ABSTRACT
This paper proposes novel distributed optimization algorithms, which exhibit predefined-time convergence for two classes of distributed time-varying optimization problems—consensus optimization and resource allocation—over the directed communication network. First, based on the time-varying scaling function mechanism, a distributed average estimator is ingeniously designed to estimate the global states of the optimized multi-agent systems (MASs). Utilizing the estimator’s outputs, a predefined-time convergence algorithm is constructed, which makes the decision variable of the MASs converge to the optimal solution of the consensus optimization problem after T1→T2→T3$$ {T}_1to {T}_2to {T}_3 $$. Then, for the quadratic objective functions, a distributed optimization algorithm based on Lagrangian multipliers is developed to solve the time-varying resource allocation problem within the predefined time T2$$ {T}_2 $$. Finally, two simulation cases verify the effectiveness of the proposed algorithms.
International Journal of Robust and Nonlinear Control, EarlyView. Read More
