Event‐triggered gradient‐based distributed optimisation for multi‐agent systems with state consensus constraint
Aijuan Wang, Xiaofeng Liao, Tao Dong · IET Control Theory and Applications · 2018
This study focuses on the event‐triggered gradient‐based algorithm for a distributed optimisation problem of multi‐agent system subject to state consensus constraint over directed networks, where each agent has local access to its own strongly convex utility function. A novel gradient‐based optimisation consensus algorithm is proposed to solve the optimisation consensus problem, where the event‐triggered strategy based on sample‐data is employed. In contrast to previous optimisation consensus work, their algorithm guarantees that the equilibrium point of the multi‐agent systems is optimal solution, and it uses the constant step‐size in the optimisation term. Under the algorithm, it can be proved that there exists a certain vector established with the optimal solution is the system equilibrium point and also the consensus point. Moreover, the sufficient condition on optimisation consensus for multi‐agent systems is derived. Finally, a numerical simulation example is given to illustrate the theoretical analysis.