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Predefined-time observer-based distributed consensus optimization control of nonlinear multi-agent systems
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DOI：10.1007/s11071-025-11735-7.png)
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Abstract
En 中文
This paper tackles the challenge of predefined-time distributed optimization for nonlinear multi-agent systems with unmeasurable states. A novel predefined-time observer is designed using time-varying functions, thereby eliminating singularities inherent in conventional approaches. Unlike existing predefined-time results that address stability solely within finite intervals, a comprehensive full-period predefined-time observer-based control framework is developed. Furthermore, a generalized convexity condition is established for certain local functions, relaxing the traditional strong convexity requirement. The original optimization problem is first transformed into an unconstrained formulation using a quadratic penalty function. Subsequently, a distributed predefined-time optimization control scheme is synthesized via a back-stepping methodology. The proposed controllers ensure output consensus and achieve minimization of the global objective function within a predefined-time scheme. Rigorous stability analysis and numerical simulations demonstrate the efficacy of the developed methodology.
Keywords:
Multi-agent systems(MASs)
Predefined-time
Immeasurable states
Distributed optimization
Penalty function
Journal
IF:
6
Papers:
1.4W
Citations:
4.1W