstepping framework with ACL and integral reinforcement learning (IRL).
    • Development of a critic-only controller structure with adaptive laws derived via gradient descent and experience replay.

    Main Results:

    • A compute-saving learning mechanism for optimal bipartite consensus is achieved.
    • Error variables of the closed-loop system are proven to be uniformly ultimately bounded (UUB).
    • Bipartite consensus evolution is confined within user-prescribed boundaries for all bounded initial conditions.

    Conclusions:

    • The proposed ACL-based scheme effectively achieves optimal bipartite consensus with guaranteed performance.
    • The critic-only controller simplifies the system while maintaining performance and stability.
    • Simulation results confirm the approach's efficacy and robustness.