Input-Feedforward-Passivity-Based Distributed Optimization Over Jointly Connected Balanced Digraphs

In this article, a distributed optimization problem is investigated via input feedforward passivity. First, an input-feedforward-passivity-based continuous-time algorithm proposed. It shown that the error system of proposed can be decomposed into group individual passive (IFP) systems interact with each other using output feedback information. Based on IFP framework, convergence conditions suitable coupling gain are derived over weight-balanced and uniformly jointly strongly connected topologies. also IFP-based converges exponentially when topology connected. Second, novel derivative based passivation systems. While most works directed topologies require knowledge eigenvalues graph Laplacian, fully distributed, namely, it robust against randomly changing digraphs any positive without knowing global Finally, numerical examples presented to illustrate algorithms.