A Predefined-Time Multiagent Approach to Distributed Nonconvex Optimization With Nonlinearly Coupled Information

This article formulates a predefined-time multiagent approach to distributed nonconvex constrained optimization and tackles the challenges caused by the nonconvexity and nonlinearly coupled information. The divergence caused by nonconvexity is handled by utilizing a momentum-based system, and the distributed computation of nonlinearly coupled optimization information, as well as the distributed comparison of global function values, is dealt with by introducing estimation state variables. Then, combined with time-base generators, the proposed multiagent system is proven to be stable at a local optimal solution within a predefined time. The simulations of a chiller system and a numerical example are elaborated to verify and demonstrate the enhanced stability, predefined-time convergence, and distributed computation capability of the proposed multiagent approach.