Distributed Prescribed-Time Convex Optimization: Cascade Design and Time-Varying Gain Approach
<|reference_start|>Distributed Prescribed-Time Convex Optimization: Cascade Design and Time-Varying Gain Approach: In this paper, we address the distributed prescribed-time convex optimization (DPTCO) problem for a class of nonlinear multi-agent systems (MASs) under undirected connected graph. A cascade design framework is proposed such that the DPTCO implementation is divided into two parts: distributed optimal trajectory generator design and local reference trajectory tracking controller design. The DPTCO problem is then transformed into the prescribed-time stabilization problem of a cascaded system. Changing Lyapunov function method and time-varying state transformation method together with the sufficient conditions are proposed to prove the prescribed-time stabilization of the cascaded system as well as the uniform boundedness of internal signals in the closed-loop systems. The proposed framework is then utilized to solve robust DPTCO problem for a class of chain-integrator MASs with external disturbances by constructing a novel variables and exploiting the property of time-varying gains. The proposed framework is further utilized to solve the adaptive DPTCO problem for a class of strict-feedback MASs with parameter uncertainty, in which backstepping method with prescribed-time dynamic filter is adopted. The descending power state transformation is introduced to compensate the growth of increasing rate induced by the derivative of time-varying gains in recursive steps and the high-order derivative of local reference trajectory is not required. Finally, theoretical results are verified by two numerical examples.<|reference_end|>
@article{zuo2024distributed,
    title={Distributed Prescribed-Time Convex Optimization: Cascade Design and
  Time-Varying Gain Approach},
    author={Gewei Zuo, Lijun Zhu, Yujuan Wang and Zhiyong Chen},
    journal={arXiv preprint arXiv:2407.11413},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.11413},
    primaryClass={math.OC cs.SY eess.SY}
}