ABSTRACT This paper studies the distributed cooperative optimization control problem for high‐order multi‐agent systems. The local cost function is strongly convex with bounded derivative, in which the strong convex coefficients and their bounds are not necessarily known. An event‐based, fully distributed optimization algorithm that eliminates the requirement for continuous communications is designed to estimate the optimal solution to an optimization problem. Based on the estimates, a set of high‐order filters is constructed to generate alternative signals with the necessary differentiability as the approximation of the optimization problem solution. Subsequently, a backstepping procedure is reconstructed. Different from the existing related results, the proposed strategy can reduce the communication cost effectively and be implemented in a fully distributed manner. The effectiveness of the design is illustrated by two examples.
Liu et al. (Fri,) studied this question.